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Purpose: Muscle, tendon, and muscle–tendon unit (MTU) stiffness as well as passive peak torque (PPT) or delayed stretching pain sensation are typical explanatory approaches for stretching adaptations. However, in literature, differences in the study inclusion, as well as applying meta-analytical models without accounting for intrastudy dependency of multiple and heteroscedasticity of data bias the current evidence. Furthermore, most of the recent analyses neglected to investigate PPT adaptations and further moderators. Methods: The presented review used the recommended meta-analytical calculation method to investigate the effects of stretching on stiffness as well as on passive torque parameters using subgroup analyses for stretching types, stretching duration, and supervision. Results: Chronic stretching reduced muscle stiffness (− 0.38, p = 0.01) overall, and also for the supervised (− 0.49, p = 0.004) and long static stretching interventions (− 0.61, p < 0.001), while the unsupervised and short duration subgroups did not reach the level of significance (p = 0.21, 0.29). No effects were observed for tendon stiffness or for subgroups (e.g., long-stretching durations). Chronic PPT (0.55, p = 0.005) in end ROM increased. Only long-stretching durations sufficiently decreased muscle stiffness acutely. No effects could be observed for acute PPT. Conclusion: While partially in accordance with previous literature, the results underline the relevance of long-stretching durations when inducing changes in passive properties. Only four acute PPT in end ROM studies were eligible, while a large number were excluded as they provided mathematical models and/or lacked control conditions, calling for further randomized controlled trials on acute PPT effects. [ABSTRACT FROM AUTHOR]

When investigating unobservable, complex traits, data collection and aggregation processes can introduce distinctive features to the data such as boundedness, measurement error, clustering, outliers, and heteroscedasticity. Failure to collectively address these features can result in statistical challenges that prevent the investigation of hypotheses regarding these traits. This study aimed to demonstrate the efficacy of the Bayesian beta-proportion generalized linear latent and mixed model (beta-proportion GLLAMM) (Rabe-Hesketh et al., Psychometrika, 69(2), 167–90, 2004a, Journal of Econometrics, 128(2), 301–23, 2004c, 2004b; Skrondal and Rabe-Hesketh 2004) in handling data features when exploring research hypotheses concerning speech intelligibility. To achieve this objective, the study reexamined data from transcriptions of spontaneous speech samples initially collected by Boonen et al. (Journal of Child Language, 50(1), 78–103, 2023). The data were aggregated into entropy scores. The research compared the prediction accuracy of the beta-proportion GLLAMM with the normal linear mixed model (LMM) (Holmes et al., 2019) and investigated its capacity to estimate a latent intelligibility from entropy scores. The study also illustrated how hypotheses concerning the impact of speaker-related factors on intelligibility can be explored with the proposed model. The beta-proportion GLLAMM was not free of challenges; its implementation required formulating assumptions about the data-generating process and knowledge of probabilistic programming languages, both central to Bayesian methods. Nevertheless, results indicated the superiority of the model in predicting empirical phenomena over the normal LMM, and its ability to quantify a latent potential intelligibility. Additionally, the proposed model facilitated the exploration of hypotheses concerning speaker-related factors and intelligibility. Ultimately, this research has implications for researchers and data analysts interested in quantitatively measuring intricate, unobservable constructs while accurately predicting the empirical phenomena. [ABSTRACT FROM AUTHOR]

We present a Bayesian approach for the analysis of rating data when a scaling component is taken into account, thus incorporating a specific form of heteroskedasticity. Model-based probability effect measures for comparing distributions of several groups, adjusted for explanatory variables affecting both location and scale components, are proposed. Markov Chain Monte Carlo techniques are implemented to obtain parameter estimates of the fitted model and the associated effect measures. An analysis on students' evaluation of a university curriculum counselling service is carried out to assess the performance of the method and demonstrate its valuable support for the decision-making process. [ABSTRACT FROM AUTHOR]

Multilevel modeling (MLM) is commonly used in psychological research to model clustered data. However, data in applied research usually violate one of the essential assumptions of MLM—homogeneity of variance. While the fixed-effect estimates produced by the maximum likelihood method remain unbiased, the standard errors for the fixed effects are misestimated, resulting in inaccurate inferences and inflated or deflated type I error rates. To correct the bias in fixed effects standard errors and provide valid inferences, small-sample corrections such as the Kenward-Roger (KR) adjustment and the adjusted cluster-robust standard errors (CR-SEs) with the Satterthwaite approximation for t tests have been used. The current study compares KR with random slope (RS) models and the adjusted CR-SEs with ordinary least squares (OLS), random intercept (RI) and RS models to analyze small, heteroscedastic, clustered data using a Monte Carlo simulation. Results show the KR procedure with RS models has large biases and inflated type I error rates for between-cluster effects in the presence of level 2 heteroscedasticity. In contrast, the adjusted CR-SEs generally yield results with acceptable biases and maintain type I error rates close to the nominal level for all examined models. Thus, when the interest is only in within-cluster effect, any model with the adjusted CR-SEs could be used. However, when the interest is to make accurate inferences of the between-cluster effect, researchers should use the adjusted CR-SEs with RS to have higher power and guard against unmodeled heterogeneity. We reanalyzed an example in Snijders & Bosker (2012) to demonstrate the use of the adjusted CR-SEs with different models. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new scale-invariant test for linear hypothesis of mean vectors with heteroscedasticity in high-dimensional settings. Most existing tests impose strong conditions on covariance matrices so that null distributions of their tests are asymptotically normal, which restricts the application of test procedures. However, our proposed test has different null distributions under mild conditions. Additionally, the well-known Welch-Satterthwaite chi-square approximation we adopted can automatically mimic the shapes of the null distributions of the test statistic. The performances of the test are illustrated by simulation and real data in finite samples which show that it has robustness and is more powerful than three competitors. [ABSTRACT FROM AUTHOR]

