A note on unbiasedness in ratio estimation

The method of ratio estimation for estimating the population mean ? of a characteristic y when we have auxillary information on a characteristic x highly correlated with y, consists in getting an estimator of the population ratio R = ?/X? and then multiplying this estimator by the known population mean X?. Though efficient, ratio estimators are in general biased and in this article we review some of the unbiased ratio estimators and discuss a method of constructing them. Next we present the Jackknife technique for reducing bias and show how the generalized Jackknife could be interpreted by the same method.     

Optimal estimation of a finite population mean under generalized random permutation models

Employing certain generalized random permutation models and a general class of linear estimators of a finite population mean, it is shown that many of the conventional estimators are “optimal” in the sense of minimum average mean square error. Simple proofs are provided by using a well-known theorem on UMV estimation. The results also cover certain simple response error situations.     

Practical Methods for Meeting Remediation Goals at Hazardous Waste Sites

Risk-based cleanup goals or preliminary remediation goals (PRGs) are established at hazardous waste sites when contaminant concentrations in air, soil, surface water, or groundwater exceed specified acceptable risk levels. When derived in accordance with the Environmental Protection Agency's risk assessment guidance, the PRG is intended to represent the average contaminant concentration within an exposure unit area that is left on the site following remediation. The PRG, however, frequently has been used inconsistently at Superfund sites with a number of remediation decisions using the PRG as a not-to-exceed concentration (NTEC). Such misapplications could result in overly conservative and unnecessarily costly remedial actions. The PRG should be applied in remedial actions in the same manner in which it was generated. Statistical methods, such as Bower's Confidence Response Goal, and mathematical methods such as "iterative removal of hot spots," are available to assist in the development of NTECs that ensure the average postremediation contaminant concentration is at or below the PRG. These NTECs can provide the risk manager with a more practical cleanup goal. In addition, an acute PRG can be developed to ensure that contaminant concentrations left on-site following remediation are not so high as to pose an acute or short-term health risk if excessive exposure to small areas of the site should occur. A case study demonstrates cost savings of five to ten times associated with the more scientifically sound use of the PRG as a postremediation site average, and development of a separate NTEC and acute PRG based on the methods referenced in this article.     

Confidence intervals,significance values,maximum likelihood estimates,etc. sharpened into Occam’s razors

Abstract

Confidence sets, p values, maximum likelihood estimates, and other results of non-Bayesian statistical methods may be adjusted to favor sampling distributions that are simple compared to others in the parametric family. The adjustments are derived from a prior likelihood function previously used to adjust posterior distributions.     

Fast and Accurate Inference for the Smoothing Parameter in Semiparametric Models

A fast and accurate method of confidence interval construction for the smoothing parameter in penalised spline and partially linear models is proposed. The method is akin to a parametric percentile bootstrap where Monte Carlo simulation is replaced by saddlepoint approximation, and can therefore be viewed as an approximate bootstrap. It is applicable in a quite general setting, requiring only that the underlying estimator be the root of an estimating equation that is a quadratic form in normal random variables. This is the case under a variety of optimality criteria such as those commonly denoted by maximum likelihood (ML), restricted ML (REML), generalized cross validation (GCV) and Akaike's information criteria (AIC). Simulation studies reveal that under the ML and REML criteria, the method delivers a near‐exact performance with computational speeds that are an order of magnitude faster than existing exact methods, and two orders of magnitude faster than a classical bootstrap. Perhaps most importantly, the proposed method also offers a computationally feasible alternative when no known exact or asymptotic methods exist, e.g. GCV and AIC. An application is illustrated by applying the methodology to well‐known fossil data. Giving a range of plausible smoothed values in this instance can help answer questions about the statistical significance of apparent features in the data.     

Slashed generalized exponential distribution

In this paper, we introduce an extension of the generalized exponential (GE) distribution, making it more robust against possible influential observations. The new model is defined as the quotient between a GE random variable and a beta-distributed random variable with one unknown parameter. The resulting distribution is a distribution with greater kurtosis than the GE distribution. Probability properties of the distribution such as moments and asymmetry and kurtosis are studied. Likewise, statistical properties are investigated using the method of moments and the maximum likelihood approach. Two real data analyses are reported illustrating better performance of the new model over the GE model.     

Assessing the Performance of a Classification‐Based Vulnerability Analysis Model

In this article, a classification model based on the majority rule sorting (MR‐Sort) method is employed to evaluate the vulnerability of safety‐critical systems with respect to malevolent intentional acts. The model is built on the basis of a (limited‐size) set of data representing (a priori known) vulnerability classification examples. The empirical construction of the classification model introduces a source of uncertainty into the vulnerability analysis process: a quantitative assessment of the performance of the classification model (in terms of accuracy and confidence in the assignments) is thus in order. Three different app oaches are here considered to this aim: (i) a model–retrieval‐based approach, (ii) the bootstrap method, and (iii) the leave‐one‐out cross‐validation technique. The analyses are presented with reference to an exemplificative case study involving the vulnerability assessment of nuclear power plants.     

中国消费者信心影响因素实证分析

消费者信心是对消费者整体所表现出来的信心程度及其变动的一种测度。使用离散顺序选择模型对2009年第1季度中国大陆消费者信心指数的原始调查数据进行实证分析,结果显示：消费者对未来经济发展的信心受到其对未来就业、收入、生活和投资四个方面信心的显著影响。     

十月革命与中国道路自信

十月革命最伟大的历史贡献是为人类社会开辟了一条崭新的发展道路。十月革命虽始于苏俄,但惠及世界。中国特色社会主义道路是十月革命道路的延续与创新。近三十多年来,中国在社会主义建设上取得的震惊世界的伟大成就,进一步夯实了中国特色社会主义道路自信的实践基础,但同时也存在抹黑、否定十月革命、诋毁中国特色社会主义道路的历史虚无主义思潮,对此必须坚决予以驳斥。