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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]

This paper provides empirical support for the notion that Autoregressive Conditional Heteroskedasticity (ARCH) in daily stock return data reflects time dependence in the process generating information flow to the market. Daily trading volume, used as a proxy for information arrival time, is shown to have significant explanatory power regarding the variance of daily returns, which is an implication of the assumption that daily returns are subordinated to intraday equilibrium returns. Furthermore, ARCH effects tend to disappear when volume is included in the variance equation. [ABSTRACT FROM AUTHOR]

The number of topics that a test collection contains has a direct impact on how well the evaluation results reflect the true performance of systems. However, large collections can be prohibitively expensive, so researchers are bound to balance reliability and cost. This issue arises when researchers have an existing collection and they would like to know how much they can trust their results, and also when they are building a new collection and they would like to know how many topics it should contain before they can trust the results. Several measures have been proposed in the literature to quantify the accuracy of a collection to estimate the true scores, as well as different ways to estimate the expected accuracy of hypothetical collections with a certain number of topics. We can find ad-hoc measures such as Kendall tau correlation and swap rates, and statistical measures such as statistical power and indexes from generalizability theory. Each measure focuses on different aspects of evaluation, has a different theoretical basis, and makes a number of assumptions that are not met in practice, such as normality of distributions, homoscedasticity, uncorrelated effects and random sampling. However, how good these estimates are in practice remains a largely open question. In this paper we first compare measures and estimators of test collection accuracy and propose unbiased statistical estimators of the Kendall tau and tau AP correlation coefficients. Second, we detail a method for stochastic simulation of evaluation results under different statistical assumptions, which can be used for a variety of evaluation research where we need to know the true scores of systems. Third, through large-scale simulation from TREC data, we analyze the bias of a range of estimators of test collection accuracy. Fourth, we analyze the robustness to statistical assumptions of these estimators, in order to understand what aspects of an evaluation are affected by what assumptions and guide in the development of new collections and new measures. All the results in this paper are fully reproducible with data and code available online. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of this paper is to explore the performances of prominent and popular heteroscedasticity-consistent covariance matrix estimators (HCCMEs) in small samples. The HCCMEs are selected from the literature and their ability to estimate the true covariance matrix of the coefficient's vector is evaluated through simulation runs. We calculate the percentage difference between the expected value of the HCCME and the true covariance matrix to set a convenient stage to make the comparisons under several different regression settings. The main contribution of the paper is the inclusion of the HCCMEs that have been introduced into the literature recently. We report the performances of the HCCMEs under different settings of the covariates and error term variances. We let the covariates follow uniform, normal, Student's t, and Cauchy distributions and tailor the error term variances to increase gradually. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new generalized alpha-skew-T (GAST) distribution for generalized autoregressive conditional heteroskedasticity (GARCH) models in modelling daily Value-at-Risk (VaR). Some mathematical properties of the proposed distribution are derived including density function, moments and stochastic representation. The maximum likelihood estimation method is discussed to estimate parameters via a simulation study. Then, the real data application on S&P-500 index is performed to investigate the performance of GARCH models specified under GAST innovation distribution with respect to normal, Student's-t and Skew-T models in terms of the VaR accuracy. Backtesting methodology is used to compare the out-of-sample performance of the VaR models. The results show that GARCH models with GAST innovation distribution outperforms among others and generates the most conservative VaR forecasts for all confidence levels and for both long and short positions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we consider simultaneous confidence intervals for all-pairwise comparisons of treatment means in a one-way layout under heteroscedasticity. Two kinds of simultaneous intervals are provided based on the fiducial generalized pivotal quantities of the interest parameters. We prove that they both have asymptotically correct coverage. Simulation results and an example are also reported. It is concluded from calculational evidence that the second kind of simultaneous confidence intervals, which we provide, performs better than existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we perform the prediction of the signal-to-interference-plus-noise ratio (SINR) root mean square (RMS) in the IEEE 802.16 system. We obtain the time series of the SINR RMS using system-level simulations. The SINR RMS is a heteroscedastic stochastic process. We propose a nonlinear transform of the SINR RMS that generates a linear homoscedastic stochastic process, which may be considered Gaussian in practice. We construct the prediction model of this Gaussian stochastic process using the linear autoregressive process. We propose three predictions of the SINR RMS, that is, the minimum mean square, the median, and the maximum-likelihood predictions, using the prediction of the linear autoregressive process. Our minimum mean-square error (MMSE) prediction of the SINR RMS is more reliable than the prediction based on averaging of the SINR RMS. [ABSTRACT FROM AUTHOR]

