Asymptotic Expansion and Conditional Robustness for the Sample Multiple Correlation Coefficient Under Nonnormality

ABSTRACT

Asymptotic distributions of the standardized estimators of the squared and non squared multiple correlation coefficients under nonnormality were obtained using Edgeworth expansion up to O(1/n). Conditions for the normal-theory asymptotic biases and variances to hold under nonnormality were derived with respect to the parameter values and the weighted sum of the cumulants of associated variables. The condition for the cumulants indicates a compensatory effect to yield the robust normal-theory lower-order cumulants. Simulations were performed to see the usefulness of the formulas of the asymptotic expansions using the model with the asymptotic robustness under nonnormality, which showed that the approximations by Edgeworth expansions were satisfactory.     

Accuracy Analysis of the Percentile Method for Estimating Non Normal Manufacturing Quality

We consider asymmetric kernel estimates based on grouped data. We propose an iterated scheme for constructing such an estimator and apply an iterated smoothed bootstrap approach for bandwidth selection. We compare our approach with competing methods in estimating actuarial loss models using both simulations and data studies. The simulation results show that with this new method, the estimated density from grouped data matches the true density more closely than with competing approaches.     

Estimation of a discriminant function from a mixture of two inverse Weibull distributions

The classification of a random variable based on a mixture can be meaningfully discussed only if the class of all finite mixtures is identifiable. In this paper, we find the maximum-likelihood estimates of the parameters of the mixture of two inverse Weibull distributions by using classified and unclassified observations. Next, we estimate the nonlinear discriminant function of the underlying model. Also, we calculate the total probabilities of misclassification as well as the percentage bias. In addition, we investigate the performance of all results through a series of simulation experiments by means of relative efficiencies. Finally, we analyse some simulated and real data sets through the findings of the paper.     

Bias Reduction through First-order Mean Correction, Bootstrapping and Recursive Mean Adjustment

Standard methods of estimation for autoregressive models are known to be biased in finite samples, which has implications for estimation, hypothesis testing, confidence interval construction and forecasting. Three methods of bias reduction are considered here: first-order bias correction, FOBC, where the total bias is approximated by the O(T^-1) bias; bootstrapping; and recursive mean adjustment, RMA. In addition, we show how first-order bias correction is related to linear bias correction. The practically important case where the AR model includes an unknown linear trend is considered in detail. The fidelity of nominal to actual coverage of confidence intervals is also assessed. A simulation study covers the AR(1) model and a number of extensions based on the empirical AR(p) models fitted by Nelson & Plosser (1982). Overall, which method dominates depends on the criterion adopted: bootstrapping tends to be the best at reducing bias, recursive mean adjustment is best at reducing mean squared error, whilst FOBC does particularly well in maintaining the fidelity of confidence intervals.     

利用最小化代价函数方法实现多窗口谱分析

利用最小化代价函数的方法推导了一种谱分析的多窗口。性能分析的结果表明,此方法与离散长球序列多窗口谱分析方法具有相当的估计偏差与方差性能。与离散长球序列多窗口相比,此方法得到的多窗口具有直观的解析表达式而无须求解矩阵的特征分解问题,因而具有较小的计算量。通过对离散白噪声与ＡＲ过程进行的多窗口谱分析对比实验,验证了此方法的有效性和正确性。     

Perceptions of Intimate Partner Violence,Age, and Self-Enhancement Bias

Educational programs in human service professions such as social work, criminal justice, psychology, and public administration stress the importance of recognizing domestic/intimate partner violence as well as elder abuse. Students' abilities to recognize domestic violence in older couples have not been well-investigated. In this study, three vignettes were developed (Pat and Lee at age 75, Pat and Lee at age 30, Imagine yourself with Lee at age 75) in which intimate partner violence was perpetrated by the character Lee. Twenty-five items followed each vignette. When the variables of educational standing (graduate/undergraduate), ethnicity, and academic major were controlled, there were significant differences between the vignettes in 14 of 25 items (General Linear Model, F = 1.552, df = 50, p = .012). More than three out of four respondents for each vignette identified this as domestic violence and believed there was potential for serious harm. However, respondents were less likely to believe that a 75-year-old partner would know when to terminate a relationship in which there was intimate partner violence. Respondents who were asked to imagine themselves with Lee at 75 were likely to perceive Lee as more dangerous than respondents for the other vignettes. Implications are considered for educators.     

Moral Disengagement in the Corporate World

We analyze mechanisms of moral disengagement used to eliminate moral consequences by industries whose products or production practices are harmful to human health. Moral disengagement removes the restraint of self-censure from harmful practices. Moral self-sanctions can be selectively disengaged from harmful activities by investing them with socially worthy purposes, sanitizing and exonerating them, displacing and diffusing responsibility, minimizing or disputing harmful consequences, making advantageous comparisons, and disparaging and blaming critics and victims. Internal industry documents and public statements related to the research activities of these industries were coded for modes of moral disengagement by the tobacco, lead, vinyl chloride (VC), and silicosis-producing industries. All but one of the modes of moral disengagement were used by each of these industries. We present possible safeguards designed to protect the integrity of research.     

Moving Toward Spiritual Competency

Abstract

In spite of growing interest in spirituality and religion, and the prominent position given to religion in social work's educational policies and ethical standards, no studies have explored the profession's level of spiritual sensitivity. Accordingly, we examined the visibility of faith groups and their characterizations in 71 influential social work textbooks. This study found that faith groups, in contrast to a number of comparison groups, were essentially invisible as populations worthy of students' direct attention. Further, when faith groups were discussed, they tended to be characterized in a biased, spiritually insensitive manner. To move toward spiritual competency, social work must increase the amount of material devoted to faith groups and ensure that they are depicted in a manner that is consistent with their worldviews.     

A Study of Productive Expenditure Bias in County-level Finance in China

中国县级决策者主要对上级负责 , 追求尽可能高的经济增长率 , 而非居民福利最 大化 , 导致其财政决策偏向生产性支出。基于2067个县(市)2001—2005年财政经济 数据的实证检验,为县级财政生产性支出偏向的存在提供了有力的证据。生产性支出 偏向严重地妨碍公共财政体制的建设和向公共服务型政府的转型。解决这一问题的根 本出路在于逐步增强县级政府满足本地居民需求的激励。

关键词: 财政支出结构 财政支出偏向 县级政府

Being responsible mainly to their superiors, decision-makers at the county level in China pursue the maximum growth rate rather than their residents’ welfare. This leads to a preference for productive expenditure in their fiscal decisions. An empirical test based on 2,067 counties (cities) provides robust evidence for the existence of productive expenditure bias at the county level. Such bias causes a serious impediment to the construction of a system of public finance and the transformation to public service-oriented government in China. An appropriate solution to this problem is to gradually enhance incentives for county- level governments to satisfy the demands of local residents.