Detection of multiple undocumented change-points using adaptive Lasso

The problem of detecting multiple undocumented change-points in a historical temperature sequence with simple linear trend is formulated by a linear model. We apply adaptive least absolute shrinkage and selection operator (Lasso) to estimate the number and locations of change-points. Model selection criteria are used to choose the Lasso smoothing parameter. As adaptive Lasso may overestimate the number of change-points, we perform post-selection on change-points detected by adaptive Lasso using multivariate t simultaneous confidence intervals. Our method is demonstrated on the annual temperature data (year: 1902–2000) from Tuscaloosa, Alabama.     

The Copula Information Criteria

We derive two types of Akaike information criterion (AIC)‐like model‐selection formulae for the semiparametric pseudo‐maximum likelihood procedure. We first adapt the arguments leading to the original AIC formula, related to empirical estimation of a certain Kullback–Leibler information distance. This gives a significantly different formula compared with the AIC, which we name the copula information criterion. However, we show that such a model‐selection procedure cannot exist for copula models with densities that grow very fast near the edge of the unit cube. This problem affects most popular copula models. We then derive what we call the cross‐validation copula information criterion, which exists under weak conditions and is a first‐order approximation to exact cross validation. This formula is very similar to the standard AIC formula but has slightly different motivation. A brief illustration with real data is given.     

A new model selection procedure based on dynamic quantile regression

In this article, we propose a novel robust data-analytic procedure, dynamic quantile regression (DQR), for model selection. It is robust in the sense that it can simultaneously estimate the coefficients and the distribution of errors over a large collection of error distributions even those that are heavy-tailed and may not even possess variances or means; and DQR is easy to implement in the sense that it does not need to decide in advance which quantile(s) should be gathered. Asymptotic properties of related estimators are derived. Simulations and illustrative real examples are also given.     

Some notes on robust sure independence screening

Sure independence screening (SIS) proposed by Fan and Lv [4 J. Fan and R. Li, Variable selection via nonconcave penalized likelihood and its oracle properties, J. Amer. Statist. Assoc. 96 (2001), pp. 1348–1360. doi: 10.1198/016214501753382273[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]] uses marginal correlations to select important variables, and has proven to be an efficient method for ultrahigh-dimensional linear models. This paper provides two robust versions of SIS against outliers. The two methods, respectively, replace the sample correlation in SIS with two robust measures, and screen variables by ranking them. Like SIS, the proposed methods are simple and fast. In addition, they are highly robust against a substantial fraction of outliers in the data. These features make them applicable to large datasets which may contain outliers. Simulation results are presented to show their effectiveness.     

Supplier Evaluation and Selection Based on Stochastic Dominance: A Quality-Based Approach

Quality has become a major business strategy such that organizations with successful improvement of their products quality can gain productivity, enhance market penetration, achieve great profitability, and strongly sustain their competitive advantages. The quality of materials received from suppliers determines not only the quality of assembled products but also satisfaction and loyalty of downstream customers. In this article, we employ decision-making processes of the stochastic dominance on the basis of loss-based capability indices to compare certain potential suppliers. In view of compared results of the first-order and second-order stochastic dominances, each supplier is categorized as a superior supplier, weakly superior supplier, strongly non dominated supplier, or non dominated supplier. We develop a general computational procedure to select the preferable suppliers in an analytical way. To assist decision-makers in selecting preferable suppliers, quantile-quantile plots of loss-based capability indices presenting the results of the first-order stochastic dominance of the indices’ estimators are developed so that they can simultaneously visualize pair-wise comparisons of the suppliers and make appropriate decisions. Finally, a practical example invoking the stochastic dominance using the loss-based capability indices to carry out the quality-based supplier evaluation and selection is presented to demonstrate the applicability of our proposed methodology.     

