Koul, Susarla and Van Ryzin (1981, Ann. Statist. 9, 1276-1288) proposed a generalization of the ordinary least squares estimator in linear models with censored data. This paper uses counting processes and martingale techniques to provide a proof of the asymptotic normality of the estimator. A...

Comparisons are made between the maximal expected gain of a prophet and the maximal expected reward of an ordinary player observing a sequence of uniformly bounded i.i.d. random variables when the future is discounted. The player uses pure threshold stopping times which are asymptotically...

This paper obtains asymptotic representations of the regression quantiles and the regression rank-scores processes in linear regression setting when the errors are a function of Gaussian random variables that ale stationary and long range dependent. These representations are then used to obtain...

Asymptotics of M-estimators of the regression coefficients in linear models (both scale-variant and scale-invariant) when the number of regression coefficients tends to infinity as the sample size increases are investigated The main purpose of this study is to establish the asymptotic properties...

Limit theorems are derived for probability measures of random configurations over graphs which are used as prior distributions in pattern theory. For one-dimensional graphs, these limits can be viewed as distributions of certain stochastic processes, while in higher dimensions the limits will in...

Nonparametric versions of Wilks' theorem are proved for empirical likelihood estimators of slope and mean parameters for a simple linear regression model. They enable us to construct empirical likelihood confidence intervals for these parameters. The coverage errors of these confidence intervals...

C. D. Hardin, Jr., G. Samorodnitsky, and M. S. Taqqu (1991,Ann. Appl. Probab. 1 582-612) have shown that the regression E[Y X = x] is typically asymptotically linear when (X, Y) is an [alpha]-stable random vector with [alpha] 2. We provide necessary and sufficient conditions for asymptotic...

The distribution of a nonnegative linear combination of mean square matrices associated with a balanced multivariate mixed model can be approximated by a central Wishart distribution. A necessary and sufficient condition for this approximation to be exact is presented. A multivariate test...

Consider the independent Wishart matrices S1 W([Sigma] + [lambda][Theta],q1) and S2 W([Sigma], q2) where [Sigma] is an unknown positive definite (p.d.) matrix, [Theta] is an unknown nonnegative definite (n.n.d.) matrix, and [lambda] is a known positive scalar. For the estimation of [Theta], a...

Our concern in this paper is a detection of a change in regression coefficients of a linear model. First, we examine the null and alternative distributions of the likelihood ratio statistic and study its asymptotic behavior. We then propose analytic approximations for the p-value and power of...