A local measure of association that allows both heteroscedasticity and a non-linear association was developed during the 1990s. The basic goal is to measure the strength of the association between X and Y, given X, when Y = θ(X) + τ(X)ϵ for some unknown functions θ(X) and τ(X). Application of this method requires the estimation of the derivative of θ(X). The focus in this paper is on four alternatives to a very slight modification of the method used by Doksum et al. when estimating this derivative. The main result is that in simulations, a certain robust analogue of their method dominates in terms of mean squared error, even under normality. The bias of the method is found to be small but a little larger than the bias associated with the method used by Doksum et al. The method is based in part on bootstrap bagging followed by a lowess smooth. [ABSTRACT FROM AUTHOR]

In this paper, we consider a fixed regression model where the errors are a strictly stationary process and in which both functions, the conditional mean and the conditional variance (volatility), are unknown. Two nonparametric estimators of the volatility function based on local polynomial fitting are studied. Expressions of the asymptotic bias and variance are given and the asymptotic normality is shown for both estimators. The influence of the dependence of the data is observed in the expressions of the variance. A simulation study and an analysis with real economic data illustrate the behavior of the proposed nonparametric estimators. [ABSTRACT FROM AUTHOR]

The autoregressive conditional heteroscedastic (ARCH) model and its extensions have been widely used in modelling changing variances in financial time series. Since the asset return distributions frequently display tails heavier than normal distributions, it is worth while studying robust ARCH modelling without a specific distribution assumption. In this paper, rather than modelling the conditional variance, we study ARCH modelling for the conditional scale. We examine the L1-estimation of ARCH models and derive the limiting distributions of the estimators. A robust standardized absolute residual anticorrelation based on least absolute deviation estimation is proposed. Then a robust portmanteau statistic is constructed to test the adequacy of the model, especially the specification of the conditional scale. We obtain their asymptotic distributions under mild condition. Examples show that the suggested L1-norm estimators and the goodness-of-fit test are robust against error distributions and are accurate for moderate sample sizes. This paper provides a useful tool in modelling conditional heteroscedastic time series data. [ABSTRACT FROM AUTHOR]

This article evaluates the performance of alternative estimation methods for gravity models with heteroscedasticity and zero trade values. Both problematic issues, recently addressed by Santos Silva and Tenreyro in an influential paper, are re-examined here. We use Monte Carlo simulations to compare the Pseudo Poisson Maximum Likelihood (PPML) estimator recommended by Santos Silva and Tenreyro, a Gamma Pseudo-Maximum-Likelihood (GPML), a Nonlinear Least Squares (NLS) estimator and a Feasible Generalized Least Squares (FGLS) estimator with more traditional techniques. Additionally, estimates of the gravity equation are obtained for three different data sets with the abovementioned methods. The results of the simulation study indicate that, although the PPML estimator is less affected by heteroscedasticity than others are, its performance is similar, in terms of bias and SEs, to the FGLS estimator performance, in particular for small samples. GPML presents however the lowest bias and SEs in the simulations without zero values. The results of the empirical estimations, using three different samples containing real data, indicate that the choice of estimator has to be made for each specific dataset. It is highly recommended to follow a model selection approach using a number of tests to select the more appropriate estimator for any application. [ABSTRACT FROM AUTHOR]

In this paper, we investigate tests of linear hypotheses in heteroscedastic one-way MANOVA via proposing a modified Bartlett (MB) test. The MB test is easy to conduct via using the usual χ-table. It is shown to be invariant under affine transformations, different choices of the contrast matrix used to define the same hypothesis and different labeling schemes of the mean vectors. Simulation studies and real data applications demonstrate that the MB test performs well and is generally comparable to Krishnamoorthy and Lu's (J Statist Comput Simul 80(8):873-887, ) parametric bootstrap test in terms of size controlling and power. [ABSTRACT FROM AUTHOR]

