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A variant of the bivariate Poisson common shock model is proposed which, contrary to the original, spans all possible degrees of dependence. Its basic distributional properties are described, moment-based estimation is studied, and its use is illustrated on real data. [ABSTRACT FROM AUTHOR]

This paper finds that heteroskedasticity in nonclassical error-in-variable models leads to biased and inconsistent estimates when higher-order moments of data are used. A closed-form estimator is provided to correct this bias based on information from the first three moments. [ABSTRACT FROM AUTHOR]

We prove two consistency theorems for the lasso estimators of sparse linear regression models with exponentially β - mixing errors, in which the number of regressors p is large, even much larger than the sample size n . [ABSTRACT FROM AUTHOR]

We discuss an approach to construct explicitly calculable consistent estimators for parameters of some nonlinear regression models. The estimators of such a kind can be used as initial estimators in one-step estimation procedures for unknown parameters of these models. [ABSTRACT FROM AUTHOR]

In this paper, we consider the testing problem whether burn-in is required based on the long-run average cost for a population with bathtub-shaped failure rate function. We propose a test based on kernel density estimation. We then apply our proposed test to two real data sets in the context of reliability. [ABSTRACT FROM AUTHOR]

Autoregressive (AR) models are an important tool in the study of time series data. However, the standard AR model only allows for unimodal marginal and conditional densities, and cannot capture conditional heteroscedasticity. Previously, the Gaussian mixture AR (GMAR) model was considered to remedy these shortcomings by using a Gaussian mixture conditional model. We introduce the Laplace mixture (LMAR) model that utilizes a Laplace mixture conditional model, as an alternative to the GMAR model. We characterize the LMAR model and provide conditions for stationarity. An MM (minorization–maximization) algorithm is then proposed for maximum pseudolikelihood (MPL) estimation of an LMAR model. Conditions for asymptotic inference and a rule for model selection for the MPL estimator are considered. An example analysis of data arising from the calcium imaging of a zebrafish brain is performed. [ABSTRACT FROM AUTHOR]

We consider bias reduced estimators for the tail index and tail probabilities under random right censoring in case of Pareto-type distributions. The solution is based on second-order refined peaks-over-threshold modelling as developed in Beirlant et al. (2009). [ABSTRACT FROM AUTHOR]

In this work we focus on functional coefficient regression (FCR) models. Here we study the estimation of FCR models by splines, with autoregressive errors and show the rates of convergence of the proposed estimator. The importance of taking into account the correlation is assessed via simulation studies and multi-step ahead forecasts for a real data set. [ABSTRACT FROM AUTHOR]

The purpose of this paper is, in the first step, to consider a class of GMM estimators with interesting asymptotic properties and a reasonable number of computations for two dimensionally indexed Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. In the second step, we use the central limit theorem of Huang (1992) for spatial martingale differences to establish the LAN property for general two-dimensional discrete models on a regular grid with Gaussian errors. We then apply this result to the spatial GARCH model and derive the limit distribution of the maximum likelihood estimators of the parameters. Results of numerical simulations are presented. [ABSTRACT FROM AUTHOR]

Abstract: Necessary and sufficient conditions for the existence of order statistics moments of -stable random variables are introduced. Using the obtained results, all parameters of -stable distribution are estimated. [Copyright &y& Elsevier]

Abstract: Necessary and sufficient conditions are derived for the equalities of the best linear unbiased estimators (BLUEs) of parametric functions under a general linear model and its restricted and stochastically restricted models to hold. [Copyright &y& Elsevier]

Abstract: We propose a new efficient root- consistent estimate for the direction of the index parameter vector in a class of models with single-index structure. To select the important predictors, we suggest using the adaptive LASSO and establish the oracle property. Simulation results also confirm the theoretical findings. [Copyright &y& Elsevier]

Abstract: A class of absolutely continuous bivariate exponential distributions is constructed using the product form of a first order autoregressive model. Inference methods are proposed for parameter estimation and diagnosis. Data analysis is carried out to illustrate the applications. [Copyright &y& Elsevier]

Abstract: We studied an unconditional approach based on partial maximization using different penalty values for comparing two incidence rates from Poisson distributions. We also consider a full maximization approach and an approach based on estimation and full maximization. Generally, the third approach has good performance. [Copyright &y& Elsevier]

