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The article concerns the problem of parameter estimation for Type-I censored data using general estimating equations. It proposes a new class of estimators that are analogs to the estimators obtained by the method of moments for complete samples. It is proved that the proposed estimators are exactly the maximum likelihood estimators in the class of distributions from the exponential family. The asymptotic properties of the presented estimators are compared to the maximum likelihood ones. The obtained results are presented jointly with a simulation study and computational examples. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a class of periodic integer-valued autoregressive BL-PINAR(1) models with Bell innovations distribution based on the binomial thinning operator. The basic probabilistic and statistical properties of this class are studied. Indeed, the first and the second moment periodically stationary conditions are established. The closed forms of these moments are, under the obtained conditions, derived. Furthermore, the periodic autocovariance structure is also considered while providing the closed form of the periodic autocorrelation function. The conditional least squares (CLS), Yule–Walker (YW), weighted conditional least squares (WCLS), and conditional maximum likelihood (CML) methods are applied to estimate the underlying parameters. The asymptotic properties of the CLS and the YW estimators are obtained. The performances of these methods are compared through a simulation study. An application on a real data set is provided. [ABSTRACT FROM AUTHOR]

In the present paper, we are mainly concerned with the statistical inference for the functional of nonparametric copula models satisfying linear constraints. The asymptotic properties of the obtained estimates and test statistics are given. Finally, a general notion of bootstrap for the proposed estimates and test statistics, constructed by exchangeably weighting sample, is presented, which is of its own interest. These results are proved under some standard structural conditions on some classes of functions and some mild conditions on the model, without assuming anything about the marginal distribution functions, except continuity. Our theoretical results and numerical examples by simulations demonstrate the merits of the proposed techniques. [ABSTRACT FROM AUTHOR]

In this article, we investigate a linear model that incorporates stationary causal processes, with a focus on utilizing Huber-Dutter methods to investigate the estimators of unknown parameters and a scale for the errors. Our results indicate that the Huber-Dutter methods can effectively be utilized for linear models that feature errors, which are short-range dependent linear processes, heavy-tailed linear processes, as well as some commonly used non linear time series. [ABSTRACT FROM AUTHOR]

In real data analysis, the underlying model is frequently unknown. Hence, the modeling strategy plays a key role in the success of data analysis. Inspired by the idea of model averaging, we propose a novel semiparametric modeling strategy for the conditional quantile prediction, without assuming that the underlying model is any specific parametric or semiparametric model. Due to the optimality of the weights selected by leave-one-out cross-validation, the proposed modeling strategy provides a more precise prediction than those based on some commonly used semiparametric models such as the varying coefficient and additive models. Asymptotic properties are established in the proposed modeling strategy along with its estimation procedure. We conducted extensive simulations to compare our method with alternatives across various scenarios. The results show that our method provides more accurate predictions. Finally, we applied our approach to the Boston housing data, yielding more precise quantile predictions of house prices compared with commonly used methods, and thus offering a clearer picture of the Boston housing market. [ABSTRACT FROM AUTHOR]

This paper focuses on the estimation of parameters in linear models with fixed regressors and exchangeable errors, especially, these errors are assumed to be a subset of an infinite exchangeable sequence. We propose a modified M-estimation to estimate the slope parameter, which outperforms the traditional M-estimation when the random errors are exchangeable. The modified M-estimation utilizes a design point centralization technique, leading to a more suitable estimator with improved theoretical properties. To establish the asymptotic properties of the modified M-estimator, we also develop a weighted central limit theorem for exchangeable sequences, which is of independent interest. Regarding the intercept, we prove that there is no consistent estimator for the intercept when the exchangeable errors have non-zero covariance, and we propose an unbiased estimator for the intercept as a viable compromise. Based on our theoretical findings, we develop a simulation-based approach to approximate the confidence set of the slope, and we establish a prediction model specifically designed for linear models with exchangeable errors. Additionally, we illustrate the finite sample performance of the proposed method through various numerical simulations and a real data application. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an approximative maximum likelihood estimator (MLE) for the drift coefficient of a stochastic partial differential equation in the case where the corresponding Fourier coefficients u k (t) , k = 1 , ... , N over a finite interval of time [ 0 , T ] are observed on a uniform time grid: 0 = t 0 < t 1 < ⋯ < t M = T , with Δ : = t i − t i − 1 = T / M , i ˙ = 1 , ... , M . We provide an explicit Berry–Esseen bound in Kolmogorov distance for this approximative MLE when N , M , T → ∞ , assuming that T 3 N 7 / M 2 → 0 and N 2 / T → 0 . [ABSTRACT FROM AUTHOR]