This article analyzes the Brazilian business cycle from Jan 2000 to February 2020 using a structural vector autoregression (SVAR) model. In Brazilian literature, articles aiming to obtain stylized facts using SVAR models adopt controversial identification hypotheses. The identification via heteroskedasticity emerges as an alternative, eliminating the need for such restrictions. Despite the limited sample size of Brazilian data, we exogenously select regimes with sufficient changes in the variance of the residuals over time. This allows us to identify a SVAR model via heteroskedasticity, as proposed by Brunnermeier et al. (Am Econ Rev 111(6):1845–79, 2021), using a different set of macro variables. The results from this approach are similar to those of a model identified via sign restrictions, providing support for the economic theory used by the latter to identify the shocks. The agnostic approach of identification via heteroskedasticity has enabled the identification of a new shock. We interpret it as a "pessimism" shock, particularly related to future expectations about economic activity. [ABSTRACT FROM AUTHOR]

Industry development indicators refer to a set of measures used to assess the performance and growth of industries. The main aim of this study was to assess the relationship between industry development indicators in Ethiopia using a multivariate GARCH model based on World Bank data from 1982 to 2021. A time series technique using annual data for the period 1982–2021 is utilized, and multivariate generalized autoregressive conditional heteroscedasticity was performed for volatility modeling. The results of the diagonal BEKK (1, 1)-GARCH model showed that there is strong evidence for a GARCH effect and the presence of a weaker ARCH effect, Equations show a statistically significant co-variation in shocks, which depends more on their lags than on past errors. Consequently, development indicator shocks are influenced by past information. The cross-volatility effects are higher than the own-volatility effects in Industry GDP, manufacturing GDP, and manufacturing exports. However, past volatility shocks in industry growth have less effect on cross-volatility than its own volatility shock. The implication of the study is that both domestic policymakers and development partners should support and motivate the growth of manufacturing sectors and manufacturing exports, since this is a necessary condition for promoting industry growth. [ABSTRACT FROM AUTHOR]

Competencies related to the evaluation of own cognitive processes, called metacognitive monitoring, are crucial as they help decide whether to persist in or desist from cognitive efforts. One of the most well-known phenomena in this context—the Dunning–Kruger effect—is that less skilled people tend to overestimate their performance. This effect has been reported for various kinds of performance including creativity. More recently, however, it has been suggested that this phenomenon could be a statistical artifact caused by the better-than-average effect and by regression toward the mean. Therefore, we examined the Dunning–Kruger effect in the context of creative thinking performance (i.e., divergent thinking ability) across two studies (Study 1: N = 425; Study 2: N = 317) and applied the classical quartile-based analysis as well as newly recommended, advanced statistical approaches: the Glejser test of heteroscedasticity and nonlinear quadratic regression. We found that the results indeed depended on the employed statistical method: While classical analyses supported the Dunning–Kruger effect across all conditions, it was not consistently supported by the more advanced statistical methods. These findings are in line with recent work challenging certain assumptions of the Dunning–Kruger effect and we discuss factors that undermine accurate self-assessments, especially in the context of creative performance. [ABSTRACT FROM AUTHOR]

In the last few years, frequentist model averaging has received more and more attention. However, the majority of related work focuses on the parametric model setup and devotes more energy to different mean structures but ignores the form of variance. In this paper, we consider a regression model with the mean being a varying-coefficient model and the variance being multiplicative heteroscedasticity and introduce a model averaging approach that uses the B-spline smoothing method to estimate unknown coefficient functions and obtains the estimators of unknown parameters in both the mean and variance functions of the model by the maximum likelihood method. The resulting model averaging estimator is proved to have asymptotic optimality under some regular conditions. Simulation experiments are conducted to compare the performance of our method with that of other common heteroscedasticity-robust model selection and model averaging methods under the finite-sample case. Our method is also verified in a real dataset. [ABSTRACT FROM AUTHOR]

This paper proposes zero-adjusted log-symmetric quantile regressions to deal with the issue of regression estimation when there are many zeros in the dependent variable. We introduce the zero-adjusted log-symmetric distributions that accommodate the presence of zeros and are consistent with heteroscedasticity. The model builds on a conditional quantile distribution and the parameters are estimated by maximum likelihood. The quantile approach is more flexible to analyze the effects of the explanatory variables on the dependent variable, increasing the relevance of the proposed models to the existing literature on zero-adjusted log-symmetric regression models. We apply Monte Carlo simulations to evaluate the quality of the estimates and compare alternative model specifications. As an illustration, the proposed models are confronted with actual data on hourly wages of retired people and time spent on extramarital affairs. The empirical results indicate that the proposed models provide a better fitting to the data. Particularly, the zero-adjusted log-symmetric quantile model outperformed the existing zero-adjusted gamma and zero-adjusted inverse Gaussian regression models. [ABSTRACT FROM AUTHOR]

This paper provides two specification tests for the system of spatial autoregressive model of order m. We derive the theoretical limit distributions and show in a detailed Monte Carlo simulation study that the tests result in reasonable sized testing procedures with large power. In the empirical application, we analyze Euro Stoxx 50 returns in two different time spans, looking for insights how well models with different specifications of the spatial weighting matrices (local, country, industry and country-industry specific dependencies including interaction effects) fit to the data. The analyzes also demonstrate the ability of the tests to detect inaccurate Value-at-Risk forecasts. [ABSTRACT FROM AUTHOR]

We propose an adjusted robust heteroscedastic autoregressive spatiotemporal model with a data-driven process to predict the hourly PM 2.5 concentrations in Xi'an and Xianyang, China. To begin with, an adjusted variance function for the heteroscedastic model is proposed to capture the different variances of the PM 2.5 concentrations during the periods of heating and non-heating. Compared to the traditional heteroscedastic method, the proposed adjusted heteroscedastic method has lower standard errors for most of the coefficient estimators. Secondly, to improve the prediction accuracy, the temporal correlations of the PM 2.5 data are incorporated into the adjusted heteroscedastic model by augmenting some additional predictors generated from an autoregressive process. The prediction results of the testing data demonstrate that the adjusted robust heteroscedastic autoregressive model significantly improves the prediction. Finally, utilizing the spatiotemporal simple kriging method, the spatial correlations of PM 2.5 concentrations at different air monitoring stations are incorporated into the prediction by adding the weighted robust residuals to the previous prediction. The prediction performance of the final proposed forecasting method is shown to be further enhanced. [ABSTRACT FROM AUTHOR]