Abstract: Two-step logit models are extensions of the ordinary logistic regression model, which are designed for complex ordinal outcomes commonly seen in practice. In this paper, we establish some asymptotic properties of the maximum likelihood estimator (MLE) of the regression parameter vector under some mild conditions, which include existence of the MLE, convergence rate and asymptotic normality of the MLE. We relax the boundedness condition of the regressors required in most existing theoretical results, and all conditions are easy to verify. [Copyright &y& Elsevier]

Abstract: Empirical likelihood methods based on some weighted score equations are proposed for constructing confidence intervals for the coefficient in the simple bilinear model without assuming normality for the errors and without estimating the asymptotic variance explicitly. A simulation study confirms the good finite sample behavior of the proposed methods. [Copyright &y& Elsevier]

Abstract: In this paper, we study -estimation for the partially linear model under monotonic constraints. We use monotone -splines to approximate the monotone nonparametric function. We show the large sample properties of the resulting estimators. The proposed estimator of parameter part is root- consistent, and asymptotically normal and the estimator for the nonparametric component achieves the optimal convergence rate. A simulation study is conducted to evaluate the finite sample performance of the method. The proposed procedure is illustrated by an air pollution study. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to study the consistency of the kernel density estimator pertaining to a continuous time stationary process , with an underlying density . More precisely, in a rather general dependency setting, where we use a martingale difference device and a technique based on a sequence of projections on -fields, we establish the almost sure pointwise and uniform consistencies with rates of the estimate of built upon the part of the process . [Copyright &y& Elsevier]

Abstract: In this paper, we consider the independent component (IC) model, and the asymptotic properties of the complex valued unmixing matrix estimates that are based on simultaneous use of two scatter matrix functionals. [Copyright &y& Elsevier]

Abstract: We propose Bayesian model selection based on composite datasets, which can be constructed from various subsample estimates. The method remains consistent without fully specifying a probability model, and is useful for dependent data, when asymptotic variance of the parameter estimator is difficult to estimate. [Copyright &y& Elsevier]

Abstract: This paper applied the maximization by parts (MBP) and the modified MBP (MMBP) methods to estimate the C-MGARCH model and compare the effectiveness of two methods. Monte Carlo simulation studies show that both MBP and MMBP methods are more efficient than that of the IFM method. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a flexible model for cyclic abundance data, that takes into account predator–prey interactions. The idea is to incorporate a predator–prey dynamical model into a non homogeneous Poisson process of periodic intensity rate. Further, we devise an easy to implement estimation method and derive asymptotic confidence intervals. Finally, a simulation study indicates that the method performs well for small samples which are typical in ecology. [Copyright &y& Elsevier]

Abstract: In this paper, we investigate the estimation of conditional density function based on the single-index model for functional time series data. The asymptotic normality of the conditional density estimator and the conditional mode estimator for the mixing dependence functional time series data are obtained, respectively. Furthermore, as applications, the asymptotic confidence interval of the conditional density function and the conditional mode are also presented for . [Copyright &y& Elsevier]

Abstract: The augmented inverse probability weighted (AIPW) estimator employing the optimal augmentation term is more efficient than the inverse probability weighted (IPW) estimator. However, the AIPW estimator could lose substantial efficiency compared to the IPW estimator when the optimal augmentation term is incorrectly modeled. We propose a modified AIPW (MAIPW) estimator by adapting Tan’s (2010b) “tilde” estimator, which was proposed for structural models, for regression models with missing data. When the missing mechanism is correctly modeled, the proposed MAIPW estimator is more efficient than the IPW estimator, and is more efficient than the AIPW estimator using the same augmentation term, except when the augmentation term is a correct model for the optimal one, in which case both MAIPW and AIPW estimators attain the semiparametric efficiency bound, thus are equally efficient. In addition, like the AIPW estimator, the MAIPW estimator is doubly robust. Through simulation experiments, we compare numerical performances of the MAIPW estimator and some other estimators that attempt to improve efficiency upon the IPW estimator. [Copyright &y& Elsevier]

