Linear Estimation in Censored Samples from Multivariate Normal Populations

Iterative maximum-likelihood estimation of the parameters of normal populations from singly and doubly censored samples.

Multivariate Demand: Modeling and Estimation from Censored Sales

Demand modeling and forecasting is important for inventory management, retail assortment and revenue management applications. Current practice focuses on univariate demand forecasting, where models are built separately for each product. However, in many industries there is empirical evidence of correlated product demand. In addition, demand is usually observed in several periods during a sellin...

Predictors of Failure Times of Censored Units in Progressively Censored Samples from Normal Distribution

In this article, we consider the problem of predicting times to failure of units censored in multiple stages in a progressively censored sample from normal distribution. The best linear unbiased predictor (BLUP), the maximum likelihood predictor (MLP), a modified maximum likelihood predictor (MMLP), an approximate maximum likelihood predictor (AMLP), and the conditional median predictor (CMP) a...

Variance estimation for multivariate normal dynamic linear models

In multivariate normal dynamic and state-space linear models the observational variance matrix is usually assumed known. Apart from a handful of special cases, estimation procedures that allow for the variance of the observational errors to be left unspecified are not widely available. The foundation of this paper is the general multivariate normal dynamic linear model with unknown but fixed ob...

Prediction of Times to Failure of Censored Units in Hybrid Censored Samples from Exponential Distribution

In this paper, we discuss different predictors of times to failure of units censored in a hybrid censored sample from exponential distribution. Bayesian and non-Bayesian point predictors for the times to failure of units are obtained. Non-Bayesian prediction Intervals are obtained based on pivotal and highest conditional density methods. Bayesian prediction intervals are also proposed. One real...