This study aims to test for detecting a change point in the conditional quantile of general location-scale time series models. This issue is quite important in risk management because the conditional quantile is utilized to measure the value-at-risk or expected shortfall of financial assets. In this paper, we design two types of cumulative sum tests based on the conditional quantiles. Their limiting null distributions are derived under regularity conditions, together with consistency of the proposed tests under the alternative. Monte Carlo simulations demonstrate the good performance of the proposed tests in terms of both stability and power for various time series settings. A real data analysis using the daily returns of the Brent Oil futures also confirms the validity of the tests in real-world applications. [ABSTRACT FROM AUTHOR]

Fisher–von Mises–Langevin distributions are widely used for modeling directional data. In this paper, the problem of testing homogeneity of mean directions of several Fisher–von Mises–Langevin populations is considered when the concentration parameters are unknown and heterogeneous. First, an adaptive test based on the likelihood ratio statistic is proposed. Critical points are evaluated using a parametric bootstrap. Second, a heuristic test statistic is considered based on pairwise group differences. A nonparametric bootstrap procedure is adapted for evaluating critical points. Finally, a permutation test is also proposed. An extensive simulation study is performed to compare the size and power values of these tests with those proposed earlier. It is observed that both parametric and nonparametric bootstrap based tests achieve size values quite close to the nominal size. Asymptotic tests and permutation tests have size values higher than the nominal size. Bootstrap tests are seen to have very good power performance. The robustness of tests is also studied by considering contamination in Fisher–von Mises–Langevin distributions. R packages are developed for the actual implementation of all tests. A real data set has been considered for illustrations. [ABSTRACT FROM AUTHOR]

This paper studies a novel model averaging estimation issue for linear regression models when the responses are right censored and the covariates are measured with error. A novel weighted Mallows-type criterion is proposed for the considered issue by introducing multiple candidate models. The weight vector for model averaging is selected by minimizing the proposed criterion. Under some regularity conditions, the asymptotic optimality of the selected weight vector is established in terms of its ability to achieve the lowest squared loss asymptotically. Simulation results show that the proposed method is superior to the other existing related methods. A real data example is provided to supplement the actual performance. [ABSTRACT FROM AUTHOR]

Many community-rated health insurance markets include risk equalization (also known as risk adjustment) to mitigate risk selection incentives for competing insurers. Empirical evaluations of risk equalization typically quantify selection incentives through predictable profits and losses net of risk equalization for various groups of consumers (e.g. the healthy versus the chronically ill). The underlying assumption is that absence of predictable profits and losses implies absence of selection incentives. This paper questions this assumption. We show that even when risk equalization perfectly compensates insurers for predictable differences in mean spending between groups, selection incentives are likely to remain. The reason is that the uncertainty about residual spending (i.e., spending net of risk equalization) differs across groups, e.g., the risk of substantial losses is larger for the chronically ill than for the healthy. In a risk-rated market, insurers are likely to charge a higher profit mark-up (to cover uncertainty in residual spending) and a higher safety mark-up (to cover the risk of large losses) to chronically ill than to healthy individuals. When such differentiation is not allowed, insurers face incentives to select in favor of the healthy. Although the exact size of these selection incentives depends on contextual factors, our empirical simulations indicate they can be non-trivial. Our findings suggest that – in addition to the equalization of differences in mean spending between the healthy and the chronically ill – policy measures might be needed to diminish (or compensate insurers for) heteroscedasticity of residual spending across groups. [ABSTRACT FROM AUTHOR]

A nonparametric procedure to estimate the conditional probability that a nonstationary geostatistical process exceeds a certain threshold value is proposed. The method consists of a bootstrap algorithm that combines conditional simulation techniques with nonparametric estimations of the trend and the variability. The nonparametric local linear estimator, considering a bandwidth matrix selected by a method that takes the spatial dependence into account, is used to estimate the trend. The variability is modeled estimating the conditional variance and the variogram from corrected residuals to avoid the biasses. The proposed method allows to obtain estimates of the conditional exceedance risk in non-observed spatial locations. The performance of the approach is analyzed by simulation and illustrated with the application to a real data set of precipitations in the USA.Supplementary materials accompanying this paper appear on-line. [ABSTRACT FROM AUTHOR]

The objective is to provide a nonparametric bootstrap method for functional data that consists of independent realizations of a continuous one-dimensional process. The process is assumed to be nonstationary, with a functional mean and a functional variance, and dependent. The resampling method is based on nonparametric estimates of the model components. Numerical studies were conducted to check the performance of the proposed procedure, by approximating the bias and the standard error of two estimators. A practical application of the proposed approach to pollution data has also been included. Specifically, it is employed to make inference about the annual trend of ground-level ozone concentration at Yarner Wood monitoring station in the United Kingdom. Supplementary material to this paper is provided online. [ABSTRACT FROM AUTHOR]

The sequential tail empirical process is analyzed in a stochastic model allowing for serially dependent observations and heteroscedasticity of extremes in the sense of Einmahl et al. (J. R. Stat. Soc. Ser. B. Stat. Methodol. 78(1), 31–51, 2016). Weighted weak convergence of the sequential tail empirical process is established. As an application, a central limit theorem for an estimator of the extreme value index is proven. [ABSTRACT FROM AUTHOR]

Symmetric noise is the prevailing assumption in production analysis, but it is often violated in practice. Not only does asymmetric noise cause least-squares models to be inefficient, it can hide important features of the data which may be useful to the firm/policymaker. Here, we outline how to introduce asymmetric noise into a production or cost framework as well as develop a model to introduce inefficiency into said models. We derive closed-form solutions for the convolution of the noise and inefficiency distributions, the log-likelihood function, and inefficiency, as well as show how to introduce determinants of heteroskedasticity, efficiency and skewness to allow for heterogenous results. We perform a Monte Carlo study and profile analysis to examine the finite sample performance of the proposed estimators. We outline R and Stata packages that we have developed and apply to three empirical applications to show how our methods lead to improved fit, explain features of the data hidden by assuming symmetry, and how our approach is still able to estimate efficiency scores when the least-squares model exhibits the well-known "wrong skewness" problem in production analysis. The proposed models are useful for modeling risk linked to the outcome variable by allowing error asymmetry with or without inefficiency. [ABSTRACT FROM AUTHOR]