Abstract: In this paper we consider the estimation problem for the quasi-likelihood model in presence of non-sample information (NSI). More specifically, we introduce a shrinkage estimation strategy for simultaneous model selection and parameter estimation by using the maximum quasi-likelihood estimates as the benchmark estimator, and define the pretest estimator (PTE), shrinkage estimator (SE) and positive-rule shrinkage estimator (PSE). Furthermore, we apply the lasso-type estimation strategy and compare the relative performance of lasso with the suggested estimators. The shrinkage estimators are shown to be efficient estimators compared to others. When the NSI is true the PTE has less risk compared to shrinkage and lasso estimators. [Copyright &y& Elsevier]

Abstract: The em algorithm can be used to compute maximum likelihood estimates of model parameters for skew- mixture models. We show that the intractable expectations needed in the e-step can be written out analytically. These closed form expressions bypass the need for numerical estimation procedures, such as Monte Carlo methods, leading to accurate calculation of maximum likelihood estimates. Our approach is illustrated on two real data sets. [Copyright &y& Elsevier]

Abstract: The estimation of a diffusion tensor imaging (DTI) model proposed by involves two steps. The second step requires solving an ordinary differential equation, which in practice is solved by using a numerical approximation. We investigate how to balance the additional numerical error introduced by this approximation with the statistical estimation error using empirical mean integrated squared error for Euler’s and the second order Runge–Kutta approximations. We give practical guideline on how fast should the numerical approximation step grow with respect to the sample size. However, the typical scales of the neural fibers estimated using DTI in brains are much smaller than the maximal allowed numerical approximation step for approximation of any order. It is further observed that a simple Euler’s approximation gives very similar results as obtained by using a more sophisticated numerical approximation of a higher order. In view of the increasing computational costs for numerical approximations of higher orders, the use of Euler’s approximation is well-justified for the DTI. [Copyright &y& Elsevier]

Abstract: Given measurements of the number of customers in a queueing system over a finite time interval, it is natural to try to fit a stationary birth-and-death process model, because it is remarkably tractable, even when the birth and death rates depend on the state in an arbitrary way. Natural estimators of the birth (death) rate in each state are the observed number of transitions up (down) from that state divided by the total time spent in that state. It is tempting to validate the model by comparing the steady-state distribution of the model based on those estimated rates to the empirical steady-state distribution recording the proportion of time spent in each state. However, it is inappropriate to draw strong conclusions from a close fit to the same data, because these two distributions are necessarily intimately related, even if the model assumptions are not nearly satisfied. We elaborate by (i) establishing stochastic comparisons between these two fitted distributions using likelihood-ratio stochastic ordering and (ii) quantifying their difference. [Copyright &y& Elsevier]

Abstract: With sparse structures and conditional independence, one could estimate the precision matrix of Gaussian graphical models more efficiently. studied objective priors for star-shape graphical models. We consider a generative star-shape model. Objective priors such as invariance priors, the Jeffreys prior and reference priors are derived for the proposed model. Closed form expressions of posterior distributions are derived based on a family of priors including the studied objective priors. [Copyright &y& Elsevier]

Abstract: Predictive recursion (PR) is a fast stochastic algorithm for nonparametric estimation of mixing distributions in mixture models. It is known that the PR estimates of both the mixing and mixture densities are consistent under fairly mild conditions, but currently very little is known about the rate of convergence. Here I first investigate asymptotic convergence properties of the PR estimate under model misspecification in the special case of finite mixtures with known support. Tools from stochastic approximation theory are used to prove that the PR estimates converge, to the best Kullback–Leibler approximation, at a nearly root- rate. When the support is unknown, PR can be used to construct an objective function which, when optimized, yields an estimate of the support. I apply the known-support results to derive a rate of convergence for this modified PR estimate in the unknown support case, which compares favorably to known optimal rates. [Copyright &y& Elsevier]

Abstract: This paper proposes two estimation methods based on a weighted least squares criterion for non-(strictly) stationary power ARCH models. The weights are the squared volatilities evaluated at a known value in the parameter space. The first method is adapted for fixed sample size data while the second one allows for online data available in real time. It will be shown that these methods provide consistent and asymptotically Gaussian estimates having asymptotic variance equal to that of the quasi-maximum likelihood estimate (QMLE) regardless of the value of the weighting parameter. Finite-sample performances of the proposed WLS estimates are shown via a simulation study for various sub-classes of power ARCH models. [Copyright &y& Elsevier]