In this article, we utilize the contrast function of the integrated diffusion process to derive parameter contrast estimators for the Cox-Ingersoll-Ross integrated diffusion process and discuss the asymptotic properties of the estimators using the mixing properties of the process. We first give the tail probability exponential inequality of the mixing long-span high-frequency data. Then, applying this inequality, we prove the strong consistency of the contrast estimators for the drift parameters and diffusion parameter of the CIR integrated diffusion process. In order to obtain these asymptotic properties, we require relatively low order r of the negative power moment E | X t − 1 | r of the diffusion process. In the existing literature, r > 9 is required in order to obtain weak consistency of the estimators. However, in our theorems, only r > 2 is required for the strong consistency of the diffusion parameter estimator, while r > 4 for the strong consistency of the drift parameter estimators. In the simulation analysis, we use the Euler discretization algorithm and the Milstein discretization algorithm, and the results show that the optimal sampling interval is around n − 1 / 3 . We use the daily log price data of the CSI 300 Index to estimate the CIR integrated diffusion model and construct a Bootstrap prediction model for the log price. The results show that the model has a good predictive performance. [ABSTRACT FROM AUTHOR]

AbstractWe propose an extension of the double autoregressive (DAR) model: the sign-double autoregressive (SDAR) model, in the spirit of the GJR-GARCH model (also named the sign-ARCH model). Our model shares the important property of DAR models where a unit root does not imply non stationarity and it allows for asymmetry, as other alternatives in the literature such as the GJR-GARCH or asymmetric linear DAR and dual-asymmetry linear DAR models. We establish consistency and asymptotic normality of the quasi-maximum likelihood estimator in the context of the SDAR model. Furthermore, it is shown by simulations that the asymptotic properties also apply in finite samples. Finally, an empirical application shows the usefulness of our model specially in periods of supply/demand crises of oil disruptions, where spikes of volatility are very likely to be predominant. [ABSTRACT FROM AUTHOR]

This paper provides a least squares regression estimation of the tail index for long memory processes where the innovations are α -stable random sequences. The estimate is based on the property of the characteristic function of the process near the origin. The asymptotics of the estimator are obtained by choosing suitable regression samples with the help of the properties of the α -stable distribution. The numerical simulation and an empirical analysis of financial market data are conducted to assess the finite sample performance of the proposed estimator. [ABSTRACT FROM AUTHOR]

In this article, estimation for a new capability index S pmk ′ which is based on asymmetric loss function (linear-exponential) is discussed when the underlying process follows generalized exponential distribution. Various estimates of the model parameters are proposed including maximum likelihood method, bias-corrected maximum likelihood method and bootstrap bias-corrected maximum likelihood method, and subsequently the process capability index S pmk ′ are obtained. Through extensive simulation studies, we compare the performance of the aforementioned methods of estimation for the PCI S pmk ′ in terms of their absolute bias (AB) and mean squared errors (MSEs). Besides, four bootstrap methods are employed for constructing the confidence intervals for the index S pmk ′ by using the considered methods of estimation. Monte Carlo simulations are performed to compare the performances of the bootstrap confidence intervals (BCIs) with respect to average widths and coverage probabilities. Finally, to show the effectiveness of the proposed methods of estimation and BCIs, two published data sets related to electronic and food industries are analyzed. Simulation results showed that the bootstrap bias corrected maximum likelihood method of estimation gives the best results among other estimation methods in terms of AB and MSEs, while the two real data sets show that width of bias-corrected accelerated bootstrap interval is minimum among all other considered BCIs. [ABSTRACT FROM AUTHOR]