Theory cannot be fully validated unless original results have been replicated, resulting in conclusion consistency. Replications are the strongest source of evidence to verify research findings and knowledge claims. In the social sciences, replication studies often fail and thus a continuing need for replication studies to confirm tentative facts, expand knowledge to gain new understanding, and verify hypotheses. Failure to replicate in the social and behavioral sciences sometimes arises due to dissimilarity between hypotheses formulated in original and replication studies. Alternatively, failure to replicate also occurs when the same hypothesis is tested; but done so in the absence of knowledge from previous investigations, as when original study effect sizes are not considered in replication studies. To increase replicability of research findings, this paper demonstrates that the application of two one-sided tests to evaluate a replication question provides a superior means for conducting replications, assuming all other methodological procedures remained as similar as possible. Furthermore, this paper sought to explore the impact of heteroscedasticity and unbalanced designs in replication studies in four paired conditions of variance and sample size. Two Monte Carlo simulations, each with two stages, were conducted to investigate conclusion consistency among different replication procedures to determine the repeatability of an observed effect. Overall, the proposed approach yielded a higher proportion of successful replications than the conventional approach (testing the original null hypothesis of no effect). Thus, findings can be confirmed by replications and in the absence of confirmation, there cannot be a final statement about any theory. [ABSTRACT FROM AUTHOR]

We introduce methods to apply stochastic frontier analysis (SFA) to financial assets as an alternative to data envelopment analysis, because SFA allows us to fit a frontier with noisy data. In contrast to conventional SFA, we wish to deal with estimation risk, heteroscedasticity in noise and inefficiency terms. We investigate measurement error in the risk and return measures using a simulation–extrapolation method and develop residual plots to test model fit. We find that shrinkage estimators for estimation risk makes a striking difference to model fit, dealing with measurement error only improves confidence in the model, and the residual plots are vital for establishing model fit. The methods are important because they allow us to fit a frontier under the assumption that the risks and returns are not known exactly. [ABSTRACT FROM AUTHOR]

We investigate the performance of VaR (Value at Risk) forecasts, considering different multivariate models: HS (Historical Simulation), DCC-GARCH (Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity) with normal and Student's t distribution, GO-GARCH (Generalized Orthogonal-Generalized Autoregressive Conditional Heteroskedasticity), and copulas Vine (C-Vine, D-Vine, and R-Vine). For copula models, we consider that marginal distribution follow normal, Student's t and skewed Student's t distribution. We assessed the performance of the models using stocks belonging to the Ibovespa index during the period from January 2012 to April 2022. We build portfolios with 6 and 12 stocks considering two strategies to form the portfolio weights. We use a rolling estimation window of 500 and 1000 observations and 1%, 2.5%, and 5% as significance levels for the risk estimation. To evaluate the quality of the risk forecasts, we compute the realized loss and cost. Our results show that the performance of the models is sensitive to the use of different significance levels, rolling windows, and strategies to determine portfolio weights. Furthermore, we find that the model that presents the best trade-off between the costs from risk overestimation and underestimation does not coincide with the model suggested by the realized loss. [ABSTRACT FROM AUTHOR]

In a one-way design with unknown and unequal error variances, we are interested in testing the null hypothesis H 0 : μ 1 = μ 2 = ⋯ = μ k against the ordered alternative H 1 : μ 1 ≤ μ 2 ≤ ⋯ ≤ μ k (with at least one strict inequality). We propose the likelihood ratio test (LRT) as well as two Min-T tests (Min-T and AMin-T) and study their asymptotic behaviour. For better finite sample properties, a parametric bootstrap procedure is used. Asymptotic accuracy of the parametric bootstrap is established. Simulation results show that all the tests control the nominal level in case of small, moderate, and highly unbalanced sample sizes. This even holds for combinations with large variances. However, the asymptotic likelihood ratio test (ALRT) achieves the nominal size only for large samples and k = 3 . Power comparisons between LRT and Min-T test show that the LRT has more power than Min-T. However, the Min-T test is easier to use as it does not require evaluation of MLEs. Similar observations are made while comparing ALRT and AMin-T test. The robustness of these tests under departure from normality is also investigated by considering five non-normal distributions (t-distribution, Laplace, exponential, Weibull, and lognormal). When high variances are combined with small or highly unbalanced sample sizes, the tests are observed to be robust only for symmetric distributions. If variances of groups are equal and sample sizes of each group are at least 10, the proposed tests perform satisfactorily for symmetric and skewed distributions. R packages are developed for all tests and a practical example is provided to illustrate their application. [ABSTRACT FROM AUTHOR]

In this paper, a two-way ANOVA model is considered when interactions between two factors are present and errors are normally distributed with heteroscedastic cell variances. The problem of testing the homogeneity of simple effects against their ordered alternatives has not been studied before in the literature for this model. Here, we develop the likelihood ratio test and two heuristic tests based on multiple contrasts. Two algorithms are proposed for finding solutions of the likelihood equations under the null and full parameter spaces. The existence and uniqueness of solutions and convergence of the algorithms are established. Hence, this paper also finds the maximum likelihood estimators of simple effects when they are order restricted. A parametric bootstrap procedure is used to implement all the tests and the asymptotic accuracy of the parametric bootstrap is proved. An extensive simulation study is carried out to study the size and power performance of the tests. Results show that all the parametric bootstrap-based test procedures achieve nominal sizes for small, moderate, and highly unbalanced sample sizes. Nominal size is controlled even in the case when small samples are combined with large and heterogeneous variances. The robustness of tests is also investigated under departure from normality. The proposed tests are illustrated with the help of three examples. Finally, an "R" package has been developed. [ABSTRACT FROM AUTHOR]

