Influence in the Elliptical Modified Ridge Regression

In this article, we present diagnostic methods for the modified ridge regression under elliptical model based on the pseudo-likelihood function. The maximum likelihood estimators of the parameters in the modified ridge elliptical model are given and local influence measures are developed. Finally, illustration of our methodology is given through a numerical example.     

Some ridge regression estimators for the zero-inflated Poisson model

The zero-inflated Poisson regression model is commonly used when analyzing economic data that come in the form of non-negative integers since it accounts for excess zeros and overdispersion of the dependent variable. However, a problem often encountered when analyzing economic data that has not been addressed for this model is multicollinearity. This paper proposes ridge regression (RR) estimators and some methods for estimating the ridge parameter k for a non-negative model. A simulation study has been conducted to compare the performance of the estimators. Both mean squared error and mean absolute error are considered as the performance criteria. The simulation study shows that some estimators are better than the commonly used maximum-likelihood estimator and some other RR estimators. Based on the simulation study and an empirical application, some useful estimators are recommended for practitioners.     

The successive raising estimator and its relation with the ridge estimator

The raised estimators are used to reduce collinearity in linear regression models by raising a column in the experimental data matrix which may be nearly linear with the other columns. The raising procedure has two components, namely stretching and rotating, which we can analyze separately. We give the relationship between the raised estimators and the classical ridge estimators. Using a case study, we show how to determine the perturbation parameter for the raised estimators by controlling the amount of precision to be retained in the original data.     

On the restricted almost unbiased two-parameter estimator in linear regression model

Özkale and Kaçiranlar introduced the restricted two-parameter estimator (RTPE) to deal with the well-known multicollinearity problem in linear regression model. In this paper, the restricted almost unbiased two-parameter estimator (RAUTPE) based on the RTPE is presented. The quadratic bias and mean-squared error of the proposed estimator is discussed and compared with the corresponding competitors in literatures. Furthermore, a numerical example and a Monte Carlo simulation study are given to explain some of the theoretical results.     

Unlocking Doors: Providing MSW Programs and Students with Educational “Keys” to Social Justice

ABSTRACT

How can social work educators identify what constitutes social justice as a practice, as a social work stance? How can we teach our students to recognize this stance, to work toward it, to practice it, and to live it? Symbolic interactionist Erving Goffman's concepts of keys and keying, as underscored in his work Frame Analysis, provide useful tools for helping students to recognize the value of social justice within social work educational encounters and to apply this value when they enter the field. The concepts of keys and keying can also help programs to assess and amplify their commitments to social justice.     

国外网络信息的发展与应用

美国具有强大的网络信息优势,在网络信息的发展趋势和应用方面也代表了国外网络信息的发展方向;日本在网络信息的发展和应用方面一直紧跟美国走在世界前列。以美国和日本为代表分析了发达国家信息化发展的社会环境和政策措施,指出了发展中国家如何面对机会和挑战,发展本国的信息化产业。     

Variable selection for high-dimensional generalized linear models with the weighted elastic-net procedure

High-dimensional data arise frequently in modern applications such as biology, chemometrics, economics, neuroscience and other scientific fields. The common features of high-dimensional data are that many of predictors may not be significant, and there exists high correlation among predictors. Generalized linear models, as the generalization of linear models, also suffer from the collinearity problem. In this paper, combining the nonconvex penalty and ridge regression, we propose the weighted elastic-net to deal with the variable selection of generalized linear models on high dimension and give the theoretical properties of the proposed method with a diverging number of parameters. The finite sample behavior of the proposed method is illustrated with simulation studies and a real data example.     

出租车新运营模式下的LED广告精准投放策略

出租车LED广告是出租车广告的重要形式,其广告效果与投放策略息息相关。出租车在传统运营模式下,出租车广告投放策略比较粗放,没有考虑消费者的差异化需求。随着打车软件应用的普及,出租车运营模式发生了根本性改变,特别是在新的运营模式下积累了大量的运行数据,为出租车广告投放策略的精细化设计和精准投放提供了必要条件。本文针对新的运营模式下的广告精准投放问题进行研究,构建了提取居民出行行为时间特征和空间特征以及出租车司机行为特征的方法,并针对居民出行行为规律和司机的行为模式和偏好,提出了出租车LED广告精准投放的三种策略,即时段定向投放策略,空间定向投放策略和时空定向投放策略。最后,利用某市实际的出租车轨迹数据进行实例分析。本文提出的出租车广告精准投放策略的设计思路和方法对于相关研究具有一定参考价值,提出的精准投放策略对出租车广告投放和管理相关部门的决策也具有实际参考价值。     

Biased discriminant analysis: Evaluation of the optimum probability of misclassification

This article extends the work of DiPillo (1976) on the Biased Minimum x² Rule. The optimum value of k (the biasing factor) Is determined and the true probability of misclassification is found. The proportion improvements reported in the 1976 paper are shown to be conservative. Some suggestions for algorithms to determine the optimal value of k are presented.     

Latent root regression: a biased regression methodology for use with collinear predictor variables

Many different biased regression techniques have been proposed for estimating parameters of a multiple linear regression model when the predictor variables are collinear. One particular alternative, latent root regression analysis, is a technique based on analyzing the latent roots and latent vectors of the correlation matrix of both the response and the predictor variables. It is the purpose of this paper to review the latent root regression estimator and to re-examine some of its properties and applications. It is shown that the latent root estimator is a member of a wider class of estimators for linear models