In practice, the failure type for some subjects may be missing or uncertain in competing risks data. Analysis of such uncertain failure type in current status data with two competing risks suffers from issues of model identifiability and requires some model assumptions to deal with that (Koley and Dewanji. Journal of Applied Statistics 49(7), (2022)). In this work, we attempt to alleviate this identifiability problem using some additional information and without any such model assumption. In particular, we consider additional information in the form of some prior knowledge on the missing probabilities. Next, we briefly discuss another type of additional information from a validation sample which ascertains failure type. We consider parametric estimation of the model parameters and non-parametric estimation of the sub-distribution functions. We investigate the associated large sample properties theoretically and the finite sample properties through simulation. We also consider analysis of a real data set for the purpose of illustration. [ABSTRACT FROM AUTHOR]

This paper deals with some probabilistic and statistical properties of periodic generalized Poisson integer-valued autoregressive processes of order p, PG P INAR (p). Necessary and sufficient conditions for the periodic stationarity, both in mean and second order, are established. The closed-forms of the mean and the second moment are, under these conditions, obtained. Moreover, the Wold–Cramér expression of the underlying second-order periodically stationary process is then established. The autocovariance structure is studied, while providing the closed-form of the periodic autocorrelation function. The Yule–Walker (YW), the two-stage conditional least squares (CLS) and the conditional maximum likelihood (CML) estimation methods of the underlying parameters are obtained. An intensive simulation study and an application on a real count data consisting of the daily number of daytime road accidents in Schiphol area, in Netherlands are provided. [ABSTRACT FROM AUTHOR]

AbstractThis paper aims to study the non parametric Negative Binomial Quasi-Maximum Likelihood Estimation (NBQMLE) for locally stationary integer valued processes. So, we have considered two locally stationary integer-valued models of negative binomial type, namely: INARCH(p) and INGARCH(p,q) models. Imposing some contraction arguments, we have extended the stationary negative-binomial QMLE to a localized one in our non-stationary environment. This estimation method is based on a kernel function that achieves the convergence rates of nhn order. Under some regularity assumptions, the consistency, as well as the asymptotic normality of the obtained estimator, are established. The performances of the established estimators are evaluated via a simulation study and an application to real data set. [ABSTRACT FROM AUTHOR]

In the past two decades, model averaging has attracted more and more attention and is regarded as a much better tool to solve model uncertainty than model selection. Compared with the conditional mean regression, the quantile regression serves as a robust alternative and shows a lot more information about the conditional distribution of a response variable. In this paper, we propose a jackknife model averaging procedure that chooses the weights by minimizing a leave-one-out cross-validation criterion function for mixed-data kernel-weighted spline quantile regressions that contain both continuous and categorical regressors when all candidate models are potentially misspecified. We demonstrate the JMA estimator is asymptotically optimal in terms of minimizing the out-of-sample final prediction error. Simulation experiments are conducted to assess the relative finite-sample performance of the proposed JMA method with respect to other model selection and averaging methods. Our JMA method is applied to the wage and house datasets. [ABSTRACT FROM AUTHOR]

We consider a linear model with a change point according to the unknown random threshold of a covariate. We give the expectation-maximization (EM) estimation of the regression and change point parameters. The existence of the random change point is detected by the supremum (SUP) test of score statistics. Theoretically, we establish the convergence and asymptotic distribution of the estimation and show that the EM estimates converge in distribution to a normal distribution. In addition, the numerical performance of the proposed approach is demonstrated through simulation studies. Finally, applying our methodology to household financial decisions, we see that the average debt tolerance of Chinese households is estimated to be 1.1364 times the sum of total household income and financial assets. The effect of assets and income on consumption shows a rapid decline if the household exceeds the average debt tolerance. [ABSTRACT FROM AUTHOR]

We analyse a second-order SPDE model in multiple space dimensions and develop estimators for the parameters of this model based on discrete observations of a solution in time and space on a bounded domain. While parameter estimation for one and two spatial dimensions was established in recent literature, this is the first work which generalizes the theory to a general, multi-dimensional framework. Our approach builds upon realized volatilities, enabling the construction of an oracle estimator for volatility within the underlying model. Furthermore, we show that the realized volatilities have an asymptotic illustration as response of a log-linear model with spatial explanatory variable. This yields novel and efficient estimators based on realized volatilities with optimal rates of convergence and minimal variances. For proving central limit theorems, we use a high-frequency observation scheme. To showcase our results, we conduct a Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

