Estimating population characteristics by incorporating prior values in stratified random sampling/ranked set sampling

In this paper, we discuss the estimation of population characteristics using stratified random sampling in an infinite population framework, including ranked set sampling as a special case. The use of prior values is considered and the underlying distribution is assumed to be unknown. The estimator considered in each stratum is the weighted mean of the U-statistic and prior value. The optimum weight is obtained by minimizing the mean squared error of the estimator of the population characteristics, but it contains unknown parameters and those parameters are replaced with their estimates. Simulation results show the gains in efficiency of the proposed estimator, yielding gains of at least 1.2 times larger than the usual unbiased estimator under certain condition specified in the text. Guidelines for the usage of the proposed estimator are shown and an application to a real data set is provided.     

Second‐order bias‐corrected AIC in multivariate normal linear models under non‐normality

This paper deals with a bias correction of Akaike's information criterion (AIC) for selecting variables in multivariate normal linear regression models when the true distribution of observation is an unknown non‐normal distribution. It is well known that the bias of AIC is $O(1)$ , and there are a number of the first‐order bias‐corrected AICs which improve the bias to $O(n^{-1})$ , where $n$ is the sample size. A new information criterion is proposed by slightly adjusting the first‐order bias‐corrected AIC. Although the adjustment is achieved by merely using constant coefficients, the bias of the new criterion is reduced to $O(n^{-2})$ . Then, a variance of the new criterion is also improved. Through numerical experiments, we verify that our criterion is superior to others. The Canadian Journal of Statistics 39: 126–146; 2011 © 2011 Statistical Society of Canada     

Statistical inference of agreement coefficient between two raters with binary outcomes

Abstract

Scott’s pi and Cohen’s kappa are widely used for assessing the degree of agreement between two raters with binary outcomes. However, many authors have pointed out its paradoxical behavior, that comes from the dependence on the prevalence of a trait under study. To overcome the limitation, Gwet [Computing inter-rater reliability and its variance in the presence of high agreement. British Journal of Mathematical and Statistical Psychology 61(1):29–48] proposed an alternative and more stable agreement coefficient referred to as the AC₁. In this article, we discuss a likelihood-based inference of the AC₁ in the case of two raters with binary outcomes. Construction of confidence intervals is mainly discussed. In addition, hypothesis testing, and sample size estimation are also presented.     

Elite mobility and continuity during a regime change

How does a regime change influence elite mobility? By collecting data on elites after the Meiji Restoration in Japan in 1868, through which Japan transitioned from a feudal regime to a modern regime, we provide new evidence that the impact of the regime change on elite mobility varies across the stages of the regime change. We analyze the impact of the regime change from two aspects: (1) the composition of elites or elite membership and (2) the internal hierarchy within them. The regime change opened an opportunity for commoners to join the elite group. After the Meiji Restoration, the share of elites whose fathers were commoners in the former regime increased, as did the influence of meritocracy on elite ranks. However, once the new regime was established, the elite hierarchy started to reflect the social stratum of the former regime and the influence of meritocracy declined.