A Zero-drift GARCH (ZD-GARCH) model is recently proposed to study conditional and unconditional heteroskedasticity together. Despite its attractive statistical properties, our research demonstrates that the stability test based on this model fails when structural changes are present. To overcome this issue, we allow the Markov regime-switching (MRS) feature within the ZD-GARCH framework and propose an MRS-ZD-GARCH model. A revised stability estimator is further derived. The effectiveness of our proposed approach to test the stability with and without structural changes is evidenced via simulation studies. Using the empirical data of the S&P 500, NASDAQ and Apple returns, we show that the new model can also outperform the ZD-GARCH model in practice and provide more informative results. Therefore, the MRS-ZD-GARCH model could be a widely useful tool to study the stability of financial data and help address risk management issues in other contexts. [ABSTRACT FROM AUTHOR]

Plant growth is complex, and involves many processes and its understanding is essential to maximize the crop's potential. The use of modeling is an essential tool to characterize the plant's growth and development, in addition, generates strategies for future plantings, adapting the management. Therefore, this study aimed to apply and make considerations based on parameters of the Logistic and Gompertz models to the fresh plants mass, of three sunflower cultivars, sown in three seasons, and select the best model for the cultivars. The data used came from nine uniformity trials with the sunflower cultivars Aguará 6, Nusol 4510, and Rhino and were adjusted according to the accumulated growing degree days, using the Logistic and Gompertz models. Parameters were estimated using the methods of ordinary least squares (OLS) and generalized least squares (GLS). In the presence of violations, the power method was used to structure the variance. Fit quality of the models to the data was assessed by the adjusted determination coefficient, Akaike information criterion, and Schwarz's Bayesian criterion. Logistic and Gompertz models fitted the data, converging on interpretable parameters, both by OLS and GLS methods. The insertion of the power structure into the models resulted in a better fit for the data. The cultivars Aguará 6 and Nusol 4510 are best described by the Logistic model and present a higher positive growth phase in the first trial. The sunflower cultivar Rhino is best described by the Gompertz model and presents a reduction in the positive growth phase in the first trial. [ABSTRACT FROM AUTHOR]

Background: To utilize EQ-5D in economic evaluations, a societal-based value set is needed. To date, no value sets exist for any EQ-5D instrument in Pakistan. Previous EQ-5D studies conducted in Pakistan 'borrowed' health preferences developed in other countries. However, for a value set to be valid for Pakistani population, it should represent the preferences of the Pakistani population, and culture and living standards of Pakistan. Objective: The aim of this study was to derive a Pakistani EQ-5D-3L value set. Methods: A moderately representative sample aged 18 years and over was recruited from the Pakistani general population. A multi-stage stratified quota method with respect to ethnicity, gender, age and religion was utilized. Two elicitation techniques, the composite time trade-off (cTTO) and discrete choice experiments (DCE) were applied. Interviews were undertaken by trained interviewers using computer-assisted face-to-face interviews with the EuroQol Portable Valuation Technology (EQ-PVT) platform. To estimate the value set, a hybrid regression model combining cTTO and DCE data was used. Results: A total of 289 respondents who completed the interviews were included for the analysis. The hybrid model correcting for heteroskedasticity without a constant was selected as the final model for the value set. It is shown that being unable to do usual activities (level 3) was assigned the largest weight, followed by mobility level 3, self-care level 3, pain/discomfort level 3 and anxiety/depression level 3. The worst health state was assigned the value − 0.171 in the final model. Conclusions: A Pakistani country-specific EQ-5D-3L value set is now available. The availability of this value set may help promote and facilitate health economic evaluations and health-related quality-of-life (HRQoL) research in Pakistan. [ABSTRACT FROM AUTHOR]

The paper deals with a robust, projection-type measure of effect size when comparing J > 1 dependent groups. The measure of effect size is scale invariant and does not assume or require that the underlying multivariate distribution is elliptically contoured. By design the measure of effect size is equal to zero when the corresponding measures of location are equal. A simple method is suggested for testing the hypothesis that this effect size is zero. The method is readily extended to comparing K-variate distributions associated with two independent groups. One of the main goal is to report simulation results on how well the method performs in terms of controlling the Type I error probability. The method, when comparing independent groups, is used to reveal new insights into the connection between depression and cortisol levels. [ABSTRACT FROM AUTHOR]

A functional time series approach is proposed for investigating spatial correlation in daily maximum temperature forecast errors for 111 cities spread across the U.S. The modelling of spatial correlation is most fruitful for longer forecast horizons, and becomes less relevant as the forecast horizon shrinks towards zero. For 6-day-ahead forecasts, the functional approach uncovers interpretable regional spatial effects, and captures the higher variance observed in inland cities versus coastal cities, as well as the higher variance observed in mountain and midwest states. The functional approach also naturally handles missing data through modelling a continuum, and can be implemented efficiently by exploiting the sparsity induced by a B-spline basis. The temporal dependence in the data is modeled through temporal dependence in functional basis coefficients. Independent first order autoregressions with generalized autoregressive conditional heteroskedasticity [AR(1)+GARCH(1,1)] and Student-t innovations work well to capture the persistence of basis coefficients over time and the seasonal heteroskedasticity reflecting higher variance in winter. Through exploiting autocorrelation in the basis coefficients, the functional time series approach also yields a method for improving weather forecasts and uncertainty quantification. The resulting method corrects for bias in the weather forecasts, while reducing the error variance. [ABSTRACT FROM AUTHOR]

We use weekly changes in the size of the Federal Reserve's balance sheet as a policy tool that has largely been ignored in the literature to investigate the relationship between the unconventional monetary policy and stock market returns when the federal funds rate reaches the zero lower bound. Our empirical framework is based on a structural VAR that is identified using heteroscedasticity in weekly data on the components of the Fed's balance sheet. We find evidence that the unconventional expansionary monetary policy is effective in stimulating the stock market, as it has positive and statistically significant effects on stock returns. In extending our analysis to disaggregate returns, our findings suggest heterogenous and asymmetric responses of disaggregate returns to an unconventional policy shock. [ABSTRACT FROM AUTHOR]

