Hypothesis test for paired samples in the presence of missing data

Missing data are present in almost all statistical analysis. In simple paired design tests, when some subject has one of the involved variables missing in the so-called partially overlapping samples scheme, it is usually discarded for the analysis. The lack of consistency between the information reported in the univariate and multivariate analysis is, perhaps, the main consequence. Although the randomness on the missing mechanism (missingness completely at random) is an usual and needed assumption for this particular situation, missing data presence could lead to serious inconsistencies on the reported conclusions. In this paper, the authors develop a simple and direct procedure which allows using the whole available information in order to perform paired tests. In particular, the proposed methodology is applied to check the equality among the means from two paired samples. In addition, the use of two different resampling techniques is also explored. Finally, real-world data are analysed.     

水基泡沫流变特性研究进展

水基泡沫在石油钻井、驱油及矿物浮选等方面应用广泛，但因其自身结构的复杂性及不稳定性，准确描述其流变性需要考虑液膜排液、气体扩散、泡沫质量、泡沫结构、可压缩性、壁面滑移、测量系统相对于气泡的尺寸、环境温度及压力等多方面因素，以致于目前尚无有关泡沫流变性的公认理论。实验研究过程中控制壁面滑移的较普遍做法是增加流道壁面（转子表面或管壁）的粗糙度，基于适当假设所提出的一些理论修正方法具有较好的适应性。体积平衡法假设泡沫管流摩擦系数为常量，在一定程度上解决了泡沫可压缩性给管流压降计算所带来的困难。目前普遍认为泡沫流变性可用幂律模式或赫谢尔–巴尔克莱模式很好地描述，是否存在“屈服应力”则因测试条件差异而存在争议。     

Bias-corrected confidence bands in nonparametric regression

Bias-corrected confidence bands for general nonparametric regression models are considered. We use local polynomial fitting to construct the confidence bands and combine the cross-validation method and the plug-in method to select the bandwidths. Related asymptotic results are obtained. Our simulations show that confidence bands constructed by local polynomial fitting have much better coverage than those constructed by using the Nadaraya–Watson estimator. The results are also applicable to nonparametric autoregressive time series models.     

青少年网瘾的心理危机分析与社会矫正

社会转型时期弱势群体的社会心理问题日益凸显,其中,青少年网络社会问题已引起普遍关注.青少年成长过程中的社会心理环境、对青少年单一的评价系统、学业和就业的巨大压力、群体内部人际交往的不适应等,都是促使部分青少年沉迷网络不能自拔的重要原因.要使青少年从根源上戒除网络游戏成瘾,摆脱由此造成的心理危机,仅靠单纯的心理辅导、正面...     

“边缘化”大学生个体成因、预防与矫治的研究

＂边缘化＂学生是指行为超出正常规范、学业严重不佳的学生个体。认为中学及大学阶段的管理、社会因素、理想与现实的错位以及个人性格心理特点等是形成＂边缘化＂学生个体的主要原因;管理者建立＂防边缘化＂预警机制、抓住管理的关键时期及努力营造良好学风是有效预防＂边缘化＂的措施;选择适当的角度和有效的工作方法可以对＂边缘化＂学生完成成功矫治。     

Two-tailed asymptotic inferences for a proportion

This paper evaluates 29 methods for obtaining a two-sided confidence interval for a binomial proportion (16 of which are new proposals) and comes to the conclusion that: Wilson's classic method is only optimal for a confidence of 99%, although generally it can be applied when n≥50; for a confidence of 95% or 90%, the optimal method is the one based on the arcsine transformation (when this is applied to the data incremented by 0.5), which behaves in a very similar manner to Jeffreys’ Bayesian method. A simpler option, though not so good as those just mentioned, is the classic-adjusted Wald method of Agresti and Coull.     

Verification bias on sensitivity and specificity measurements in diagnostic medicine: a comparison of some approaches used for correction

Verification bias may occur when the test results of not all subjects are verified by using a gold standard. The correction for this bias can be made using different approaches depending on whether missing gold standard test results are random or not. Some of these approaches with binary test and gold standard results include the correction method by Begg and Greenes, lower and upper limits for diagnostic measurements by Zhou, logistic regression method, multiple imputation method, and neural networks. In this study, all these approaches are compared by employing a real and simulated data under different conditions.     

Estimation of population mean and variance in flock management: a ranked set sampling approach in a finite population setting

Ranked set sampling is a sampling technique that provides substantial cost efficiency in experiments where a quick, inexpensive ranking procedure is available to rank the units prior to formal, expensive and precise measurements. Although the theoretical properties and relative efficiencies of this approach with respect to simple random sampling have been extensively studied in the literature for the infinite population setting, the use of ranked set sampling methods has not yet been explored widely for finite populations. The purpose of this study is to use sheep population data from the Research Farm at Ataturk University, Erzurum, Turkey, to demonstrate the practical benefits of ranked set sampling procedures relative to the more commonly used simple random sampling estimation of the population mean and variance in a finite population. It is shown that the ranked set sample mean remains unbiased for the population mean as is the case for the infinite population, but the variance estimators are unbiased only with use of the finite population correction factor. Both mean and variance estimators provide substantial improvement over their simple random sample counterparts.     

Bias corrected MLEs under progressive type-II censoring scheme

ABSTRACT

Censoring frequently occurs in survival analysis but naturally observed lifetimes are not of a large size. Thus, inferences based on the popular maximum likelihood (ML) estimation which often give biased estimates should be corrected in the sense of bias. Here, we investigate the biases of ML estimates under the progressive type-II censoring scheme (pIIcs). We use a method proposed in Efron and Johnstone [Fisher's information in terms of the hazard rate. Technical Report No. 264, January 1987, Stanford University, Stanford, California; 1987] to derive general expressions for bias corrected ML estimates under the pIIcs. This requires derivation of the Fisher information matrix under the pIIcs. As an application, exact expressions are given for bias corrected ML estimates of the Weibull distribution under the pIIcs. The performance of the bias corrected ML estimates and ML estimates are compared by simulations and a real data application.