Jinook Jeong  Byung Ho Yoon 《统计学通讯:模拟与计算》2013,42(2):315-321

Hausman test is popularly used to examine the endogeneity of explanatory variables in a regression model. To derive a well-defined asymptotic distribution of Hausman test, the correlation between the instrumental variables and the error term needs to converge to zero. However, it is possible that there remains considerable correlation in finite samples between the instruments and the error, even though their correlation eventually converges to zero. This article investigates the potential problem that such “pseudo-exogenous” instruments may create. We show that the performance of Hausman test is deteriorated when the instruments are asymptotically exogenous but endogenous in finite samples, through Monte Carlo simulations.  相似文献   

    

Seyed Taghi Akhavan Niaki  Paravaneh Jahani 《Journal of applied statistics》2013,40(6):1301-1318

Since multi-attribute control charts have received little attention compared with multivariate variable control charts, this research is concerned with developing a new methodology to employ the multivariate exponentially weighted moving average (MEWMA) charts for m-attribute binomial processes; the attributes being the number of nonconforming items. Moreover, since the variable sample size and sampling interval (VSSI) MEWMA charts detect small process mean shifts faster than the traditional MEWMA, an economic design of the VSSI MEWMA chart is proposed to obtain the optimum design parameters of the chart. The sample size, the sampling interval, and the warning/action limit coefficients are obtained using a genetic algorithm such that the expected total cost per hour is minimized. At the end, a sensitivity analysis has been carried out to investigate the effects of the cost and the model parameters on the solution of the economic design of the VSSI MEWMA chart.  相似文献   

    

Stephen G. Walker 《统计学通讯:模拟与计算》2013,42(3):520-527

This article presents a novel and simple approach to the estimation of a marginal likelihood, in a Bayesian context. The estimate is based on a Markov chain output which provides samples from the posterior distribution and an additional latent variable. It is the mean of this latent variable which provides the estimate for the value of the marginal likelihood.  相似文献   

    

《Journal of Statistical Computation and Simulation》2012,82(18):3613-3627

Streaming feature selection is a greedy approach to variable selection that evaluates potential explanatory variables sequentially. It selects significant features as soon as they are discovered rather than testing them all and picking the best one. Because it is so greedy, streaming selection can rapidly explore large collections of features. If significance is defined by an alpha investing protocol, then the rate of false discoveries will be controlled. The focus of attention in variable selection, however, should be on fit rather than hypothesis testing. Little is known, however, about the risk of estimators produced by streaming selection and how the configuration of these estimators influences the risk. To meet these needs, we provide a computational framework based on stochastic dynamic programming that allows fast calculation of the minimax risk of a sequential estimator relative to an alternative. The alternative can be data driven or derived from an oracle. This framework allows us to compute and contrast the risk inflation of sequential estimators derived from various alpha investing rules. We find that a universal investing rule performs well over a variety of models and that estimators allowed to have larger than conventional rates of false discoveries produce generally smaller risk.  相似文献   

    

《统计学通讯:理论与方法》2012,41(24):6098-6118

Abstract

Time averaging has been the traditional approach to handle mixed sampling frequencies. However, it ignores information possibly embedded in high frequency. Mixed data sampling (MIDAS) regression models provide a concise way to utilize the additional information in high-frequency variables. In this paper, we propose a specification test to choose between time averaging and MIDAS models, based on a Durbin-Wu-Hausman test. In particular, a set of instrumental variables is proposed and theoretically validated when the frequency ratio is large. As a result, our method tends to be more powerful than existing methods, as reconfirmed through the simulations.  相似文献   

    

Matt P. Wand  John T. Ormerod 《Stat》2012,1(1):31-41

The agéd number theoretic concept of continued fractions can enhance certain Bayesian computations. The crux of this claim is due to continued fraction representations of numerically challenging special function ratios that arise in Bayesian computing. Continued fraction approximation via Lentz's Algorithm often leads to efficient and stable computation of such quantities. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