A fixed-effects model estimates the regressor effects on the mean of the response, which is inadequate to account for heteroscedasticity. In this paper, we adapt the asymmetric least squares (expectile) regression to the fixed-effects panel model and propose a new model: expectile regression with fixed effects (ERFE). The ERFE model applies the within transformation strategy to solve the incidental parameter problem and estimates the regressor effects on the expectiles of the response distribution. The ERFE model captures the data heteroscedasticity and eliminates any bias resulting from the correlation between the regressors and the omitted factors. We derive the asymptotic properties of the ERFE estimators and suggest robust estimators of its covariance matrix. Our simulations show that the ERFE estimator is unbiased and outperforms its competitors. Our real data analysis shows its ability to capture data heteroscedasticity (see our R package, https://github.com/amadoudiogobarry/erfe). [ABSTRACT FROM AUTHOR]

One of the most challenging aspects of multivariate geostatistics is dealing with complex relationships between variables. Geostatistical co-simulation and spatial decorrelation methods, commonly used for modelling multiple variables, are ineffective in the presence of multivariate complexities. On the other hand, multi-Gaussian transforms are designed to deal with complex multivariate relationships, such as non-linearity, heteroscedasticity and geological constraints. These methods transform the variables into independent multi-Gaussian factors that can be individually simulated. This study compares the performance of the following multi-Gaussian transforms: rotation based iterative Gaussianisation, projection pursuit multivariate transform and flow transformation. Case studies with bivariate complexities are used to evaluate and compare the realisations of the transformed values. For this purpose, commonly used geostatistical validation metrics are applied, including multivariate normality tests, reproduction of bivariate relationships, and histogram and variogram validation. Based on most of the metrics, all three methods produced results of similar quality. The most obvious difference is the execution speed for forward and back transformation, for which flow transformation is much slower. [ABSTRACT FROM AUTHOR]

Proficiency testing is the use of interlaboratory comparisons to assess the ability of a laboratory to perform a particular test or measurement competently and demonstrate the reliability of the results generated. To achieve this goal, the international standard ISO 13528:2022 presents four performance statistics and one of them is the z score. Some researchers have proposed an alternative approach for proficiency testing scheme interpretation based on an analysis of variance method. This short communication presents residual analysis to choose an appropriate analysis of variance model for proficiency testing interpretation when the assigned value is obtained by consensus value among participants. The analysis of variance approach was compared with z score to assess the performance of interlaboratory comparison for determining the amount of NOx present in vehicle emissions. Preliminary results indicate that the conclusions obtained by both methodologies differ for one of the participating laboratories. This occurs due to violation of the homoscedasticity assumption which is not considered when using the z score. On the other hand, residual analysis can provide such information and feasible generalized least squares can deal with heteroscedasticity. [ABSTRACT FROM AUTHOR]

A two-step estimator of a nonparametric regression function via Kernel regularized least squares (KRLS) with parametric error covariance is proposed. The KRLS, not considering any information in the error covariance, is improved by incorporating a parametric error covariance, allowing for both heteroskedasticity and autocorrelation, in estimating the regression function. A two step procedure is used, where in the first step, a parametric error covariance is estimated by using KRLS residuals and in the second step, a transformed model using the error covariance is estimated by KRLS. Theoretical results including bias, variance, and asymptotics are derived. Simulation results show that the proposed estimator outperforms the KRLS in both heteroskedastic errors and autocorrelated errors cases. An empirical example is illustrated with estimating an airline cost function under a random effects model with heteroskedastic and correlated errors. The derivatives are evaluated, and the average partial effects of the inputs are determined in the application. [ABSTRACT FROM AUTHOR]

A large literature on modelling cross-section dependence in panels has been developed through interactive effects. However, there are areas where research has not really caught on yet. One such area is the one concerned with whether the regressors are correlated with factor loadings or not. This is an important issue because if the regressors are uncorrelated with loadings, we can simply use the consistent two-way fixed effects (FE) estimator without employing any more sophisticated econometric methods such as the principal component (PC) or the common correlated effects estimators. We explore this issue, which has received surprisingly little attention and propose a Hausman-type test to address the matter. Further, we develop two nonparametric variance estimators for the FE and PC estimators as well as their difference, that are robust to the presence of heteroscedasticity, autocorrelation and slope heterogeneity. Under the null hypothesis of no correlation between the regressors and loadings the proposed test follows the χ 2 distribution asymptotically. Monte Carlo simulation results confirm satisfactory size and power performance of the test even in small samples. Finally, we provide extensive empirical evidence in favour of uncorrelated factor loadings. In this situation, the FE estimator would provide a simple and robust estimation strategy which is invariant to nontrivial computational issues associated with the PC estimator. [ABSTRACT FROM AUTHOR]

For the panel data case where cross-sectional units are nested within higher-level groups, and there are many such groups, we propose a test that allows one to determine whether controlling for fixed effects at the more aggregate level is sufficient. The alternative is that one should allow for fixed effects at the unit level. The regression-based test is simple to carry out, even for unbalanced panels. In addition, the test is easily made robust to arbitrary heteroskedasticity, serial correlation across time, and even cluster correlation at the group level. We also show how to modify the traditional Hausman test of a single coefficient to be fully robust to serial correlation and cluster correlation. The tests work well in terms of size and power in a small simulation study. We apply the test to choosing between a fixed effects analysis at the school district level and the disaggregated school level. [ABSTRACT FROM AUTHOR]

Modern variable selection procedures make use of penalization methods to execute simultaneous model selection and estimation. A popular method is the least absolute shrinkage and selection operator, the use of which requires selecting the value of a tuning parameter. This parameter is typically tuned by minimizing the cross-validation error or Bayesian information criterion, but this can be computationally intensive as it involves fitting an array of different models and selecting the best one. In contrast with this standard approach, we have developed a procedure based on the so-called “smooth IC” (SIC) in which the tuning parameter is automatically selected in one step. We also extend this model selection procedure to the distributional regression framework, which is more flexible than classical regression modelling. Distributional regression, also known as multiparameter regression, introduces flexibility by taking account of the effect of covariates through multiple distributional parameters simultaneously, e.g., mean and variance. These models are useful in the context of normal linear regression when the process under study exhibits heteroscedastic behaviour. Reformulating the distributional regression estimation problem in terms of penalized likelihood enables us to take advantage of the close relationship between model selection criteria and penalization. Utilizing the SIC is computationally advantageous, as it obviates the issue of having to choose multiple tuning parameters. [ABSTRACT FROM AUTHOR]

