A Note on the Upper Bound to Variance of the Sample Extreme from a Finite Population

We construct a new upper bound for the variance of the sample minimum and maximum in the case of a simple random sample drawn without replacement. The bound is optimal in the form provided. Similar bounds are shown for the other order statistics.     

Some distributions arising from faulty inspection with multitype defectives,and an application to grading

Continuing their investigation of distributions arising from faulty and "incomplete" inspection from a finite popu-lation (Johnson et al. (1980), Comm. Statist.A9 917-922, Kotz and Johnson, ibid, All, 1997-2016) the authors investigate cases when inspection is not restricted to a single type of defect. Applications to grading problems, when grading is based on sets of defects found by sampling inspections, are indicated.     

On the distribution of the estimated mean from nonstandard mixtures of distributions

The distribution of the estimated mean of the nonstandard mixture of distributions that has a discrete probability mass at zero and a gamma distribution for positive values is derived. Furthermore, for the studied nonstandard mixture of distributions, the distribution of the standardized statistic (estimator - true mean)/standard deviation of estimator is derived. The results are used to study the accuracy of the confidence interval for the mean based on a large sample approximation. Quantiles for the standardized statistic are also calculated.     

Tests for Serial Dependence and Other Specification Analysis in Models of Markets in Disequilibrium

The assumption of serial independence of disturbances is the starting point of most of the work done on analyzing market disequilibrium models. We derive tests for serial dependence given normality and homoscedasticity using the Lagrange multiplier (LM) test principle. Although the likelihood function under serial dependence is very complicated and involves multiple integrals of dimensions equal to the sample size, the test statistic we obtain through the LM principle is very simple. We apply the test to the housing-start data of Fair and Jaffee (1972) and study its finite sample properties through simulation. The test seems to perform quite well in finite samples in terms of size and power. We present an analysis of disequilibrium models that assumes that the disturbances are logistic rather than normal. The relative performances of these distributions are investigated by simulation.     

Editorial

This paper uses the empirical characteristic function (ECF) procedure to estimate the parameters of mixtures of normal distributions. Since the characteristic function is uniformly bounded, the procedure gives estimates that are numerically stable. It is shown that, using Monte Carlo simulation, the finite sample properties of th ECF estimator are very good, even in the case where the popular maximum likelihood estimator fails to exist. An empirical application is illustrated using the monthl excess return of the Nyse value-weighted index.     

Block–Savits Characterization and Star Ordering of Exponential Mixtures

Block and Savits (1980 Block , H. W. , Savits , T. H. ( 1980 ). Laplace transforms for classes of life distributions . Ann. Probab. 8 : 465 – 474 .[Crossref], [Web of Science ®] , [Google Scholar]) established a characterization of life distributions using the Laplace transform. In this article, we remark that one of the necessary conditions to be IFRA distribution is equivalent to the star ordering of exponential mixtures. It leads to the definition of two new classes of life distributions, called LIFR and LIFRA, and their dual classes: LDFR and LDFRA. It occurs that these classes have many useful aging properties and preserve known reliability operations. Properties of the classes are studied and relations with known classes are established.     

The REBMIX Algorithm for the Multivariate Finite Mixture Estimation

The article extends the REBMIX to multivariate data. Random variables may follow normal, lognormal, or Weibull parametric families and should be independent within components. The initial weights and component parameters are not required. Preprocessing of observations folows the histogram, Parzen window, or k-nearest neighbor approach. The number of components, weights, and component parameters are gained iteratively by using information measures of the distance, such as the total of positive relative deviations and the information criterion. The number of classes or the number of the nearest neighbors can be optimized, as well. The REBMIX software is available on http://www.fs.uni-lj.si/lavek.     

Variable selection in finite mixture of semi-parametric regression models

Abstract

In this paper we are concerned with variable selection in finite mixture of semiparametric regression models. This task consists of model selection for non parametric component and variable selection for parametric part. Thus, we encountered separate model selections for every non parametric component of each sub model. To overcome this computational burden, we introduced a class of variable selection procedures for finite mixture of semiparametric regression models using penalized approach for variable selection. It is shown that the new method is consistent for variable selection. Simulations show that the performance of proposed method is good, and it consequently improves pervious works in this area and also requires much less computing power than existing methods.     

Reliabilities of a single-unit system with multi-phased missions

ABSTRACT

Phased-mission systems (PMS) can be widely found in a lot of practical application areas. Reliability evaluations and analysis for this kind of systems become important issues. The reliability of PMS is typically defined as the probability that the system successfully accomplishes the missions of all phases. However, the k-out-of-n system success criterion for PMS has not been investigated. In this paper, according to this criterion, we develop two new models, which are static and dynamic, respectively. The assumptions for these two models are described in detail as well. The system reliabilities for both models are presented for the first time by employing finite Markov chain imbedding approach (FMCIA). In terms of FMCIA, we define different state spaces for the two models, and transition probability matrices are obtained. Then some numerical examples are given to illustrate the application of FMCIA. Finally, some discussions are made and conclusions are summarized.     

Step partially accelerated life tests under finite mixture models

In this paper, step partially accelerated life tests are considered when the lifetime of an item under use condition follows a finite mixture of distributions. The analysis is performed when each of the components follows a general class of distributions, which includes, among others, the Weibull, compound Weibull (or three-parameter Burr type XII), power function, Gompertz and compound Gompertz distributions. Based on type-I censoring, the maximum likelihood estimates (MLEs) of the mixing proportions, scale parameters and acceleration factor are obtained. Special attention is paid to a mixture of two exponential components. Simulation results are obtained to study the precision of MLEs.