    

Ting Chen  Chong Zhang  Xia Chen  Liqing Li 《Statistical Analysis and Data Mining》2009,1(5):287-295

The relationship between yarn properties, fabric parameters, and shear stiffness of worsted fabrics is modeled using the soft computing technique. Because of the small number of samples, the artificial neural network model to be established must be a small‐scale one. Therefore, this soft computing approach includes two stages. First, the yarn properties and fabric parameters are selected by utilizing an input variable selection method, so as to find the most relevant yarn properties and fabric parameters as the input variables to fit the small‐scale artificial neural network model. The first part of this method takes the human knowledge on the shear stiffness of fabrics into account. The second part utilizes a data sensitivity criterion based on a distance method. Second, the artificial neural network model of the relationship between yarn properties, fabric parameters, and shear stiffness of fabrics is established. The results show that the artificial neural network model yields accurate prediction and a reasonably good artificial neural network model can be achieved with relatively few data points by integrating with the input variable selecting method developed in this research. The results also show that there is great potential for this research in the field of computer‐assisted design in textile technology. Copyright © 2009 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 1: 000‐000, 2009  相似文献   

    

Guy Feldman  Anindya Bhadra  Sergey Kirshner 《Stat》2014,3(1):258-272

We consider the problem of feature selection in a high‐dimensional multiple predictors, multiple responses regression setting. Assuming that regression errors are i.i.d. when they are in fact dependent leads to inconsistent and inefficient feature estimates. We relax the i.i.d. assumption by allowing the errors to exhibit a tree‐structured dependence. This allows a Bayesian problem formulation with the error dependence structure treated as an auxiliary variable that can be integrated out analytically with the help of the matrix‐tree theorem. Mixing over trees results in a flexible technique for modelling the graphical structure for the regression errors. Furthermore, the analytic integration results in a collapsed Gibbs sampler for feature selection that is computationally efficient. Our approach offers significant performance gains over the competing methods in simulations, especially when the features themselves are correlated. In addition to comprehensive simulation studies, we apply our method to a high‐dimensional breast cancer data set to identify markers significantly associated with the disease. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

    

Väinö Jääskinen  Jie Xiong  Jukka Corander  Timo Koski 《Scandinavian Journal of Statistics》2014,41(3):639-655

Finite memory sources and variable‐length Markov chains have recently gained popularity in data compression and mining, in particular, for applications in bioinformatics and language modelling. Here, we consider denser data compression and prediction with a family of sparse Bayesian predictive models for Markov chains in finite state spaces. Our approach lumps transition probabilities into classes composed of invariant probabilities, such that the resulting models need not have a hierarchical structure as in context tree‐based approaches. This can lead to a substantially higher rate of data compression, and such non‐hierarchical sparse models can be motivated for instance by data dependence structures existing in the bioinformatics context. We describe a Bayesian inference algorithm for learning sparse Markov models through clustering of transition probabilities. Experiments with DNA sequence and protein data show that our approach is competitive in both prediction and classification when compared with several alternative methods on the basis of variable memory length.  相似文献   

    

Ramkrishna S. Solanki  Housila P. Singh 《统计学通讯:理论与方法》2014,43(16):3380-3401

This article addresses the problem of estimating the population mean in stratified random sampling using the information of an auxiliary variable. A class of estimators for population mean is defined with its properties under large sample approximation. In particular, various classes of estimators are identified as particular member of the suggested class. It has been shown that the proposed class of estimators is better than usual unbiased estimator, usual combined ratio estimator, usual product estimator, usual regression estimator and Koyuncu and Kadilar (2009 Koyuncu, N., Kadilar, C. (2009). Ratio and product estimators in stratified random sampling. J. Statist. Plan. Infere. 139:2552–2558.[Crossref], [Web of Science ®] , [Google Scholar]) class of estimators. The results have been illustrated through an empirical study.  相似文献