Use of several auxiliary variables in estimating the population mean in a two-occasion rotation pattern

ABSTRACT

This paper addresses the problem of estimation of the population mean on the current (second) occasion in two-occasion successive sampling. Utilizing the readily available information on several auxiliary variables on both occasions and the information on the study variable from the previous occasion, an estimation procedure of the population mean on the current occasion has been proposed. Theoretical properties of the proposed estimator have been investigated. Optimum replacement policy to the proposed estimator has been discussed. The proposed estimator has been compared empirically with the sample mean estimator, when there is no matching and the optimum estimator which is a linear combination of the means of the matched and unmatched portions of the sample at the current occasion. Appropriate recommendations have been made for practical applications.     

Generalized Weibull Linear Models

For the first time, a new class of generalized Weibull linear models is introduced to be competitive to the well-known generalized (gamma and inverse Gaussian) linear models which are adequate for the analysis of positive continuous data. The proposed models have a constant coefficient of variation for all observations similar to the gamma models and may be suitable for a wide range of practical applications in various fields such as biology, medicine, engineering, and economics, among others. We derive a joint iterative algorithm for estimating the mean and dispersion parameters. We obtain closed form expressions in matrix notation for the second-order biases of the maximum likelihood estimates of the model parameters and define bias corrected estimates. The corrected estimates are easily obtained as vectors of regression coefficients in suitable weighted linear regressions. The practical use of the new class of models is illustrated in one application to a lung cancer data set.     

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.     

Comparison of estimators of the location parameter using pitman's closeness criterion

Necessary and sufficient conditions for a linear estimator to dominate another linear estimator of a location parameter under the Pitman's criterion of comparison are discussed. Consequently it is demonstrated that a linear biased estimator can not dominate a linear unbiased estimator under Pitman's criterion and that the sample mean is the Closest Linear Unbiased Estimator (CLUE). It is also shown that the ridge regression estimator with a known biasing constant can not dominate the ordinary least squares estimator. If an estimator δdominates an estimator δin the average loss sense then sufficient conditions are obtained under which δis also preferred over δunder Pitman's criterion. Further we obtain sufficient conditions under which preference under the Pitman's criterion will lead to preference under the mean squared error sense.     

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.     

刑事自诉中止审理研究

2006年笔者选择了四川五个区县进行刑事自诉案件的调查，调查采取查阅档案和访谈等方式进行。通过调查，发现自诉案件立案后在遇到被告人下落不明的时候，法庭裁定中止审理的案件越来越多。但是法庭中止审理之后。自诉案件就被搁置起来，原告的权利得不到维护，附带民事诉讼事实上被终止，由纠纷带来的社会秩序的裂痕无法平复。一些基层法院变通的处理办法是劝说原告撤诉，并告之将来被告出现后可再行起诉，但是这样的变通在法律上又是行不通的。因此，要解决目前在刑事自诉案件中止审理程序中的不足之处，就有必要建立起我国的刑事缺席审判制度，从而有效的解决纠纷。保护公民的合法权利。     

刑事判决书上网制度

刑事诉讼的判决书是集中反映审判过程和处理当事人实体问题的载体,笔者积极倡导应将刑事判决书及时而不加修饰地在网上发布,这不仅符合审判公开的原则而且也符合司法公开、公平、公正的理念,"正义不仅应得到实现,而且应当以人们看得见的方式得以实现"刑事判决书关系当事人的重大利益,是高高在上的法律与民众之间最重要的沟通媒介,人们可以通过上网这种现代化的获知信息的便捷方式来了解判决书内容,使得司法更透明,更亲民,也更有利维护司法的权威。针对现在国内判决书现状,判决书上网的必要性及判决书上网的具体操作谈谈看法。     

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.     

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.     

Integrating psychometric and cultural theory approaches to formulate an alternative measure of risk perception

Psychometric and cultural theory are key approaches used to explain risk perceptions. However, both approaches explain only 20 and 5–10% of the variance of risk perceptions, respectively. Through advances in social psychology, terror management theory revealed that certain survey questions used in the psychometric approach primed thoughts of death. Such unintentional priming evoked defense mechanisms that would subconsciously activate participants’ cultural biases and values when answering some questions but not others. There are two implications: one, psychometric theorists need to modify their questionnaires to reduce the incidence of priming thoughts of death; and two, integrating the psychometric and cultural theory approaches may potentially explain a higher amount of variance in risk perceptions. Specifically, attitudes (psychometric approach), demographics, affect and past behavior collectively influence cultural biases, which in turn exert an effect on risk perception. However, such a model calls for an appropriate measure of the dynamic nature of cultural biases that current attitudinal measures fall short of. Drawing from methodology within industrial/organizational psychology, the Situational Judgment Test (SJT) is proposed as an alternative measure for risk perception. Steps to designing an SJT are outlined and followed through to culminate in a 10-item SJT that measures cultural biases.