Empirical bayes estimation of reliability characteristics for an exponential family

This paper considers empirical Bayes (EB) squared-error-loss estimations of mean lifetime, variance and reliability function for failure-time distributions belonging to an exponential family, which includes gamma and Weibull distributions as special cases. EB estimators are proposed when the prior distribution of the lifetime parameter is completely unknown but has a compact (known or unknown) support. Asymptotic optimality and rates of convergence of these estimators are investigated. The rates established here under the compact support restriction are better than the polynomial rates of convergence obtained previously.     

Statistical estimation of time-trends in two-course crop rotations

An assessment of time-trends in yield parameters is essential to the utilization of data from long-term field trials for the comparison of different crop rotations and input regimes, and the identification of sustainable production systems. The barley-vetch rotation established at Breda in northern Syria has provided the basis for estimation of the time-trends in yield data from selected treatments in a two-course crop rotation trial. The model used for the estimation accounts for the effect of rainfall, a major determinant of each annual yield value, and the first-order autocorrelation structure in the errors arising from the same plot over time. An expression for the minimum number of cycles required to detect a significant time-trend has been obtained. Results from the barley-vetch rotation under two fertilizer regimes have been discussed.     

Estimation of a rare sensitive attribute in a stratified two-stage unrelated randomized response model by using Poisson probability distribution

The present article deals with the estimation of mean number of respondents who possess a rare sensitive character in presence of known and unknown proportion of a rare unrelated non-sensitive attribute by using the Poisson probability distribution in stratified random sampling as well as in stratified random double sampling. The variance of rare sensitive character is also derived under proportional and optimal allocation methods in stratified random sampling when stratum sizes are known and unknown. The properties of the suggested estimation procedures have been deeply examined. The proposed model is found to be dominant over Lee et al. [Estimation of a rare sensitive attribute in a stratified sample using Poisson distribution. Statistics. 2013;47:575–589] model. Numerical illustrations are presented to support the theoretical results. Results are analysed and suitable recommendations are put forward to the survey practitioners.     

A two-stage unrelated randomized response model for estimating a rare sensitive attribute in probability proportional to size sampling using Poisson distribution

This paper describes the estimating procedures of mean number of entities that possess a rare sensitive attribute using the Mangat (1992) randomized device, when the population consists of some clusters and the population is again stratified with some clusters in each stratum. Unbiased estimation procedures for the mean number of individuals have been discussed and their properties are described when the parameter of a rare unrelated attribute is assumed to be known and unknown. An empirical study is carried out to show the dominance of the proposed estimator over Lee et al. (2013) estimator.     

On sign-based regression quantiles

A sign-based (SB) approach suggests an alternative criterion for quantile regression fit. The SB criterion is a piecewise constant function, which often leads to a non-unique solution. We compare the mid-point of this SB solution with the least absolute deviations (LAD) method and describe asymptotic properties of SB estimators under a weaker set of assumptions as compared with the assumptions often used with the generalized method of moments. Asymptotic properties of LAD and SB estimators are equivalent; however, there are finite sample differences as we show in simulation studies. At small to moderate sample sizes, the SB procedure for modelling quantiles at longer tails demonstrates a substantially lower bias, variance, and mean-squared error when compared with the LAD. In the illustrative example, we model a 0.8-level quantile of hospital charges and highlight finite sample advantage of the SB versus LAD.     

Parameter Estimation for Hidden Markov Models with Intractable Likelihoods

Approximate Bayesian computation (ABC) is a popular technique for analysing data for complex models where the likelihood function is intractable. It involves using simulation from the model to approximate the likelihood, with this approximate likelihood then being used to construct an approximate posterior. In this paper, we consider methods that estimate the parameters by maximizing the approximate likelihood used in ABC. We give a theoretical analysis of the asymptotic properties of the resulting estimator. In particular, we derive results analogous to those of consistency and asymptotic normality for standard maximum likelihood estimation. We also discuss how sequential Monte Carlo methods provide a natural method for implementing our likelihood‐based ABC procedures.     

Estimation of Finite Population Variance Using Scrambled Responses in the Presence of Auxiliary Information

In this article, a new estimator for estimating the finite population variance of a sensitive variable based on scrambled responses collected using a randomization device is introduced. The estimator is then improved by using known auxiliary information. The estimators due to Das and Tripathi (1978: Sankhya) and Isaki (1983: JASA) are shown to be special cases of the proposed estimator. Numerical simulations are performed to study the magnitude of the gain in efficiency when using the estimator with auxiliary information with respect to the estimator based only on the scrambled responses. An idea to extend the present work from SRSWOR design to more complex design is also given.     

Effects of face masks on language comprehension in bilingual children

Due to the COVID-19 pandemic, many children receive language input through face coverings. The impact of face coverings for children's abilities to understand language remains unclear. Past research with monolingual children suggests that hearing words through surgical masks does not disrupt word recognition, but hearing words through transparent face shields proves more challenging. In this study, we investigated effects of different face coverings (surgical masks and transparent face shields) on language comprehension in bilingual children. Three-year-old English-Mandarin bilingual children (N = 28) heard familiar words in both English and Mandarin spoken through transparent face shields, surgical masks, and without masks. When tested in English, children recognized words presented without a mask and through a surgical mask, but did not recognize words presented with transparent face shields, replicating past findings with monolingual children. In contrast, when tested in Mandarin, children recognized words presented without a mask, through a surgical mask, and through a transparent face shield. Results are discussed in terms of specific properties of English and Mandarin that may elicit different effects for transparent face shields. Overall, the present findings suggest that face coverings, and in particular, surgical masks do not disrupt spoken word recognition in young bilingual children.     

Projecting populations for major Pacific Island countries with and without COVID-19: pro-active insights for population policy

Journal of Population Research - The paper projects aggregate populations of six Pacific Island countries in both pre- and post-COVID19 scenarios using a Cohort Component Method for the period...