Abstract: We consider in this paper the efficient ways to generate multi-stage scenario trees. A general modified K-means clustering method is first presented to generate the scenario tree with a general structure. This method takes the time dependency of the simulated path into account. Based on the traditional and modified K-means analyses, the moment matching of multi-stage scenario trees is described as a linear programming (LP) problem. By simultaneously utilizing simulation, clustering, non-linear time series and moment matching skills, a sequential generation method and another new hybrid approach which can generate the whole multi-stage tree right off are proposed. The advantages of these new methods are: the vector autoregressive and multivariate generalized autoregressive conditional heteroscedasticity (VAR-MGARCH) model is adopted to properly reflect the inter-stage dependency and the time-varying volatilities of the data process, the LP-based moment matching technique ensures that the scenario tree generation problem can be solved more efficiently and the tree scale can be further controlled, and in the meanwhile, the statistical properties of the random data process are maintained properly. What is more important, our new LP methods can guarantee at least two branches are derived from each non-leaf node and thus overcome the drawback in relevant papers. We carry out a series of numerical experiments and apply the scenario tree generation methods to a portfolio management problem, which demonstrate the practicality, efficiency and advantages of our new approaches over other models or methods. [Copyright &y& Elsevier]

Quasi-likelihood was extended to right censored data to handle heteroscedasticity in the frame of the accelerated failure time (AFT) model. However, the assumption of known variance function in the quasi-likelihood for right censored data is usually unrealistic. In this paper, we propose a nonparametric quasi-likelihood by replacing the specified variance function with a nonparametric variance function estimator. This nonparametric variance function estimator is obtained by smoothing a function of squared residuals via local polynomial regression. The rate of convergence of the nonparametric variance function estimator and the asymptotic limiting distributions of the regression coefficient estimators are derived. It is demonstrated in simulations that for finite samples the proposed nonparametric quasi-likelihood method performs well. The new method is illustrated with one real dataset. [ABSTRACT FROM AUTHOR]

Heteroscedasticity checking in regression analysis plays an important role in modelling. It is of great interest when random errors are correlated, including autocorrelated and partial autocorrelated errors. In this paper, we consider multivariate t linear regression models, and construct the score test for the case of AR(1) errors, and ARMA(s,d) errors. The asymptotic properties, including asymptotic chi-square and approximate powers under local alternatives of the score tests, are studied. Based on modified profile likelihood, the adjusted score test is also developed. The finite sample performance of the tests is investigated through Monte Carlo simulations, and also the tests are illustrated with two real data sets. [ABSTRACT FROM AUTHOR]

This paper extends the scedasticity comparison among several groups of observations, usually complying with the homoscedastic and the heteroscedastic cases, in order to deal with data sets laying in an intermediate situation. As is well known, homoscedasticity corresponds to equality in orientation, shape and size of the group scatters. Here our attention is focused on two weaker requirements: scatters with the same orientation, but with different shape and size, or scatters with the same shape and size but different orientation. We introduce a multiple testing procedure that takes into account each of the above conditions. This approach discloses a richer information on the data underlying structure than the classical method only based on homo/heteroscedasticity. At the same time, it allows a more parsimonious parametrization, whenever the patterned model is appropriate to describe the real data. The new inferential methodology is then applied to some well-known data sets, chosen in the multivariate literature, to show the real gain in using this more informative approach. Finally, a wide simulation study illustrates and compares the performance of the proposal using data sets with gradual departure from homoscedasticity. [ABSTRACT FROM AUTHOR]

A two-step Lagrange Multiplier test strategy has recently been suggested as a device to reveal spatial nonstationarity and spurious spatial regression. The present paper generalises this procedure by incorporating control for unobserved heteroscedasticity. Using Monte Carlo simulation, the behaviour of several relevant tests for nonstationarity and/or heteroscedasticity is investigated. The two-step Lagrange Multiplier test for spatial nonstationarity turns out to be robust towards heteroscedasticity. While several tests for heteroscedasticity prove inconclusive under certain circumstances, it is shown that a Lagrange Multiplier test for heteroscedasticity based on spatially differenced variables serves well as an indication of heteroscedasticity irrespective of stationarity status. [ABSTRACT FROM AUTHOR]

Abstract: This paper discusses a class of minimum distance tests for fitting a parametric variance function in heteroscedastic regression models. These tests are based on certain minimized distances between a nonparametric variance function estimator and the parametric variance function estimator. The paper establishes the asymptotic normality of the proposed test statistics and that of the corresponding minimum distance estimator under the fitted model. These estimators turn out to be -consistent. Consistency of this sequence of tests at some fixed alternatives and asymptotic power under some local nonparametric alternatives are also discussed. Some simulation studies are conducted to assess the finite sample performance of the proposed test. [ABSTRACT FROM AUTHOR]

