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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]