We investigate the problem of joint statistical estimation of several parameters for a stochastic differential equation driven by an additive fractional Brownian motion. Based on discrete-time observations of the model, we construct an estimator of the Hurst parameter, the diffusion parameter and the drift, which lies in a parametrised family of coercive drift coefficients. Our procedure is based on the assumption that the stationary distribution of the SDE and of its increments permits to identify the parameters of the model. Under this assumption, we prove consistency results and derive a rate of convergence for the estimator. Finally, we show that the identifiability assumption is satisfied in the case of a family of fractional Ornstein–Uhlenbeck processes and illustrate our results with some numerical experiments. [ABSTRACT FROM AUTHOR]

The compound Poisson process (CPP) is a common mathematical model for describing many phenomena in medicine, reliability theory and risk theory. However, in the case of low-frequency phenomena, we are often unable to collect a sufficiently large database to conduct analysis. In this article, we focused on methods for determining confidence intervals for the rate of the CPP when the sample size is small. Based on the properties of process parameter estimators, we proposed a new method for constructing such intervals and compared it with other known approaches. In numerical simulations, we used synthetic data from several continuous and discrete distributions. The case of CPP, in which rewards came from exponential distribution, was discussed separately. The recommendation of how to use each method to have a more precise confidence interval is given. All simulations were performed in R version 4.2.1. [ABSTRACT FROM AUTHOR]

Analysis of binary matched pairs data is problematic due to infinite maximum likelihood estimates of the log odds ratio and potentially biased estimates, especially for small samples. We propose a penalised version of the log-likelihood function based on adjusted responses which always results in a finite estimator of the log odds ratio. The probability limit of the adjusted log-likelihood estimator is derived and it is shown that in certain settings the maximum likelihood, conditional and modified profile log-likelihood estimators drop out as special cases of the former estimator. We implement indirect inference to the adjusted log-likelihood estimator. It is shown, through a complete enumeration study, that the indirect inference estimator is competitive in terms of bias and variance in comparison to the maximum likelihood, conditional, modified profile log-likelihood and Firth's penalised log-likelihood estimators. [ABSTRACT FROM AUTHOR]

A useful strategy to examine serial correlation in panel regression models is to adopt serially correlated error terms. The random effect is also present to address the individual-specific heterogeneity. Familiar transformations can restructure the AR(1) process into a serially uncorrelated dynamic model. It may result in unfitting estimation results once the statistical inference is carried on the conditional likelihood. We discuss in the article the consequence of this action. In particular, in the case of large cross-section units and short time-sequences, we provide analytical expressions for the asymptotic bias of maximum likelihood estimates when the analyst naïvely neglects the initial conditions problem. Under several conditions, we compute the size of biases and measure the mean squared errors to illustrate this violation. We also construct a joint regression model of the initial and subsequent responses to handle the initial conditions. Then, we conduct simulation studies to determine the asymptotic behavior. [ABSTRACT FROM AUTHOR]

We consider model averaging estimation problem in the linear regression model with missing response data, that allows for model misspecification. Based on the 'complete' data set for the response variable after inverse propensity score weighted imputation, we construct a leave-one-out cross-validation criterion for allocating model weights, where the propensity score model is estimated by the covariate balancing propensity score method. We derive some theoretical results to justify the proposed strategy. Firstly, when all candidate outcome regression models are misspecified, our procedures are proved to achieve optimality in terms of asymptotically minimizing the squared loss. Secondly, when the true outcome regression model is among the set of candidate models, the resulting model averaging estimators of the regression parameters are shown to be root-n consistent. Simulation studies provide evidence of the superiority of our methods over other existing model averaging methods, even when the propensity score model is misspecified. As an illustration, the approach is further applied to study the CD4 data. [ABSTRACT FROM AUTHOR]

For parameter estimation of continuous and discrete distributions, we propose a generalization of the method of moments (MM), where Stein identities are utilized for improved estimation performance. The construction of these Stein-type MM-estimators makes use of a weight function as implied by an appropriate form of the Stein identity. Our general approach as well as potential benefits thereof are first illustrated by the simple example of the exponential distribution. Afterward, we investigate the more sophisticated two-parameter inverse Gaussian distribution and the two-parameter negative-binomial distribution in great detail, together with illustrative real-world data examples. Given an appropriate choice of the respective weight functions, their Stein-MM estimators, which are defined by simple closed-form formulas and allow for closed-form asymptotic computations, exhibit a better performance regarding bias and mean squared error than competing estimators. [ABSTRACT FROM AUTHOR]