Fan and Yao (1998) proposed an efficient method to estimate the conditional variance of heteroskedastic regression models. Chen, Cheng, and Peng (2009) applied variance reduction techniques to the estimator of Fan and Yao (1998) and proposed a new estimator for conditional variance to account for the skewness of financial data. In this paper, we apply empirical likelihood methods to construct confidence intervals for the conditional variance based on the estimator of Fan and Yao (1998) and the reduced variance modification of Chen et al. (2009). Simulation studies and data analysis demonstrate the advantage of the empirical likelihood method over the normal approximation method. [ABSTRACT FROM AUTHOR]

In the present paper, a general class of heteroscedastic one-factor models is considered. In these models, the residual variances of the observed scores are explicitly modelled as parametric functions of the one-dimensional factor score. A marginal maximum likelihood procedure for parameter estimation is proposed under both the assumption of multivariate normality of the observed scores conditional on the single common factor score and the assumption of normality of the common factor score. A likelihood ratio test is derived, which can be used to test the usual homoscedastic one-factor model against one of the proposed heteroscedastic models. Simulation studies are carried out to investigate the robustness and the power of this likelihood ratio test. Results show that the asymptotic properties of the test statistic hold under both small test length conditions and small sample size conditions. Results also show under what conditions the power to detect different heteroscedasticity parameter values is either small, medium, or large. Finally, for illustrative purposes, the marginal maximum likelihood estimation procedure and the likelihood ratio test are applied to real data. [ABSTRACT FROM AUTHOR]

It is not unusual for the response variable in a regression model to be subject to censoring or truncation. Tobit regression models are specific examples of such a situation, where for some observations the observed response is not the actual response, but the censoring value (often zero), and an indicator that censoring (from below) has occurred. It is well-known that the maximum likelihood estimator for such a linear model assuming Gaussian errors is not consistent if the error term is not homoscedastic and normally distributed. In this paper, we consider estimation in the Tobit regression context when homoscedasticity and normality of errors do not hold, as well as when the true response is an unspecified nonlinear function of linear terms, using sliced inverse regression (SIR). The properties of SIR estimation for Tobit models are explored both theoretically and based on extensive Monte Carlo simulations. We show that the SIR estimator is a strong competitor to other Tobit regression estimators, in that it has good properties when the usual linear model assumptions hold, and can be much more effective than other Tobit model estimators when those assumptions break down. An example related to household charitable donations demonstrates the usefulness of the SIR estimator. [ABSTRACT FROM AUTHOR]

This paper presents a robust algorithm for a voice activity detector (VAD) based on generalized autoregressive conditional heteroscedasticity (GARCH) filter, variance gamma distribution (VGD), and adaptive threshold function. GARCH models are new statistical methods that are used especially in economic time series. There is a consensus that speech signals exhibit variances that change through time. GARCH models are a popular choice to model these changing variances. A speech signal is assumed to have a VGD because the VGD has heavier tails than the Gaussian distribution (GD). The distribution of noise signal is assumed to be Gaussian. In proposed method, heteroscedasticity will be modeled by GARCH, and then the parameters of the distributions will be estimated recursively. Finally, hard detection is the result of comparing a multiple observation likelihood ratio test (MOLRT) with an adaptive threshold function. The simulation results show that the proposed VAD is able to operate down to --5 dB and in nonstationary environments. [ABSTRACT FROM AUTHOR]

In this paper we compare through Monte Carlo simulations the finite sample properties of estimators of the fractional differencing parameter, d. This involves frequency domain, time domain, and wavelet based approaches, and we consider both parametric and semiparametric estimation methods. The estimators are briefly introduced and compared, and the criteria adopted for measuring finite sample performance are bias and root mean squared error. Most importantly, the simulations reveal that (1) the frequency domain maximum likelihood procedure is superior to the time domain parametric methods, (2) all the estimators are fairly robust to conditionally heteroscedastic errors, (3) the local polynomial Whittle and bias-reduced log-periodogram regression estimators are shown to be more robust to short-run dynamics than other semiparametric (frequency domain and wavelet) estimators and in some cases even outperform the time domain parametric methods, and (4) without sufficient trimming of scales the wavelet-based estimators are heavily biased. [ABSTRACT FROM AUTHOR]

In this paper, different cosmetic estimators are obtained, by using different individual weights for auxiliary-variable's observations that are still unexplored, and a simulation study is conducted to assess their performance. The results show that these weights are helpful in reducing heteroskedasticity, resulting in minimum SSE and therefore, may be considered as an alternative of linear transformations. Furthermore, it is shown that by using these weights in linear calibration approach, generated calibration estimators are equivalent to those produced by the instrumental approach. [ABSTRACT FROM AUTHOR]

