This article considers the problem of testing and interval estimation of the reliability parameter P (Y1 > Y2), where Y1 and Y2 are independent normal random variables with unknown means and variances. A new approximate method is proposed, and is compared with two existing approximate methods and a generalized variable method. Simulation studies indicate that the sizes of the existing approxima...

In this paper, we are concerned with how to select significant variables in semiparametric modeling. Variable selection for semiparametric regression models consists of two components: model selection for nonparametric components and selection of significant variables for the parametric portion. Thus, semiparametric variable selection is much more challenging than parametric variable selection ...

The Weibull distribution is a very applicable model for lifetime data. In this paper, we have investigated inference on the parameters of Weibull distribution based on record values. We first propose a simple and exact test and a confidence interval for the shape parameter. Then, in addition to a generalized confidence interval, a generalized test variable is derived for the scale parameter whe...

The main approach to handle endogeneity in econometrics has been instrumental variable estimator (IVE). An instrument m requires E(mu) = 0 where u is an error term, and if E(mu) 6= 0 is likely, then m gets discarded. But when E(mu) 6= 0, fairly often one has a good idea on the sign of E(mu), and it will be a waste to toss out the sign information. This paper shows how to make use of the informa...

Discriminative latent variable models (LVM) are frequently applied to various visual recognition tasks. In these systems the latent (hidden) variables provide a formalism for modeling structured variation of visual features. Conventionally, latent variables are defined on the variation of the foreground (positive) class. In this work we augment LVMs to include negative latent variables correspo...

Many problems require the selection of a subset variables from full set optimization variables. The computational complexity an exhaustive search over all possible subsets is, however, prohibitively expensive, necessitating more efficient but potentially suboptimal strategies. We focus on sparse variable for generalized Rayleigh quotient and eigenvalue problems. Such often arise in signal proce...