Notes on testing carry-over effects in continuous data under an incomplete block crossover design

We consider hypothesis testing and estimation of carry-over effects in continuous data under an incomplete block crossover design when comparing two experimental treatments with a placebo. We develop procedures for testing differential carry-over effects based on the weighted-least-squares (WLS) method. We apply Monte Carlo simulations to evaluate the performance of these test procedures in a variety of situations. We use the data regarding the forced expiratory volume in one second (FEV₁) readings taken from a double-blind crossover trial comparing two different doses of formoterol with a placebo to illustrate the use of test procedures proposed here.     

Estimation of variance in bivariate normal distribution after the preliminary test of homogeneity

A preliminary test estimator of variance in the bivariate normal distribution is proposed after the Pitman–Morgan test of homogeneity of two variances. The bias and mean square error of the estimator are derived. The relative efficiency (RE) of the preliminary test estimator is studied. Computations and 3D graphs of RE for different parameters are analyzed. In order to get the maximum RE, recommendations of the significance level for the preliminary test are given for various sample sizes by using the max–min criterion.     

CPI之GEKS指数序列更新方法及窗口长度选择问题辨析

大数据来源下CPI指数的创新编制,对及时了解新经济时代的物价走向和识别通胀危机、预测宏观经济拐点以实现我国通胀治理现代化、推动经济平稳和高质量发展具有重大意义。GEKS多边指数是近些年国际学术界重点研发的大数据热点价格指数,但其构造方法颇具争议。借助超市扫描大数据,就GEKS指数序列更新方法、窗口长度选择等学界难题开展理论与实证研究,获得了以下富有启发性的结论:①GEKS指数序列更新方法2、3应用效果相对较差;②随着窗口长度的增加,GEKS环比价格指数会趋于单位值,不同更新方法下的GEKS链式指数也会呈现一定的趋同性;而GEKS指数的通胀趋势判断力却不受此影响,但更新方法的选择却会导致其不同的通胀趋势预测结果;③更新方法4会随着窗口长度的增加而呈现更强的替代偏误,方法1却没有出现明显的替代偏误。综合而言,更新方法1和13个月窗口长度应该是编制GEKS指数序列更为合理的组合方式。     

Jackknifing in Categorical Data Analysis

Estimation of nonlinear functions of a multinomial parameter vector is necessary in many categorical data problems. The first and second order jackknife are explored for the purpose of reduction of bias. The second order jackknife of a function g(.) of a multinomial parameter is shown to be asymptotically normal if all second order partials ?²g( p )?dp_i?p_j obey a Hölder condition with exponent α>1/2. Numerical results for the estimation of the log odds ratio in a 2times2 table demonstrate the efficiency of the jackknife method for reduction of mean-square-error and the construction of approximate confidence intervals.     

Random regression models for human immunodeficiency virus ribonucleic acid data subject to left censoring and informative drop-outs

Objectives in many longitudinal studies of individuals infected with the human immunodeficiency virus (HIV) include the estimation of population average trajectories of HIV ribonucleic acid (RNA) over time and tests for differences in trajectory across subgroups. Special features that are often inherent in the underlying data include a tendency for some HIV RNA levels to be below an assay detection limit, and for individuals with high initial levels or high ranges of change to drop out of the study early because of illness or death. We develop a likelihood for the observed data that incorporates both of these features. Informative drop-outs are handled by means of an approach previously published by Schluchter. Using data from the HIV Epidemiology Research Study, we implement a maximum likelihood procedure to estimate initial HIV RNA levels and slopes within a population, compare these parameters across subgroups of HIV-infected women and illustrate the importance of appropriate treatment of left censoring and informative drop-outs. We also assess model assumptions and consider the prediction of random intercepts and slopes in this setting. The results suggest that marked bias in estimates of fixed effects, variance components and standard errors in the analysis of HIV RNA data might be avoided by the use of methods like those illustrated.     

Robust models in probability sampling

In the estimation of a population mean or total from a random sample, certain methods based on linear models are known to be automatically design consistent, regardless of how well the underlying model describes the population. A sufficient condition is identified for this type of robustness to model failure; the condition, which we call 'internal bias calibration', relates to the combination of a model and the method used to fit it. Included among the internally bias-calibrated models, in addition to the aforementioned linear models, are certain canonical link generalized linear models and nonparametric regressions constructed from them by a particular style of local likelihood fitting. Other models can often be made robust by using a suboptimal fitting method. Thus the class of model-based, but design consistent, analyses is enlarged to include more realistic models for certain types of survey variable such as binary indicators and counts. Particular applications discussed are the estimation of the size of a population subdomain, as arises in tax auditing for example, and the estimation of a bootstrap tail probability.     

A package of four edited books on structural equations modeling

We consider two analytical and a bootstrap bias correction scheme existing in the literature for maximum likelihood estimators (MLEs) in the special case of a particular biparametric exponential family, the estimators being obtained from i.i.d. samples. We assess the performances of the estimators through numerical simulations for three particular cases of the family explored here. We observe that the two analytical proposals display very similar behavior for these distributions and that all proposed estimators are effective in reducing bias and mean square error of the MLEs.     

Numerical evaluation of tests based on different heteroskedasticity-consistent covariance matrix estimators

This article considers the issue of performing tests in linear heteroskedastic models when the test statistic employs a consistent variance estimator. Several different estimators are considered, namely: HC0, HC1, HC2, HC3, and their bias-adjusted versions. The numerical evaluation is performed using numerical integration methods; the Imhof algorithm is used to that end. The results show that bias-adjustment of variance estimators used to construct test statistics delivers more reliable tests when they are performed for the HC0 and HC1 estimators, but the same does not hold for the HC3 estimator. Overall, the most reliable test is the HC3-based one.     

劳动力就业城市选择对城市工资溢价的影响

中国逐步从定居社会转变为流迁社会,劳动力大范围流动体现在就业城市的自主选择上,该行为对城市工资溢价产生持续影响,同时城市工资溢价又影响劳动力的就业城市选择。利用2017年中国流动人口动态监测调查数据,构建包含个体选择的处置效应模型,实证分析发现,中国存在城市工资溢价现象,劳动力的异质性特征和城市差异性特征显著影响就业城市选择行为;纠正劳动力城市选择的内生二元选择偏差后,发现中国特大城市和超大城市的工资溢价变大,而大城市的工资溢价消失,中小城市工资比大城市更有竞争力。中国城市体系中的首尾优势是劳动力理性选择就业城市的结果,这为中国实施差异化的城市发展政策来实现城市体系的整体优化和内部协调提供了新思路。     

多元的群体 偏离的印象——对中国“90后”社会特征的解析

90后群体是当前社会关注的焦点。以中国社会发展进程为纵线,以影响90后群体的主要心理因素为横线,从社会化进程、教育化选择、网络化生活以及同一性承诺等四个方面对90后群体的多元可能和形成过程进行解析发现:90后的天生多元促成了其群体多元的真正实现:90后教育选择的多样性加剧了其多元的可能性;90后的网络化生存(工作)能力提供了其多元的现实工具;90后的自我意识中心反映了其较早的自我觉醒。因此,面对社会给予90后的诸多负面评价与90后良好自我认同的差异与偏离,我们需要聆听他们对世界的认知。