Seemingly Unrelated Ridge Regression in Semiparametric ModelsRoozbeh, M.; Arashi, M.; Gasparini, M.
doi: 10.1080/03610926.2010.542859pmid: N/A
This article is concerned with the problem of multicollinearity in the linear part of a seemingly unrelated semiparametric (SUS) model. It is also suspected that some additional non stochastic linear constraints hold on the whole parameter space. In the sequel, we propose semiparametric ridge and non ridge type estimators combining the restricted least squares methods in the model under study. For practical aspects, it is assumed that the covariance matrix of error terms is unknown and thus feasible estimators are proposed and their asymptotic distributional properties are derived. Also, necessary and sufficient conditions for the superiority of the ridge-type estimator over the non ridge type estimator for selecting the ridge parameter K are derived. Lastly, a Monte Carlo simulation study is conducted to estimate the parametric and nonparametric parts. In this regard, kernel smoothing and cross validation methods for estimating the nonparametric function are used.
Testing Goodness-of-Fit for Exponential Distribution Based on Cumulative Residual EntropyBaratpour, S.; Rad, A.
Habibi
doi: 10.1080/03610926.2010.542857pmid: N/A
Testing exponentiality has long been an interesting issue in statistical inferences. In this article, we introduce a new measure of distance between two distributions that is similar Kullback–Leibler divergence, but using the distribution function rather than the density function. This new measure is based on the cumulative residual entropy. Based on this new measure, a consistent test statistic for testing the hypothesis of exponentiality against some alternatives is developed. Critical values for various sample sizes determined by means of Monte Carlo simulations are presented for the test statistics. Also, by means of Monte Carlo simulations, the power of the proposed test under various alternative is compared with that of other tests. Finally, we found that the power differences between the proposed test and other tests are not remarkable. The use of the proposed test is shown in an illustrative example.
Some Results on Reciprocal Subtangent in the Context of Weighted ModelsSunoj, S. M.; Sreejith, T. B.
doi: 10.1080/03610926.2010.542858pmid: N/A
Recently, reciprocal subtangent has been used as a useful tool to describe the behaviour of a density curve. Motivated by this, in the present article we extend the concept to the weighted models. Characterization results are proved for models viz. gamma, Rayleigh, equilibrium, residual lifetime, and proportional hazards. An identity under weighted distribution is also obtained when the reciprocal subtangent takes the form of a general class of distributions. Finally, an extension of reciprocal subtangent for the weighted models in the bivariate and multivariate cases are introduced and proved some useful results.
The Self-Weighting ModelGarcia, Edel
doi: 10.1080/03610926.2011.654037pmid: N/A
In this brief article, we present the Self-Weighting Model (SWM), a new weighting model for statistical analysis. SWM allows within/between-set comparisons, producing estimates with a discriminatory power not found through current weighting strategies. The model is applicable to a wide range of statistical problems for which conditional weighted means are required.