Ratio- and difference-type estimators for estimating finite population variance

We consider the problem of estimating finite population variance of a study character when information on an auxiliary character is available. We define ratio- and difference-type estimators and obtain the mean-squared error of them approximately. Using a numerical study, we compare the performances of the proposed estimators with some existing variance estimators.     

On the estimation of missing values in AR(1) model with exponential innovations

We consider estimation of a missing value for a stationary autoregressive process of order one with exponential innovations and compare two methods of estimation of the missing value, with respect to Pitman's measure of closeness (PMC).     

Bayesian inference and prediction of the Pareto distribution based on ordered ranked set sampling

In this paper, order statistics from independent and non identically distributed random variables is used to obtain ordered ranked set sampling (ORSS). Bayesian inference of unknown parameters under a squared error loss function of the Pareto distribution is determined. We compute the minimum posterior expected loss (the posterior risk) of the derived estimates and compare them with those based on the corresponding simple random sample (SRS) to assess the efficiency of the obtained estimates. Two-sample Bayesian prediction for future observations is introduced by using SRS and ORSS for one- and m-cycle. A simulation study and real data are applied to show the proposed results.     

Evaluation of the predictive performance of the Liu type estimator

This article discusses the predictive performance of the Liu type (LT) estimator compared to ordinary least squares, principal components, ridge regression, and Liu estimators. The theoretical results are illustrated by a numerical example and a region is established where the LT estimator is uniformly superior to the other mentioned estimators.     

Discussion on the common threshold of several exponential distributions with different rates

The integration of results of independent studies in order to make inferences about a common threshold is an important problem with many practical applications. In this article, we apply the generalized variable method to make inferences on the common threshold of several exponential distributions when the scale (or rate) parameters are unknown and unequal. The merits of the proposed method are computed numerically and compared with other existing methods. Numerical results of both simulation studies and real data analyses show that the proposed method is applicable and its performance is better than other methods even when sample sizes are small.     

Estimation of population mean in two-occasion successive sampling

In the present article, we have proposed some classes of estimators based on transformed auxiliary variable. The biases and mean squared errors (MSEs) of the proposed estimators have been obtained. The proposed estimators have been compared with simple mean estimator when there is no matching and the optimum estimator, which is a combination of the means of the matched and unmatched portion of the sample at the second occasion. Optimum replacement policy and the efficiency of the proposed estimators have been discussed. Theoretical results are well supported with an empirical study.     

Supersmooth testing on the sphere over analytic classes

We consider the nonparametric goodness-of-fit test of the uniform density on the sphere when we have observations whose density is the convolution of an error density and the true underlying density. We will deal specifically with the supersmooth error case which includes the Gaussian distribution. Similar to deconvolution density estimation, the smoother the error density the harder is the rate recovery of the test problem. When considering nonparametric alternatives expressed over analytic classes, we show that it is possible to obtain original separation rates much faster than any logarithmic power of the sample size according to the ratio of the regularity index of the analytic class and the smoothness degree of the error. Furthermore, we show that our fully data-driven statistical procedure attains these optimal rates.     

What drives the attentional bias for fearful faces? An eye‐tracking investigation of 7‐month‐old infants’ visual scanning patterns

Seven‐month‐old infants display a robust attentional bias for fearful faces; however, the mechanisms driving this bias remain unclear. The objective of the current study was to replicate the attentional bias for fearful faces and to investigate how infants’ online scanning patterns relate to this preference. Infants’ visual scanning patterns toward fearful and happy faces were captured using eye tracking in a paired‐preference task, specifically exploring if the fear preference is driven by increased attention to particular facial features. Infants allocated increased attention toward the fearful face compared to the happy face overall, thus successfully replicating the attentional bias, and greater attention toward the fearful eyes was associated with a greater magnitude of the fear preference. The current findings suggest that the fearful eyes are a salient facial feature in capturing infants’ attention toward the fearful face and that increased scanning of the fearful eyes may be one mechanism driving the overall fear preference. In addition, scanning patterns, and attention to critical features specifically, are highlighted as a strategy for examining the mechanisms underlying the development of emotion recognition abilities in infancy.     

灾变性事件引起的消费品产业链价格波及效应研究

为了研究地震等灾变性事件对于消费品产业链内价格传递和市场的影响,本文利用从2008年2月至2009年3月四川省江油市畜产业链进行实证研究,使用向量纠错模型(VECM)和历史分解图(HDG)分析了价格调整的动态效果和沿产业链的因果关系。结果表明,价格的调整关于速度和大小都是不对称的,外生震荡对于该产业链的不同层面产生不同影响,导致价格差益的扩大和价格不完全传递。该方法能够有效地分析灾变事件条件下消费品产业链内价格波及效应问题。