Copyright of Afrika Statistika is the property of Statistics & Probability African Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Simulation of radiation is one of the most important issues in solar utilization. Time series models are useful tools in the estimation and forecasting of solar radiation series and their changes. In this paper, the effectiveness of autoregressive moving average (ARMA) models with various generalized autoregressive conditional heteroskedasticity (GARCH) processes, namely ARMA–GARCH models are evaluated for their effectiveness in radiation series. Six different GARCH approaches, which contain three different ARMA–GARCH models and corresponded GARCH in mean (ARMA–GARCH-M) models, are applied in radiation data sets from two representative climate stations in China. Multiple evaluation metrics of modeling sufficiency are used for evaluating the performances of models. The results show that the ARMA–GARCH(-M) models are effective in radiation series estimation. Both in fitting and prediction of radiation series, the ARMA–GARCH(-M) models show better modeling sufficiency than traditional models, while ARMA–EGARCH-M models are robustness in two sites and the ARMA–sGARCH-M models appear very competitive. Comparisons of statistical diagnostics and model performance clearly show that the ARMA–GARCH-M models make the mean radiation equations become more sufficient. It is recommended the ARMA–GARCH(-M) models to be the preferred method to use in the modeling of solar radiation series. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we establish the semiparametric efficient bound for the heteroscedastic partially linear single-index model with responses missing at random, and develop an efficient estimating equation method. By solving the estimating equation, we obtain estimators for the parameter vectors in the linear part and the single index part simultaneously. The estimators are asymptotically semiparametrically efficient when the propensity score function is specified correctly. It should be noted that the inverse probability weighted efficient estimating equation cannot be obtained directly from the full data efficient estimating equation by the inverse probability weighted approach. We establish the estimating equation by deriving the observed data efficient score function. Some simulation studies and a real data application were conducted to evaluate and illustrate the proposed methods. [Copyright &y& Elsevier]

We first propose in this paper a new test method for detecting heteroscedasticity of the error term in nonparametric regression. Some simulation experiments are then conducted to evaluate the performance of the proposed methodology. A real-world data set is finally analyzed to demonstrate the application of the method. [ABSTRACT FROM AUTHOR]

Abstract: Conjoint choice experiments have become a powerful tool to explore individual preferences. The consistency of respondents'' choices depends on the choice complexity. For example, it is easier to make a choice between two alternatives with few attributes than between five alternatives with several attributes. In the latter case it will be much harder to choose the preferred alternative which is reflected in a higher response error. Several authors have dealt with this choice complexity in the estimation stage but very little attention has been paid to set up designs that take this complexity into account. The core issue of this paper is to find out whether it is worthwhile to take this complexity into account in the design stage. We construct efficient semi-Bayesian D-optimal designs for the heteroscedastic conditional logit model which is used to model the across respondent variability that occurs due to the choice complexity. The degree of complexity is measured by the entropy, as suggested by . The proposed designs are compared with a semi-Bayesian D-optimal design constructed without taking the complexity into account. The simulation study shows that it is much better to take the choice complexity into account when constructing conjoint choice experiments. [Copyright &y& Elsevier]

Abstract: This paper compares the performances of neural networks and regression analysis when the data deviate from the homoscedasticity assumption of regression. To carry out this comparison, datasets are simulated that vary systematically on various dimensions like sample size, noise levels and number of independent variables. Analysis is performed using appropriate experimental designs and the results are presented. Prediction intervals for both the methods for the case of nonconstant error variance are also calculated and are graphically compared. Two real life data sets that are heteroscedastic have been analyzed and the findings are in line with the results obtained from experiments using simulated data sets. [Copyright &y& Elsevier]

Bias-corrected approximate 100(1-\alpha)\% pointwise and simultaneous confidence and prediction intervals for least squares support vector machines are proposed. A simple way of determining the bias without estimating higher order derivatives is formulated. A variance estimator is developed that works well in the homoscedastic and heteroscedastic case. In order to produce simultaneous confidence intervals, a simple Šidák correction and a more involved correction (based on upcrossing theory) are used. The obtained confidence intervals are compared to a state-of-the-art bootstrap-based method. Simulations show that the proposed method obtains similar intervals compared to the bootstrap at a lower computational cost. [ABSTRACT FROM AUTHOR]