In this study, the performance of continuous autoregressive moving average (CARMA), CARMA-generalized autoregressive conditional heteroscedasticity (CARMA-GARCH), random forest, support vector regression and ant colony optimization (SVR-ACO), and support vector regression and ant lion optimizer (SVR-ALO) models in bivariate simulating of discharge based on the rainfall variables in monthly time scale was evaluated over four sub-basins of Lake Urmia, located in northwestern Iran. The models were assessed in two stages: train and test. The results showed that the CARMA-GARCH hybrid model offered better performance in all cases than the stand-alone CARMA. The improvement percentages of the error rate in the CARMA model compared to the CARMA-GARCH hybrid model in the Mahabad Chai, Nazlu Chai, Siminehrood, and Zola Chai sub-basins were 9, 20, 17, and 6.4%, respectively, in the training phase. Among the models, the hybrid SVR models integrated with ACO and ALO optimization algorithms presented the best performance based on the Taylor diagram and evaluation criteria. Considering the use of ant colony and ant lion optimization algorithms to optimize the support vector regression model's parameters, these models offered the best performance in the study area to simulate the discharge. The improvement percentages of the error rate in the SVR-ACO model compared to the CARMA-GARCH hybrid model in the Mahabad Chai, Nazlu Chai, Siminehrood, and Zola Chai sub-basins were 11, 10, 19, and 21%, respectively, in the training phase. In contrast, the random forest model provided the lowest accuracy and the highest error in discharge simulation. [ABSTRACT FROM AUTHOR]

Spatio-temporal forecasting has various applications in climate, transportation, geo-statistics, sociology, economics and in many other fields of study. The modelling of temperature and its forecasting is a challenging task due to spatial dependency of time series data and nonlinear in nature. To address these challenges, in this study we proposed hybrid Space–Time Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroscedasticity (STARMA-GARCH) model in order to describe and identify the behaviour of monthly maximum temperature and temperature range in Bihar. At the modelling process of STARMA, spatial characteristics are incorporated into the model using a weight matrix based on great circle distance between the regions. The residuals from the fitted STARMA model have been tested for checking the behaviour of nonlinearity. Autoregressive Conditional Heteroscedasticity-Lagrange Multiplier (ARCH-LM) test has been carried out for the ARCH effect. The test results revealed that presence of both nonlinearity and ARCH effect. Hence, GARCH modelling is necessary. Therefore, the hybrid STARMA-GARCH model is used to capture the dynamics of monthly maximum temperature and temperature range. The results of the proposed hybrid STARMA 1 1 , 0 , 0 - GARCH 0 , 1 model have better modelling efficiency and forecasting precision over STARMA 1 1 , 0 , 0 model. [ABSTRACT FROM AUTHOR]

Objective: We aimed to develop an EQ-5D-5L value set for Romania. Methods: In line with the EuroQoL standardized valuation protocol, computer-assisted interviews were conducted face-to-face in a representative sample in Romania (November 2018–November 2019). Valuation methods included composite time trade-off and discrete choice experiment tasks. Several models were tested, including models that accounted for data censoring, panel structure of the data, heteroscedasticity, conditional logit, and hybrid models. The final model was selected based on logical consistency, theoretical considerations, and use of all available data. We compared our value set with other value sets from Central and Eastern Europe region. Results: Data from 1493 respondents was used to estimate the value set. A censored hybrid model corrected for heteroscedasticity was selected to represent the value set. The highest decrements in utility were observed for the pain/discomfort dimension (0.375), followed by the mobility dimension (0.293). Health utilities ranged from 1.000 to − 0.323 and 1.3% of the values were negative. The model was corrected with survey weights to better reflect the representativeness of the sample, but the first two coefficients of the self-care dimension stopped being logically consistent. Differences were found between the Romanian, Hungarian and Polish EQ-5D-5L value sets. Good agreement was noted with the Romanian EQ-5D-3L value set, with a swap between pain/discomfort and mobility in ranking of dimensions. Conclusion: A value set for EQ-5D-5L is now available for Romania. This will push one-step further the development of health technology assessment and encourage more health-related quality-of-life research to be conducted locally. [ABSTRACT FROM AUTHOR]

This paper contributes to the trade-energy literature by examining the effect of export product diversification on the energy demand of 30 countries located in the Global North over the period 1980–2014. A nonparametric time-varying coefficient panel data model with fixed effects is employed for its ability to produce robust outcomes in the presence of parameter instabilities, nonstationarity, regime shifts, and time variations. As a second step, the paper also utilizes the nonparametric fixed effects extension of Driscoll and Kraay, which is robust to spatial/cross-sectional dependence, as well as to autocorrelation and heteroscedasticity. The results indicate that export diversification lowers overall energy demand in the Global North, and the size of the impact has been on a gradual increase over the years. The paper concludes that if a conscious effort is made to ensure that product diversification is towards energy-efficient goods, export product diversification can serve as a useful strategy for managing energy consumption and mitigating its negative environmental effects. [ABSTRACT FROM AUTHOR]

We update and extend the non-parametric test proposed in Ashley and Patterson (J Financ Quant Anal 21:221–227, 2014) – of the proposition that the (pre-whitened) daily stock returns for a firm are serially independent, and hence unpredictable from their own past. That paper applied this test to daily returns from 1962 to 1981 for several U.S. corporations and aggregate indices, finding mixed evidence against this null hypothesis of serial independence. The returns dataset is updated here to include thirteen firms which are currently more relevant, and the sample is extended through the end of 2023. We also update the simulation methodology here to properly account for the conditional heteroskedasticity in the daily returns data, so that the present results should now be more statistically reliable. The results are broadly in line with our earlier results, but they do suggest further avenues of research in this area. [ABSTRACT FROM AUTHOR]