Adaptive Percolation Using Subjective Likelihoods

A phenomenon that I call “adaptive percolation” commonly arises in biology, business, economics, defense, finance, manufacturing, and the social sciences. Here one wishes to select a handful of entities from a large pool of entities via a process of screening through a hierarchy of sieves. The process is not unlike the percolation of a liquid through a porous medium. The probability model developed here is based on a nested and adaptive Bayesian approach that results in the product of beta-binomial distributions with common parameters. The common parameters happen to be the observed data. I call this the percolated beta-binomial distribution . The model turns out to be a slight generalization of the probabilistic model used in percolation theory. The generalization is a consequence of using a subjectively specified likelihood function to construct a probability model. The notion of using likelihoods for constructing probability models is not a part of the conventional toolkit of applied probabilists. To the best of my knowledge, a use of the product of beta-binomial distributions as a probability model for Bernoulli trials appears to be new. The development of the material of this article is illustrated via data from the 2009 astronaut selection program, which motivated this work.     

矩约束模型的最优矩条件选取方法

大量经济、金融以及企业管理等领域研究对象的行为特征可以通过矩约束模型来刻画。然而,该模型中参数的估计对矩条件的选取非常敏感。如何选取最优的矩条件,进而得到更准确的参数估计和更精确的统计推断,是实证研究面临的重要问题。本文从估计量均方误差（MSE）最小的角度,研究了一般矩约束模型两步有效广义矩（GMM）估计的最优矩条件选取方法。首先,利用迭代的方法,推导出两步有效GMM估计的高阶MSE,然后通过Nagar分解,求出了两步有效GMM估计量的近似MSE。接着,基于近似MSE表达式,给出了两步有效GMM估计矩条件选取准则的一般理论,即定义了最优的矩条件,提出了两步有效GMM估计的最优矩条件选取准则,并证明了选取准则的渐近有效性。模拟结果表明,本文提出的矩条件选取方法能够很好地改善两步有效GMM估计量的有限样本表现,降低估计量的有效样本偏差。本研究为实证研究中面临的矩条件选择问题提供了理论依据。     

IT外包中企业绩效的影响因素实证研究

在已有文献的基础上,识别了关系治理的三个维度：良好冲突处理、相互依赖和关系规范,并从关系治理角度出发,构建了IT外包企业绩效影响因素的概念模型。通过对北京、上海和西安三地20多家外包企业进行问卷调查,利用结构方程模型进行实证分析。实证结果表明：信任、承诺和知识共享对企业绩效有显著的正向影响,其中信任和承诺对企业绩效的影响既有直接的影响又有间接的影响,知识共享对企业绩效有直接的影响;信任、承诺和知识共享具有很强的中介作用,良好冲突处理和相互依赖以信任、承诺和知识共享为中间变量间接影响企业绩效,关系规范以承诺和知识共享为中间变量间接影响企业绩效。     

Using a genetic algorithm-based RAROC model for the performance and persistence of the funds

Assisting fund investors in making better investment decisions when faced with market climate change is an important subject. For this purpose, we adopt a genetic algorithm (GA) to search for an optimal decay factor for an exponential weighted moving average model, which is used to calculate the value at risk combined with risk-adjusted return on capital (RAROC). We then propose a GA-based RAROC model. Next, using the model we find the optimal decay factor and investigate the performance and persistence of 31 Taiwanese open-end equity mutual funds over the period from November 2006 to October 2009, divided into three periods: November 2006–October 2007, November 2007–October 2008, and November 2008–October 2009, which includes the global financial crisis. We find that for three periods, the optimal decay factors are 0.999, 0.951, and 0.990, respectively. The rankings of funds between bull and bear markets are quite different. Moreover, the proposed model improves performance persistence. That is, a fund's past performance will continue into the future.     

Variable selection for multivariate generalized linear models

Generalized linear models (GLMs) are widely studied to deal with complex response variables. For the analysis of categorical dependent variables with more than two response categories, multivariate GLMs are presented to build the relationship between this polytomous response and a set of regressors. Traditional variable selection approaches have been proposed for the multivariate GLM with a canonical link function when the number of parameters is fixed in the literature. However, in many model selection problems, the number of parameters may be large and grow with the sample size. In this paper, we present a new selection criterion to the model with a diverging number of parameters. Under suitable conditions, the criterion is shown to be model selection consistent. A simulation study and a real data analysis are conducted to support theoretical findings.