Abstract: This paper summarizes findings that extend statistical distribution properties of the Moran coefficient index measuring spatial autocorrelation to non-normal random variables. Pitman–Koopmans theorem results are extended for the mean and the variance of this index. This summary includes a corollary to this theorem, as well as a new theorem (with its proof) and two conjectures implied by it. The first of these statements is supported by asymptotic heuristics; the second is supported by simulation experiment results. Mixture random variables that include heteroscedasticity or overdispersion also are explored. In addition, a simple asymptotic variance for the Moran coefficient is presented, assessed, and found to be very precise for sample sizes as small as 25–100. The principal conclusion is that independence and sample size are the most relevant properties for Pitman–Koopmans theorem results to be extended to non-normal random variables. The independent and identically distributed property reduces the necessary sample size for this extension, as do the properties of symmetry and normal approximation. [Copyright &y& Elsevier]

Abstract: Censored median regression has proved useful for analyzing survival data in complicated situations, say, when the variance is heteroscedastic or the data contain outliers. In this paper, we study the sparse estimation for censored median regression models, which is an important problem for high dimensional survival data analysis. In particular, a new procedure is proposed to minimize an inverse-censoring-probability weighted least absolute deviation loss subject to the adaptive LASSO penalty and result in a sparse and robust median estimator. We show that, with a proper choice of the tuning parameter, the procedure can identify the underlying sparse model consistently and has desired large-sample properties including root-n consistency and the asymptotic normality. The procedure also enjoys great advantages in computation, since its entire solution path can be obtained efficiently. Furthermore, we propose a resampling method to estimate the variance of the estimator. The performance of the procedure is illustrated by extensive simulations and two real data applications including one microarray gene expression survival data. [Copyright &y& Elsevier]

Abstract: The study of count data time series has been active in the past decade, mainly in theory and model construction. There are different ways to construct time series models with a geometric autocorrelation function, and a given univariate margin such as negative binomial. In this paper, we investigate negative binomial time series models based on the binomial thinning and two other expectation thinning operators, and show how they differ in conditional variance or heteroscedasticity. Since the model construction is in terms of probability generating functions, typically, the relevant conditional probability mass functions do not have explicit forms. In order to do simulations, likelihood inference, graphical diagnostics and prediction, we use a numerical method for inversion of characteristic functions. We illustrate the numerical methods and compare the various negative binomial time series models for a real data example. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a two-stage sampling method () for making inferences on the means of two normal populations with heteroscedastic variances which is usually referred to as the Behrens–Fisher problem (). The two-stage method can be used to control Type-II error and the length of confidence interval conditional on a specified Type-I error or confidence level. Simulation results show that the two-stage method improves the powers of the tests and its performance exhibits superior robustness properties over the existing single-stage methods. Moreover, our method can be easily extended to compare multiple populations. [Copyright &y& Elsevier]

Abstract: This paper discusses the problem of fitting a parametric model to the conditional variance function in a class of heteroscedastic regression models. The proposed test is based on the supremum of the Khmaladze type martingale transformation of a certain partial sum process of calibrated squared residuals. Asymptotic null distribution of this transformed process is shown to be the same as that of a time transformed standard Brownian motion. Test is shown to be consistent against a large class of fixed alternatives and to have nontrivial asymptotic power against a class of nonparametric alternatives, where n is the sample size. Simulation studies are conducted to assess the finite sample performance of the proposed test and to make a finite sample comparison with an existing test. [Copyright &y& Elsevier]

This paper considers an important practical problem in testing time-series data for nonlinearity in mean, namely, the distortion in the size of the test encountered if the the data are heteroskedastic. It is shown that using a heteroskedastic consistent auxiliary regression together with the wild bootstrap is an effective way of dealing with the problem. Simulation results indicate that significant improvements in empirical size are obtained, particularly in small samples. [ABSTRACT FROM AUTHOR]

Abstract: A standard assumption in regression analysis is homogeneity of the error variance. Violation of this assumption can have adverse consequences for the efficiency of estimators. In this paper, we propose an empirical likelihood based diagnostic technique for heteroscedasticity in the partially linear errors-in-variables models. Under mild conditions, a nonparametric version of Wilk''s theorem is derived. Simulation results reveal that our test performs well in both size and power. [Copyright &y& Elsevier]

An efficient treatment of practical issues on detecting multiple structural changes is presented. The efficacy of this method is examined by comparing the conventional hypothesis-testing method via comprehensive simulations and empirical applications. The method recommended is a model-selection using the Bayesian information criterion and allowing for heteroscedasticity. Empirical results show that the first structural change of Japanese economic growth occurred in 1974Q2, which was detected in 1979Q4, and that the second structural change occurred in 1992Q2, which was detected in 1998Q 1. Two advantages of the model-selection method compared to the hypothesis-testing method are also discussed. [ABSTRACT FROM AUTHOR]