The coefficient of determination is well defined for linear models and its extension is long wanted for mixed-effects models in agricultural, biological, and ecological research. We revisit its extension to define measures for proportions of variation explained by the whole model, fixed effects only, and random effects only. We propose to calculate unexplained variations conditional on individual random and/or fixed effects so as to keep individual heterogeneity brought by available predictors. While these measures were naturally defined for linear mixed models, they can be defined for a generalized linear mixed model using a distance measured along its variance function, accounting for its heteroscedasticity. We demonstrate the promising performance and utility of our proposed methods via simulation studies as well as applications to real data sets in agricultural and ecological studies. [ABSTRACT FROM AUTHOR]

As extensions of means, expectiles embrace all the distribution information of a random variable. The expectile regression is computationally friendlier because the asymmetric least square loss function is differentiable everywhere. This regression also enables effective estimation of the expectiles of a response variable when potential explanatory variables are given. In this study, we propose the partial functional linear expectile regression model. The slope function and constant coefficients are estimated by using the functional principal component basis. The convergence rate of the slope function and the asymptotic normality of the parameter vector are established. To inspect the effect of the parametric component on the response variable, we develop Wald-type and expectile rank score tests and establish their asymptotic properties. The finite performance of the proposed estimators and test statistics are evaluated through simulation study. Results indicate that the proposed estimators are comparable to competing estimation methods and the newly proposed expectile rank score test is useful. The methodologies are illustrated by using two real data examples. [ABSTRACT FROM AUTHOR]

This study attempts to discuss the heteroscedasticity in a travel cost model by introducing a parameterized over-dispersion parameter. The Poisson and the negative binomial models are included in the study. The data set is from the National Survey of Fishing, Hunting, and Wildlife-Associated Recreation that was sponsored by the U.S. Fish & Wildlife Service (USFWS) in 2006. The results indicate that the negative binomial model that takes heteroscedasticity into consideration is better than the Poisson model and the other negative binomial model that treats the over-dispersion parameter as a constant. Education and travel cost are found to have negative and significant effects on the over-dispersion parameter. [ABSTRACT FROM AUTHOR]

Statistical analysis and simulation of annual maximum discharge values, while considering the corresponding maximum daily rainfall, provide a comprehensive view of flood management. This research presents the application of copula functions for simulating and modeling two variables of annual maximum discharge and corresponding precipitation. In this research, the performance of copula-based models and ARCH-based models including VAR-GARCH, copula, and copula-GARCH models was then evaluated to simulate the annual maximum discharge values. The simulation results of all three models were evaluated using NSE and NRMSE statistics. According to the 95% confidence intervals, the accuracy of all three models was confirmed. The correlation results of the studied pair variables confirmed the possibility of using copula-based models. The results of simulations revealed that a higher accuracy of the copula-GARCH approach compared with two models copula and VAR-GARCH. Considering 76% efficiency (NSE = 0.76) of the copula-GARCH approach, the results indicated 20 and 2.7% improvements in the performance of the proposed approach compared to both VAR-GARCH and copula models. The results also illustrated that by combining nonlinear ARCH models with copula-based simulations, the reliability of simulation results increased. The results obtained in this study suggest that the proposed method is very effective for increasing the certainty of frequency analysis of two variables. Because the copula-GARCH approach simulates the average values, the first and third quarters, as well as the amplitude of changes of 5 and 95% of the data better than the other two models. Violin plot of AMD series in copula scale [ABSTRACT FROM AUTHOR]

Proficiency testing schemes by interlaboratory comparisons are used to determine the performance of individual laboratories for specific tests or measurements. The international standard ISO 13528:2015 provides a description of statistical methods used for achieving this goal; one of these methods is the z score. The standard allows each participant to choose a measurement method and this can lead to inhomogeneity between participant variance known as heteroscedasticity. The ISO 13528:2015 standard does not mention heteroscedasticity in its statistical procedures. This paper describes a new approach, based on residuals analysis, to assess the performance of an interlaboratory comparison for determining the presence of benzoic acid in orange juice. The results indicate that the conclusions (using z scores) and the proposed approach are different for some laboratories. This occurs due to violation of the homoscedasticity assumption. The z score procedure does not consider this assumption violation while residual analysis can provide such information. Feasible generalized least squares allow one to deal with non-homoscedasticity. [ABSTRACT FROM AUTHOR]

The nature and novelty of crypto markets have given rise to speculative bubbles, which have permeated almost all cryptocurrencies. This paper shows that the log-periodic model with conditional heteroscedasticity structures has predictive capabilities to estimate the most likely crash date of cryptocurrency bubbles. We use the 2017 bitcoin bubble to perform the primary analysis and date a potential crash just four days before the price peak. We detect the crash date a month before the Bitcoin prices reach their highest value. The bitcoin price fell 30% two weeks after reaching its maximum value. Robustness exercises include the Ether bubble in 2021 and others in Bitcoin's history to show that the model can be helpful to crypto investors. [ABSTRACT FROM AUTHOR]

This paper presents a new non-parametric seasonal unit root testing framework that is robust to periodic non-stationary volatility in innovation variance by making an extension to the fractional seasonal variance ratio unit root tests of Eroğlu et al. (Econ Lett 167:75–80, 2018). The setup allows for both periodic heteroskedasticity structure of Burridge and Taylar (J Econ 104(1):91–117, 2001) and non-stationary volatility structure of Cavaliere and Taylor (Econ Theory 24(1):43-71, 2008). We show that the limiting null distributions of the variance ratio tests depend on nuisance parameters derived from the underlying volatility process. Monte Carlo simulations show that the standard variance ratio tests can be substantially oversized in the presence of such effects. Consequently, we propose wild bootstrap implementations of the variance ratio tests. Wild bootstrap resampling schemes are shown to deliver asymptotically pivotal inference. The simulation evidence depicts that the proposed bootstrap tests perform well in practice and essentially correct the size problems observed in the standard fractional seasonal variance ratio tests, even under extreme patterns of heteroskedasticity. [ABSTRACT FROM AUTHOR]