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In this paper, we proposed new conventional unbiased and calibration estimators for estimating finite population variance for a Mail Survey Design characterized by the presence of non-response in practice. The properties of the proposed estimators are studied theoretically and numerically. Empirical studies were conducted using two each of existing and simulated data to illustrate the performance of proposed estimators over existing ones. The results of both theoretical and numerical comparison depicted the superiority of proposed estimators for estimating population variance for a Mail Survey Design characterized by non-response. [ABSTRACT FROM AUTHOR]

Empirical likelihood has been widely used in survival data analysis recently. In this paper, we combine Bayesian idea with empirical likelihood and develop a Bayesian empirical likelihood method to analyze current status data based on the linear regression model. By constructing unbiased transformation of current status data, we derive an empirical log-likelihood function. The normal prior distribution and a Metro-Hastings method are presented to make Bayesian posterior inference. The theoretical properties of the estimators are proposed. Extensive simulation studies indicate that Bayesian empirical likelihood method performs much better than the empirical likelihood method in terms of coverage probability. Finally, we apply two real data to illustrate the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we study the empirical likelihood method for special self-exciting threshold autoregressive models. We assume that the parameters of models depend on some positive integer n, the threshold effect diminishes to zero as n increases, and the errors have heavy-tailed distributions. After replacing the indicator function in the model with a smooth function, we obtain a self-weighted and smoothed least absolute deviation estimator for the threshold parameter and the asymptotic normality of the estimator is proved. Then, the confidence intervals for the threshold parameter can be constructed by the normal approximation method. In order to improve the coverage of confidence intervals, we further propose a profile empirical likelihood ratio, and prove that this statistic has the asymptotically standard chi-squared distribution. Therefore, the confidence interval of the threshold parameter can also be constructed by the empirical likelihood method. Simulations and empirical results demonstrate that the confidence interval constructed by empirical likelihood method is superior to that constructed by normal approximation method. [ABSTRACT FROM AUTHOR]

This paper presents a new efficient and robust inference method by combing the robust generalized estimating equations and the well-known empirical likelihood method in longitudinal data analysis. Based on a bounded exponential score function and leverage-based weights, robust auxiliary random vectors are constructed to achieve robustness against outliers both in the response and the covariate domains. Moreover, the additional tuning parameter in the exponential score function can be automatically selected by the observed data. Finally, some simulation studies and a real data analysis are carried out to demonstrate the performances of the proposed method. [ABSTRACT FROM AUTHOR]