We consider a difference-in-differences setting with a continuous outcome. The standard practice is to take its logarithm and then interpret the results as an approximation of the multiplicative treatment effect on the original outcome. We argue that a researcher should rather focus on the non-transformed outcome when discussing causal inference. The first step should be to decide whether the time trend is more likely to hold in multiplicative or level form. If the former, it is preferable to estimate an exponential model by Poisson Pseudo Maximum Likelihood, which does not require statistical independence of the error term. Running OLS on the log-linearised model might instead lead to confounding distributional and mean changes. We illustrate the argument with a simulation exercise. [ABSTRACT FROM AUTHOR]

Stochastic kriging is a popular metamodeling technique to approximate computationally expensive simulation models. However, it typically treats the simulation model as a black box in practice and often fails to capture the highly nonlinear response surfaces that arise from queueing simulations.We propose a simple, effective approach to improve the performance of stochastic kriging by incorporating stylized queueing models that contain useful information about the shape of the response surface. We provide several statistical tools to measure the usefulness of the incorporated stylized models. We show that even a relatively crude stylized model can substantially improve the prediction accuracy of stochastic kriging. [ABSTRACT FROM AUTHOR]

Cross-lagged panel models (CLPMs) are widely used to test mediation with longitudinal panel data. One major limitation of the CLPMs is that the model effects are assumed to be fixed across individuals. This assumption is likely to be violated (i.e., the model effects are random across individuals) in practice. When this happens, the CLPMs can potentially yield biased parameter estimates and misleading statistical inferences. This article proposes a model named a random-effects cross-lagged panel model (RE-CLPM) to account for random effects in CLPMs. Simulation studies show that the RE-CLPM outperforms the CLPM in recovering the mean indirect and direct effects in a longitudinal mediation analysis when random effects exist in the population. The performance of the RE-CLPM is robust to a certain degree, even when the random effects are not normally distributed. In addition, the RE-CLPM does not produce harmful results when the model effects are in fact fixed in the population. Implications of the simulation studies and potential directions for future research are discussed. [ABSTRACT FROM AUTHOR]

Background: Using next-generation sequencing technology to measure gene expression, an empirically intriguing question concerns the identification of differentially expressed genes across treatment groups. Existing methods aim to identify genes whose mean expressions differ among treatment groups by assuming equal dispersion across all groups. For syndromes, however, various combinations of gene expression alterations can result in the same disease, leading to greater heteroscedasticity in the biological replicates in the disease group compared to the normal group. Traditional methods that only consider changes in the mean will fail to fully analyze gene expression in such a scenario. In addition, sequencing technology is relatively expensive; most labs can only afford a few replicates per treatment group, which poses further challenges to reliably estimating the mean and dispersion under each treatment condition. Results: We designed an empirical Bayes method and a pooled permutation test to simultaneously consider the change in mean and dispersion across treatment groups. We further computed confidence intervals based on Bayes estimates to identify differentially expressed genes that are unique to each disease sample as well as those that are common across all disease samples. We illustrated our method by applying it to gene expression data from a large offspring syndrome experiment, which motivated this study. We compared our method to competing approaches through simulation studies that mimicked the real datasets to demonstrate the effectiveness of our proposed method. Conclusions: We will show that, compared to popular methods that only aim to find the difference in the mean, our method can capture greater variation in the disease group to effectively identify differentially expressed genes for syndromes. [ABSTRACT FROM AUTHOR]

Mixed‐effects models for repeated measures (MMRM) analyses using the Kenward‐Roger method for adjusting standard errors and degrees of freedom in an “unstructured” (UN) covariance structure are increasingly becoming common in primary analyses for group comparisons in longitudinal clinical trials. We evaluate the performance of an MMRM‐UN analysis using the Kenward‐Roger method when the variance of outcome between treatment groups is unequal. In addition, we provide alternative approaches for valid inferences in the MMRM analysis framework. Two simulations are conducted in cases with (1) unequal variance but equal correlation between the treatment groups and (2) unequal variance and unequal correlation between the groups. Our results in the first simulation indicate that MMRM‐UN analysis using the Kenward‐Roger method based on a common covariance matrix for the groups yields notably poor coverage probability (CP) with confidence intervals for the treatment effect when both the variance and the sample size between the groups are disparate. In addition, even when the randomization ratio is 1:1, the CP will fall seriously below the nominal confidence level if a treatment group with a large dropout proportion has a larger variance. Mixed‐effects models for repeated measures analysis with the Mancl and DeRouen covariance estimator shows relatively better performance than the traditional MMRM‐UN analysis method. In the second simulation, the traditional MMRM‐UN analysis leads to bias of the treatment effect and yields notably poor CP. Mixed‐effects models for repeated measures analysis fitting separate UN covariance structures for each group provides an unbiased estimate of the treatment effect and an acceptable CP. We do not recommend MMRM‐UN analysis using the Kenward‐Roger method based on a common covariance matrix for treatment groups, although it is frequently seen in applications, when heteroscedasticity between the groups is apparent in incomplete longitudinal data. [ABSTRACT FROM AUTHOR]