This paper proposes a novel estimator for support vector machines with matrix-valued covariates in a high-dimensional setting. We assume that the underlying parameter matrix lies in a low-dimensional subspace that is simultaneously low-rank and row (column) sparse. We formulate the problem as a regularized hinge loss minimization problem using the nuclear and group lasso norms as penalties to exploit the low-dimensional structure. Our primary focus is deriving the statistical convergence rate of the regularized estimator for the unknown parameter matrix. To validate our theoretical findings, we conducted numerical experiments on both simulated and real-world datasets, demonstrating the efficacy of the regularized support matrix machines framework. [ABSTRACT FROM AUTHOR]

We analyze issues related to estimation and inference for the constrained sum of squares estimator (CSS) of the k-factor Gegenbauer autoregressive moving average (GARMA) model. We present theoretical results for the estimator and show that the parameters that determine the cycle lengths are asymptotically independent, converging at rate T, the sample size, for finite cycles. The remaining parameters lack independence and converge at the standard rate. Analogous with existing literature, some challenges exist for testing the hypothesis of non-cyclical long memory, since the associated parameter lies on the boundary of the parameter space. We present simulation results to explore small sample properties of the estimator, which support most distributional results, while also highlighting areas that merit additional exploration. We demonstrate the applicability of the theory and estimator with an application to IBM trading volume. [ABSTRACT FROM AUTHOR]

We prove mixing convergence of the least squares estimator of autoregressive parameters for supercritical autoregressive processes of order 2 with Gaussian innovations having real characteristic roots with different absolute values. We use an appropriate random scaling such that the limit distribution is a two-dimensional normal distribution concentrated on a one-dimensional ray determined by the characteristic root having the larger absolute value. [ABSTRACT FROM AUTHOR]

In this paper, we propose the application of shrinkage strategies to estimate coefficients in the Bell regression models when prior information about the coefficients is available. The Bell regression models are well-suited for modelling count data with multiple covariates. Furthermore, we provide a detailed explanation of the asymptotic properties of the proposed estimators, including asymptotic biases and mean squared errors. To assess the performance of the estimators, we conduct numerical studies using Monte Carlo simulations and evaluate their simulated relative efficiency. The results demonstrate that the suggested estimators outperform the unrestricted estimator when prior information is taken into account. Additionally, we present an empirical application to demonstrate the practical utility of the suggested estimators. [ABSTRACT FROM AUTHOR]

In modeling count data with overdispersion and extra zeros, zero-inflated negative binomial (ZINB) regression model is useful. In a regression model, the multicollinearity problem arises when there are some high correlations between predictor variables. This problem leads to the maximum likelihood method will not be an efficient estimator. The ridge and Liu-type estimators have been proposed to combat the multicollinearity problem so that the Liu-type estimator is better. In this paper, we proposed the Liu-type shrinkage estimators, namely linear shrinkage, preliminary test, shrinkage preliminary test, Stein-type, and positive Stein-type Liu estimators to estimate the count parameters in the ZINB model, when some of the predictor variables have not a significant effect to predict the response variable so that a sub-model may be sufficient. The asymptotic distributional biases and variances of the proposed estimators are nicely demonstrated. We also compared the performance of the Liu-type shrinkage estimators along with the Liu-type unrestricted estimator by using an extensive Monte Carlo simulation study. The results show that the performances of the proposed estimators are superior to those based on Liu-type unrestricted estimators. We also applied the proposed estimation methods to Expenditure and Default Data. [ABSTRACT FROM AUTHOR]

Statistics on manifolds attracts more and more attentions because a variety of observations or responses have manifold structures. In this paper, we address the regression task on Grassmannian manifolds, which is arisen from the statistical shape analysis. Particularly, we develop an intrinsically semi-parametric model instead of the geodesic regression for better approximating shapes. Concretely, first we introduce a nonparametric term on Grassmannian manifolds to further depict the relationships that cannot be well described by the geodesic model on Grassmannian manifolds. Second, we utilize an alternatively iterative strategy to update parametric and nonparametric parts to form a solution. Finally, we testify the effectiveness of our regression model on synthetic data on Gr (n , m) with multiple combinations of n and m, as well as a real data of corpus callosum shapes. The experimental results validate that our proposed model outperforms the conventional geodesic regression model and shows the advantage of approaches driven doubly by model and data. [ABSTRACT FROM AUTHOR]