Abstract: The estimation of the abundance of a species using the presence or absence of the species over a grid of cells simplifies data collection but the resulting statistical analysis is challenging. Several estimators have been proposed but their properties are unknown. Here we consider a generalized gamma-Poisson model which allows dependencies across the grid and develop a new estimator for this model. It is shown that this estimator is consistent, allowing us to conclude that it is indeed possible to estimate abundance from presence–absence maps. [Copyright &y& Elsevier]

Abstract: We prove Freidlin–Wentzell Large Deviation estimates under rather minimal assumptions. This allows one to derive Wentzell–Freidlin Large Deviation estimates for diffusions on the positive half line with coefficients that are neither bounded nor Lipschitz continuous. This applies to models of interest in Finance, i.e. the CIR and the CEV models, which are positive diffusion processes whose diffusion coefficient is only Hölder continuous. [Copyright &y& Elsevier]

Abstract: We establish the law of the iterated logarithm for the product limit estimator, when the data are subject to double censoring. This investigation extends the results available for the model for singly censored data. [Copyright &y& Elsevier]

Abstract: The Laplace distribution is one of the earliest distributions in probability theory. For the first time, based on this distribution, we propose the so-called beta Laplace distribution, which extends the Laplace distribution. Various structural properties of the new distribution are derived, including expansions for its moments, moment generating function, moments of the order statistics, and so forth. We discuss maximum likelihood estimation of the model parameters and derive the observed information matrix. The usefulness of the new model is illustrated by means of a real data set. [Copyright &y& Elsevier]

Abstract: One major goal in clinical applications of multi-state models is the estimation of transition probabilities. In a recent paper, introduce a substitute for the Aalen–Johansen estimator in the case of a non-Markov illness–death model. The idea behind their estimator is to weight the data by the Kaplan–Meier weights pertaining to the distribution of the total survival time of the process. In this paper we propose a modification of estimator based on presmoothing. Consistency is established. We investigate the finite sample performance of the new estimator through simulations. Data from a study on colon cancer are used for illustration purposes. [Copyright &y& Elsevier]

Abstract: Backward recurrence times in stationary renewal processes and current durations in dynamic populations observed at a cross-section may yield estimates of underlying interarrival times or survival distributions under suitable stationarity assumptions. Regression models have been proposed for these situations, but accelerated failure time models have the particularly attractive feature that they are preserved when going from the backward recurrence times to the underlying survival distribution of interest. This simple fact has recently been noticed in a sociological context and is here illustrated by a study of current duration of time to pregnancy. [Copyright &y& Elsevier]

Abstract: The modeling and analysis of lifetime data is an important aspect of statistical work in a wide variety of scientific and technological fields. introduced a probability distribution which is commonly used in the analysis of lifetime data. For the first time, based on this distribution, we propose the so-called exponentiated generalized inverse Gaussian distribution, which extends the exponentiated standard gamma distribution (). Various structural properties of the new distribution are derived, including expansions for its moments, moment generating function, moments of the order statistics, and so forth. We discuss maximum likelihood estimation of the model parameters. The usefulness of the new model is illustrated by means of a real data set. [Copyright &y& Elsevier]

Abstract: Any repairable system improves (deteriorates) with time if the interarrival times of failure tend to get larger (smaller) in some sense. In this paper we consider two such repairable systems, and their performance in terms of several partial orderings of their respective interarrival times of failure are compared. The comparison of two systems’ improvement/deterioration under minimal repair policy has been characterized in terms of - orders and also in terms of their shifted and dispersive versions. These results generalize some of the existing results in the literature and also provide some new results in this direction. [ABSTRACT FROM AUTHOR]

Abstract: In econometric analysis of panel data, one always doesn’t have enough information to assure the existence/absence of time effects, which can lead to wrong conclusions in statistical inference such as moment estimation and hypothesis testing. In this paper, estimation of second and fourth order moments of the individual effects and the errors are studied for linear panel data models without information on the existence/absence of time effects. With differences of the residuals over the individual index, the orthogonality-based moment estimators of the random individual effects and the errors are respectively obtained without affecting each other. These moment estimators are robust on the potential existence of time effects. Their asymptotic normalities are obtained under some moment conditions. Monte Carlo simulations are carried out for illustration. [ABSTRACT FROM AUTHOR]

