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Quantifying risks is of importance in insurance. In this paper, we employ the jackknife empirical likelihood method to construct confidence intervals for some risk measures and related quantities stud...
For the regression parameter β0 in the Cox model, there have been several estimators constructed based on various types of approximated likelihood, but none of them has demonstrated small-sample advan...
Thus far, likelihood-based interval estimates for quantiles have not been studied in the literature on interval censored case 2 data and partly interval censored data, and, in this context, the use of...
We study extremal problems related to nonparametric maximum likelihood estimation (MLE) of a signal in white noise.The aim is to reduce these to standard problems of optimal control which can be solve...
The usual approach to computing empirical likelihood for the mean uses Newton's method after eliminating a Lagrange multiplier and replacing the function- log(x) by a quadratic Taylor approximation t...
A test of homogeneity tries to decide whether observations come from a single distribution or from a mixture of several distributions. A powerful theory has been developed for the case where the comp...
A special semiparametric model for a univariate density is introduced that allows analyzing a number of problems via appropriate transformations. Two problems treated in some detail are testing for t...
I started looking at log-concave distributions when I was searching for an appropriate model for subpopulations of multivariate flow cytometry data about ten years ago. The use of log-concave ...
In this paper we consider a variety of procedures for numerical statistical inference in the family of univariate and multivariate stable distributions. In connection with univariate distributions (i)...
In this note we introduce an estimate for the marginal likelihood associated to hidden Markov models (HMMs) using sequential Monte Carlo (SMC) approximations of the generalized two-filter smoothing de...
Independent Component Analysis (ICA) models are very popular semiparametric models in which we observe independent copies of a random vector $X = AS$, where $A$ is a non-singular matrix and $S$ has in...
We study a parametric estimation problem related to moment condition models. As an alternative to the generalized empirical likelihood (GEL) and the generalized method of moments (GMM), a Bayesian app...
In this paper, we study the empirical likelihood method for the pth-order random coefficient integer-valued autoregressive process. In particular, the limiting distribution of the log empirical likeli...
This paper studies the empirical likelihood method for a first-order random coefficient integer-valued autoregressive model. The limiting distribution of the log empirical likelihood ratio statistic i...

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