This study investigates coefficient bias and heteroscedasticity resulting from scale differences in accounting levels-based research designs analytically and using simulations based on accounting data. Findings indicate that including a scale proxy as an independent variable is more effective than deflation at mitigating coefficient bias, even if the proxy is 95 percent correlated with the true scale factor. In fact, deflation can worsen coefficient bias. Also, deflation often does not noticeably reduce heteroscedasticity and can decrease estimation efficiency. White (1980) standard errors are close to the true ones in regressions using undeflated variables. Replications of specifications in three recent accounting studies confirm the simulation findings. The findings suggest that when scale differences are of concern, accounting researchers should include a scale proxy as an independent variable and report inferences based on White standard errors. [ABSTRACT FROM AUTHOR]

Summary: Nonparametric regression is a fundamental problem in statistics but challenging when the independent variable is measured with error. Among the first approaches was an extension of deconvoluting kernel density estimators for homescedastic measurement error. The main contribution of this article is to propose a new simulation‐based nonparametric regression estimator for the heteroscedastic measurement error case. Similar to some earlier proposals, our estimator is built on principles underlying deconvoluting kernel density estimators. However, the proposed estimation procedure uses Monte Carlo methods for estimating nonlinear functions of a normal mean, which is different than any previous estimator. We show that the estimator has desirable operating characteristics in both large and small samples and apply the method to a study of benzene exposure in Chinese factory workers. [ABSTRACT FROM AUTHOR]

Background: Mendelian randomization studies perform instrumental variable (IV) analysis using genetic IVs. Results of individual Mendelian randomization studies can be pooled through meta-analysis. We explored how different variance estimators influence the meta-analysed IV estimate.Methods: Two versions of the delta method (IV before or after pooling), four bootstrap estimators, a jack-knife estimator and a heteroscedasticity-consistent (HC) variance estimator were compared using simulation. Two types of meta-analyses were compared, a two-stage meta-analysis pooling results, and a one-stage meta-analysis pooling datasets.Results: Using a two-stage meta-analysis, coverage of the point estimate using bootstrapped estimators deviated from nominal levels at weak instrument settings and/or outcome probabilities ≤ 0.10. The jack-knife estimator was the least biased resampling method, the HC estimator often failed at outcome probabilities ≤ 0.50 and overall the delta method estimators were the least biased. In the presence of between-study heterogeneity, the delta method before meta-analysis performed best. Using a one-stage meta-analysis all methods performed equally well and better than two-stage meta-analysis of greater or equal size.Conclusions: In the presence of between-study heterogeneity, two-stage meta-analyses should preferentially use the delta method before meta-analysis. Weak instrument bias can be reduced by performing a one-stage meta-analysis. [ABSTRACT FROM AUTHOR]

We consider the problem of comparingkregression models, when the variances are not assumed to be equal. For this problem, the classical F test can lead to misleading results, and there is no simple test which adequately controls the size when the sample sizes are small. Fork= 2, the most widely used test is the “weighted F test,” also known as the “asymptotic Chow test.” But this test does not work well for small samples, and various modifications have been proposed in the literature. Fork> 2, few tests are available and only the parametric-bootstrap (PB) test of Tian et al. (2009) controls the size fairly adequately. In this article, we propose three fairly simple F tests which can easily be applied in the general case,k⩾ 2, and avoid the complications of the PB test. Our simulations indicate that these tests have satisfactory performance. Also, our simulations confirm that the power properties of our proposed tests are similar to the PB test. Therefore, our proposed tests provide simple alternatives to the PB test, which can easily be used by practitioners who may not be familiar with the PB. [ABSTRACT FROM AUTHOR]