Abstract: We study the large-sample properties of the penalized maximum likelihood estimator of a multivariate stochastic regression model with contemporaneously correlated data. The penalty is in terms of the square norm of some (vector) linear function of the regression coefficients. The model subsumes the so-called common transfer function model useful for extracting common signals in a panel of short time series. We show that, under mild regularity conditions, the penalized maximum likelihood estimator is consistent and asymptotically normal. The asymptotic bias of the regression coefficient estimator is also derived. [ABSTRACT FROM AUTHOR]

Abstract: An adjusted -squared type measure for exponential dispersion models, based on Kullback–Leibler divergences, is described and it is proved that the corrected measure is an asymptotically unbiased estimator of the population value, under the null hypotheses of no covariates. [Copyright &y& Elsevier]

Abstract: We consider semiparametric models whose infinite-dimensional parameter corresponds to a probability distribution. The NPMLE based on the profile empirical likelihood for this kind of semiparametric model has attracted considerable interest. We propose the use of a modified profile empirical likelihood to improve the accuracy of this estimation. We consider applications to the exponential-tilt model and show that the accuracy of the proposed estimator is better than that of the conventional NPMLE by numerical study. [Copyright &y& Elsevier]

Abstract: In this paper we consider the problem of constructing two-level fractional factorial designs in blocks of size two that allow for the orthogonal estimation of all main effects and two-factor interactions (after adjusting for blocks). This problem has been considered in the literature, e.g., see , and . In this paper we give two systematic methods for the construction of such designs. The first construction method gives in many situations designs requiring fewer runs than those designs previously given whereas the second method gives a systematic method for constructing designs analogous to those illustrated in by example. [Copyright &y& Elsevier]

Abstract: The common principal components model for several groups of multivariate observations assumes equal principal axes among the groups. Robust estimators can be defined replacing the sample variance by a robust dispersion measure. This paper studies the asymptotic distribution of robust projection-pursuit estimators under a common principal components model. [Copyright &y& Elsevier]

Abstract: Suppose we have data from a bivariate model with parametric marginals. Efficient estimators of the parameters in the marginal models are generally not efficient in the bivariate model. In this article, we propose a method of improving these marginal estimators and demonstrate that the magnitude of this improvement can be as large as 100 percent in some cases. [Copyright &y& Elsevier]

Abstract: An alternative to the classical mixed model with normal random effects is to use a Dirichlet process to model the random effects. Such models have proven useful in practice, and we have observed a noticeable variance reduction, in the estimation of the fixed effects, when the Dirichlet process is used instead of the normal. In this paper we formalize this notion, and give a theoretical justification for the expected variance reduction. We show that for almost all data vectors, the posterior variance from the Dirichlet random effects model is smaller than that from the normal random effects model. [Copyright &y& Elsevier]

Abstract: It is well known that the Kaplan–Meier estimate of QAL data leads to a biased estimate due to the induced dependence between the QAL and censoring time in the QAL scale. However, the issue of induced dependent censoring in the QAL scale remains less understood. We present a formal study of this induced dependence in the context of a simple illness–death model. The bias of the Kaplan–Meier estimate of the QAL distribution has been investigated. [Copyright &y& Elsevier]

Abstract: Multi-casting autoregression (MCAR, for short) is suggested as a natural extension of the bifurcating autoregressive (BAR) model (cf. [Cowan, R., Staudte, R.G., 1986. The bifurcating autoregression model in cell lineage studies. Biometrics 42, 769–783]) in order to analyze multi-splitting tree-structured data. Pathwise stationarity of the MCAR model is discussed. Least squares estimation for the autoregressive parameter is considered and relevant limiting distribution is derived, in particular, for the pathwise explosive case. These results can be regarded as generalizations of those for standard stationary and explosive AR(1) time series. A simulation study is conducted to illustrate our results. [Copyright &y& Elsevier]

Abstract: Weibull mixtures have been widely used in many applications, and they can be generalized by allowing negative mixing weights. In this note, we investigate constraints on the mixing weights and parameters of components under which the generalized mixture of Weibull distributions is a valid probability model, including the cases of exponential or Rayleigh components. In addition, characterizations are shown for generalized mixtures of three or fewer Weibull components. We use an example of a two-fold Weibull mixture model where negative weights are estimated, and the constraints are illustrated with a graphical example of three components. [Copyright &y& Elsevier]