The problems of point estimation and classification under the assumption that the training data follow a Lindley distribution are considered. Bayes estimators are derived for the parameter of the Lindley distribution applying the Markov chain Monte Carlo (MCMC), and Tierney and Kadane's [Tierney and Kadane, Accurate approximations for posterior moments and marginal densities, J. Amer. Statist. Assoc. 81 (1986), pp. 82–86] methods. In the sequel, we prove that the Bayes estimators using Tierney and Kadane's approximation and Lindley's approximation both converge to the maximum likelihood estimator (MLE), as $ n \rightarrow \infty $ n → ∞ , where n is the sample size. The performances of all the proposed estimators are compared with some of the existing ones using bias and mean squared error (MSE), numerically. It has been noticed from our simulation study that the proposed estimators perform better than some of the existing ones. Applying these estimators, we construct several plug-in type classification rules and a rule that uses the likelihood accordance function. The performances of each of the rules are numerically evaluated using the expected probability of misclassification (EPM). Two real-life examples related to COVID-19 disease are considered for illustrative purposes. [ABSTRACT FROM AUTHOR]

In this paper, we establish normalized and self-normalized Cramér-type moderate deviations for the Euler-Maruyama scheme for SDE. Due to our results, Berry-Esseen's bounds and moderate deviation principles are also obtained. Our normalized Cramér-type moderate deviations refine the recent work of Lu et al. (2022). [ABSTRACT FROM AUTHOR]

The growing popularity of data heterogeneity motivates people to identify homogeneous subgroups with identical parameters. Meanwhile, in many fields of recent data science for some applications, such as personalized education and personalized marketing, the massive data are usually recorded as categorical or ordinal variables, which highlights the importance of performing subgroup analysis on those ordinal outcomes. In this paper, we propose a cumulative link model with subject-specific intercepts to detect and identify homogeneous subgroups through concave pairwise fusion penalty for ordinal response, where heterogeneity arises from some unknown or unobserved latent factors. The concave fusion method can simultaneously determine the number of subgroups, identify the group membership, and estimate the regression coefficients. An alternating direction method of multipliers algorithm with concave penalties for the generalized linear regression model with logit link is developed and its convergence property is studied. We also establish the oracle property of the proposed penalized estimator under some mild conditions. Our simulation studies show that the proposed method could recover the heterogeneous subgroup structure effectively when the response of interest is ordinal. Further, the advantages of our method are illustrated by the analysis on a Mathematics Student Performance Data Set of two public schools from the Alentejo region of Portugal. [ABSTRACT FROM AUTHOR]

AbstractWe consider a linear model which can have a large number of explanatory variables, the errors with an asymmetric distribution or the values of the explained variable are missing at random. In order to take in account these several situations, we consider the non parametric empirical likelihood (EL) estimation method. Because a constraint in EL contains an indicator function then a smoothed function instead of the indicator will be considered. Two smoothed expectile maximum EL methods are proposed, one of which will automatically select the explanatory variables. For each of the methods we obtain the convergence rate of the estimators and their asymptotic normality. The smoothed expectile empirical log-likelihood ratio process follow asymptotically a chi-square distribution and moreover the adaptive LASSO smoothed expectile maximum EL estimator satisfies the sparsity property which guarantees the automatic selection of zero model coefficients. In order to implement these methods, we propose four algorithms. [ABSTRACT FROM AUTHOR]

In this work, we introduce the random weighting method to the nonlinear regression model and study the asymptotic properties for the randomly weighted least squares estimator with dependent errors. The results reveal that this new estimator is consistent. Moreover, some simulations are also carried out to show the performance of the proposed estimator. [ABSTRACT FROM AUTHOR]

In this article, we improve parameter estimation in the zero-inflated Poisson regression model using shrinkage strategies when it is suspected that the regression parameter vector may be restricted to a linear subspace. We consider a situation where the response variable is subject to right-censoring. We develop the asymptotic distributional biases and risks of the shrinkage estimators. We conduct an extensive Monte Carlo simulation for various combinations of the inactive predictors and censoring constants to compare the performance of the proposed estimators in terms of their simulated relative efficiencies. The results demonstrate that the shrinkage estimators outperform the classical estimator in certain parts of the parameter space. When there are many inactive predictors in the model, as well as when the censoring percentage is low, the proposed estimators perform better. The performance of the positive Stein-type estimator is superior to the Stein-type estimator in certain parts of the parameter space. We evaluated the estimators' performance using wildlife fish data. [ABSTRACT FROM AUTHOR]

