Goodness of fit for the inverse Gaussian distribution

For testing the fit of the inverse Gaussian distribution with unknown parameters, the empirical distribution-function statistic A² is studied. Two procedures are followed in constructing the test statistic; they yield the same asymptotic distribution. In the first procedure the parameters in the distribution function are directly estimated, and in the second the distribution function is estimated by its Rao-Blackwell distribution estimator. A table is given for the asymptotic critical points of A². These are shown to depend only on the ratio of the unknown parameters. An analysis is provided of the effect of estimating the ratio to enter the table for A². This analysis enables the proposal of the complete operating procedure, which is sustained by a Monte Carlo study.     

Robust confidence interval for a residual standard deviation

The residual standard deviation of a general linear model provides information about predictive accuracy that is not revealed by the multiple correlation or regression coefficients. The classic confidence interval for a residual standard deviation is hypersensitive to minor violations of the normality assumption and its robustness does not improve with increasing sample size. An approximate confidence interval for the residual standard deviation is proposed and shown to be robust to moderate violations of the normality assumption with robustness to extreme non-normality that improves with increasing sample size.     

薪酬策略与公司竞争战略的匹配机制研究

文章通过对185家企业的实地调研,比较了薪酬策略和公司竞争战略的两种匹配模式:中介匹配和调节匹配.否定了中介匹配模式,支持了调节匹配模式,从而解决了薪酬管理研究中长期存在的争议:两者到底是如何匹配的.公司竞争战略并不能自发地促生出相应的薪酬策略,而是需要相关部门较为独立地开发出相应的薪酬策略与之配套.最后,根据研究发现并提出了一些增进薪酬策略与公司竞争战略匹配度的建议.     

Fit and Skill in Employee Selection: Insights from a Study of Headhunters

We draw upon evidence from a qualitative study of headhunters to provide insights into the character and importance of candidate fit and skill for the selection of a broad range of white-collar employees. Headhunters suggest that the fit of a job candidate is assessed at two levels, one corresponding with a general compatibility with organization-level norms, culture, and strategy, the other corresponding more closely with traits and characteristics of the person or persons with whom the job candidate actually interviews. Skill—a factor which is largely neglected by those who tout the importance of fit—also plays an important and independent role in employee selection. Stalls that influence the selection of employees from a pool of candidates tend to be highly specific if not idiosyncratic, and take the form of what headhunters call hot buttons. We conclude by discussing the conceptualization, causes, and implications of fit; we also consider how the importance of fit and hot buttons challenges the explanatory logic of standard accounts of labor-market success.     

The Management of Violence in a Conflict Organization: The Case of the Abu Sayyaf

The Abu Sayyaf is an organization which has left a trail of mayhem and murder in the southern Philippines for more than a decade. It has gained international notoriety through several high profile mass kidnappings. This article looks at how the Abu Sayyaf has managed to survive and at times prosper despite the state's efforts to eradicate it. By using organizational analysis the paper demonstrates how the Abu Sayyaf has developed structures and processes which make it such a deadly force. The organization has succeeded in gaining considerable fit with the environment in which it operates.     

Semi-doubly optimal concentric circles fitting with presence of heteroscedasticity

Previous studies focus on homogeneous and isotropic assumptions about the noisy data. Many methods have been developed recently for fitting concentric circles to data. In this paper, these statistical assumptions have been relaxed. To the best of our knowledge, only one iterative method has been recently developed. Due to its complexity, no such algorithm is available to compute the reliable maximum likelihood estimator (MLE). Accordingly, we have developed four new methods that outperform the existing methods including the orthogonal distance regression (ODR). We also discuss which of these methods is superior according to the four principles: statistical efficiency, accuracy, robustness, and computational efficiency. Numerical experiments on synthetic and real images have been conducted to validate our findings.     

Correlated random effects regression analysis for a log-normally distributed variable

In regression analysis, it is assumed that the response (or dependent variable) distribution is Normal, and errors are homoscedastic and uncorrelated. However, in practice, these assumptions are rarely satisfied by a real data set. To stabilize the heteroscedastic response variance, generally, log-transformation is suggested. Consequently, the response variable distribution approaches nearer to the Normal distribution. As a result, the model fit of the data is improved. Practically, a proper (seems to be suitable) transformation may not always stabilize the variance, and the response distribution may not reduce to Normal distribution. The present article assumes that the response distribution is log-normal with compound autocorrelated errors. Under these situations, estimation and testing of hypotheses regarding regression parameters have been derived. From a set of reduced data, we have derived the best linear unbiased estimators of all the regression coefficients, except the intercept which is often unimportant in practice. Unknown correlation parameters have been estimated. In this connection, we have derived a test rule for testing any set of linear hypotheses of the unknown regression coefficients. In addition, we have developed the confidence ellipsoids of a set of estimable functions of regression coefficients. For the fitted regression equation, an index of fit has been proposed. A simulated study illustrates the results derived in this report.     

Community-Based Services and Depression from Person-Environment Fit Perspective: Focusing on Functional Impairments and Living Alone

ABSTRACT

Guided by the Person-Environment Fit perspective, we investigated the extent to which personal and environmental factors influence depression among community-dwelling adults. The data came from the special section about community-based service utilization in the 2012 Health and Retirement Study (N=1,710). Although community-based service was not significantly associated with depression after controlling for covariates, respondents with functional limitations and living alone were less likely to be depressed when using community-based services. This study demonstrates the different associations between community-based services and depression depending on personal needs. It discusses the importance of community-based services for aging-in-place policy, particularly among vulnerable populations.     

Model Checks in Inverse Regression Models with Convolution‐Type Operators

Abstract. We consider the problem of testing parametric assumptions in an inverse regression model with a convolution‐type operator. An L ₂ ‐type goodness‐of‐fit test is proposed which compares the distance between a parametric and a non‐parametric estimate of the regression function. Asymptotic normality of the corresponding test statistic is shown under the null hypothesis and under a general non‐parametric alternative with different rates of convergence in both cases. The feasibility of the proposed test is demonstrated by means of a small simulation study. In particular, the power of the test against certain types of alternative is investigated. Finally, an empirical example is provided, in which the proposed methods are applied to the determination of the shape of the luminosity profile of the elliptical galaxy NGC 5017.     

A Diversity Index for Model Space Selection in the Estimation of Benchmark and Infectious Doses via Model Averaging

In chemical and microbial risk assessments, risk assessors fit dose‐response models to high‐dose data and extrapolate downward to risk levels in the range of 1–10%. Although multiple dose‐response models may be able to fit the data adequately in the experimental range, the estimated effective dose (ED) corresponding to an extremely small risk can be substantially different from model to model. In this respect, model averaging (MA) provides more robustness than a single dose‐response model in the point and interval estimation of an ED. In MA, accounting for both data uncertainty and model uncertainty is crucial, but addressing model uncertainty is not achieved simply by increasing the number of models in a model space. A plausible set of models for MA can be characterized by goodness of fit and diversity surrounding the truth. We propose a diversity index (DI) to balance between these two characteristics in model space selection. It addresses a collective property of a model space rather than individual performance of each model. Tuning parameters in the DI control the size of the model space for MA.