The theory of Bayesian least squares is developed for a general and more tangible notion of conjugacy than in models which make the more conventional assumption of normality. This paper is primarily concerned with extending the results of classical conjugate normal-normal Bayesian analysis to...

For a discrete-time Markov chain with finite state space {1, ..., r} we consider the joint distribution of the numbers of visits in states 1, ..., r-1 during the firstNsteps or before theNth visit tor. From the explicit expressions for the corresponding generating functions we obtain...

Local confidence intervals for regression function with binary response variable are constructed. These intervals are based on both theoretical and "plug-in" normal asymptotic distribution of a usual statistic. In the plug-in approach, two ways of estimating bias are proposed; for them we obtain...

This paper investigates a new family of statistics based on Burbea-Rao divergence for testing goodness-of-fit. Under the simple and composite null hypotheses the asymptotic distribution of these tests is shown to be chi-squared. For composite hypothesis, the unspecified parameters are estimated...

Consider the multivariate linear model for the random matrixYn-p~MN(XB, V[circle times operator][Sigma]), whereBis the parameter matrix,Xis a model matrix, not necessarily of full rank, andV[circle times operator][Sigma] is annp-nppositive-definite dispersion matrix. This paper presents...

One of the most useful tools for handling multivariate distributions with givenunivariatemarginals is the copula function. Using it, any multivariate distribution function can be represented in a way that emphasizes the separate roles of the marginals and of the dependence structure. Liet...

In this paper we consider the TJW product-limit estimatorFn(x) of an unknown distribution functionFwhen the data are subject to random left truncation and right censorship. An almost sure representation of PL-estimatorFn(x) is derived with an improved error bound under some weaker assumptions....

We develop procedures for testing for changes in the mean of multivariatem-dependent stationary processes. Several test statistics are considered and corresponding limit theorems are derived. These include functional and Darling-Erdos type limit theorems. The tests are shown to be consistent...

It is well known that the maximum likelihood estimates (MLEs) of a multivariate normal distribution from incomplete data with a monotone pattern have closed-form expressions and that the MLEs from incomplete data with a general missing-data pattern can be obtained using the...

The paper presents some permutation test procedures for multivariate location. The tests are based on projected univariate versions of multivariate data. For one-sample cases, the tests are affine invariant and strictly distribution-free for the symmetric null distribution with elliptical...