A new goodness-of-fit (GoF) test is proposed and investigated for the Gaussianity of the observed functional data. The test statistic is the Cramér-von Mises distance between the observed empirical characteristic functional (CF) and the theoretical CF corresponding to the null hypothesis stating that the functional observations (process paths) were generated from a specific parametric family of Gaussian processes, possibly with unknown parameters. The asymptotic null distribution of the proposed test statistic is derived also in the presence of these nuisance parameters, the consistency of the classical parametric bootstrap is established, and some particular choices of the necessary tuning parameters are discussed. The empirical level and power are investigated in a simulation study involving GoF tests of an Ornstein–Uhlenbeck process, Vašíček model, or a (fractional) Brownian motion, both with and without nuisance parameters, with suitable Gaussian and non-Gaussian alternatives. [ABSTRACT FROM AUTHOR]

Under a nonlinear regression model with univariate response an algorithm for the generation of sequential adaptive designs is studied. At each stage, the current design is augmented by adding p design points where p is the dimension of the parameter of the model. The augmenting p points are such that, at the current parameter estimate, they constitute the locally D-optimal design within the set of all saturated designs. Two relevant subclasses of nonlinear regression models are focused on, which were considered in previous work of the authors on the adaptive Wynn algorithm: firstly, regression models satisfying the 'saturated identifiability condition' and, secondly, generalized linear models. Adaptive least squares estimators and adaptive maximum likelihood estimators in the algorithm are shown to be strongly consistent and asymptotically normal, under appropriate assumptions. For both model classes, if a condition of 'saturated D-optimality' is satisfied, the almost sure asymptotic D-optimality of the generated design sequence is implied by the strong consistency of the adaptive estimators employed by the algorithm. The condition states that there is a saturated design which is locally D-optimal at the true parameter point (in the class of all designs). [ABSTRACT FROM AUTHOR]

The penalized variable selection methods are often used to select the relevant covariates and estimate the unknown regression coefficients simultaneously, but these existing methods may fail to be consistent for the setting with highly correlated covariates. In this paper, the semi-standard partial covariance (SPAC) method with Lasso penalty is proposed to study the generalized linear model with highly correlated covariates, and the consistencies of the estimation and variable selection are shown in high-dimensional settings under some regularity conditions. Some simulation studies and an analysis of colon tumor dataset are carried out to show that the proposed method performs better in addressing highly correlated problem than the traditional penalized variable selection methods. [ABSTRACT FROM AUTHOR]

This research focuses on estimating the stress-strength reliability in a system characterized by the influence of two random stresses on its strength, employing both frequentist and Bayesian approaches. The reliability of such systems is represented by the function δ = P (U < V < W) , where V denotes the system's strength, and U and W represent the stresses. The analysis is performed under a progressive Type-II censoring scheme, considering the random variables U, V, and W as independent and following the Burr-XII distribution. In a frequentist setup, both the maximum likelihood estimator and the maximum product spacings estimator of δ have been obtained. In the Bayesian paradigm, the Bayes estimator of δ under the squared error loss function is derived utilizing the Markov chain Monte Carlo method, considering independent gamma priors for the unknown parameters. In addition, asymptotic confidence intervals and highest probability density credible intervals for δ are also formulated. An extensive simulation experiment is carried out to compare the performances of the different developed estimators. Finally, a real-life application is presented to demonstrate the practical applicability of the proposed theory. [ABSTRACT FROM AUTHOR]

This paper studies the quasi-maximum likelihood estimator (quasi-MLE) of a change point in variance for the fractionally integrated noise with memory parameter d ∈ (- 0.5 , 0.5) , and the asymptotic behaviors of the estimator are examined. We show that: (1) For the case of fixed break magnitude, the quasi-MLE of the change point is inconsistent, while that of the change-point fraction is T-consistent. (2) For the case of shrinking break magnitude, the intermediate memory dependency has no impact on the convergence rate of the quasi-MLE of the change point. However, it is not the case when the process is long-range dependent. Specifically, the asymptotic behaviors of the quasi-MLE of the change point vary from the case where 0 < d < 0.25 to the cases where d = 0.25 and 0.25 < d < 0.5 , and the phase transition occurs when d = 0.25 . In addition, the quasi-MLEs of the pre-variance and post-variance are both examined, and we show that they are consistent for both fixed and shrinking break magnitude. Monte Carlo simulations and an empirical study are conducted to illustrate the theoretical results on finite-sample performance. [ABSTRACT FROM AUTHOR]

We consider the problem of tracking an unknown time varying parameter that characterizes the probabilistic evolution of a sequence of independent observations. To this aim, we propose a stochastic gradient descent-based recursive scheme in which the log-likelihood of the observations acts as time varying gain function. We prove convergence in mean-square error in a suitable neighbourhood of the unknown time varying parameter and illustrate the details of our findings in the case where data are generated from distributions belonging to the exponential family. [ABSTRACT FROM AUTHOR]

This paper addresses statistical inference in uncertain differential equations, focusing on parameter estimation for a class of uncertain Vasicek model with a small dispersion coefficient from discrete observations. Least squares estimators are obtained using a defined contrast function. The consistency and asymptotic distribution of these estimators are established. Numerical simulations and empirical analysis on real interest rate data highlight the efficacy of the proposed estimators and the methodology's practicality in capturing interest rate dynamics. [ABSTRACT FROM AUTHOR]

This paper considers the problem of inferring the causal effect of a variable Z on a dependently censored survival time T. We allow for unobserved confounding variables, such that the error term of the regression model for T is dependent on the confounded variable Z. Moreover, T is subject to dependent censoring. This means that T is right censored by a censoring time C, which is dependent on T (even after conditioning out the effects of the measured covariates). A control function approach, relying on an instrumental variable, is leveraged to tackle the confounding issue. Further, it is assumed that T and C follow a joint regression model with bivariate Gaussian error terms and an unspecified covariance matrix, such that the dependent censoring can be handled in a flexible manner. Conditions under which the model is identifiable are given, a two-step estimation procedure is proposed, and it is shown that the resulting estimator is consistent and asymptotically normal. Simulations are used to confirm the validity and finite-sample performance of the estimation procedure. Finally, the proposed method is used to estimate the causal effect of job training programs on unemployment duration. [ABSTRACT FROM AUTHOR]

Time series of proportions are often encountered in applications such as ecology, environmental science and public health. Strategies for such data include linear regression after logistic transformation. Though easy to fit, the transformation approach renders covariate effects uninterpretable on the scale on which they were observed owing to Jensen's inequality. An alternative to the transformation approach has been to directly model the response via the beta distribution. In this paper, we extend zero-inflated beta regression models for independent proportions to time series data that is bounded over the unit interval and that may take on zero values. Estimation is within the partial-likelihood framework and is computationally feasible to implement. We outline the asymptotic theory of our maximum partial likelihood estimators under mild regularity conditions and investigate their bias and variability using simulation studies. The utility of our method is illustrated using two real data examples. [ABSTRACT FROM AUTHOR]

In this article, the M-estimator of the moderate deviations from a unit root model with possibly infinite variance errors is proposed. When the autoregressive coefficients are located on both sides of stationary and explosion, the asymptotic normal properties of the corresponding estimators for unknown parameters are proved, respectively. The asymptotic properties of the M-estimators are obtained in both the mildly integrated case and the mildly explosive case. [ABSTRACT FROM AUTHOR]

In this paper, we consider the structural change in a class of discrete valued time series, where the true conditional distribution of the observations is assumed to be unknown. The conditional mean of the process depends on a parameter $ \theta ^* $ θ ∗ which may change over time. We provide sufficient conditions for the consistency and the asymptotic normality of the Poisson quasi-maximum likelihood estimator (QMLE) of the model. We consider an epidemic change-point detection and propose a test statistic based on the QMLE of the parameter. Under the null hypothesis of a constant parameter (no change), the test statistic converges to a distribution obtained from increments of a Browninan bridge. The test statistic diverges to infinity under the epidemic alternative, which establishes that the proposed procedure is consistent in power. The effectiveness of the proposed procedure is illustrated by simulated and real data examples. [ABSTRACT FROM AUTHOR]