Multiple linear regression model with stochastic design variables

In a simple multiple linear regression model, the design variables have traditionally been assumed to be non-stochastic. In numerous real-life situations, however, they are stochastic and non-normal. Estimators of parameters applicable to such situations are developed. It is shown that these estimators are efficient and robust. A real-life example is given.     

Fiducial inference under nonparametric situations

The object of the paper is to provide recipes for various fiducial inferences on a parameter under nonparametric situations. First, the fiducial empirical distribution of a random variable was introduced under nonparametric situations. And its almost sure behavior was established. Then based on it, fiducial model and hence fiducial distribution of a parameter are obtained. Further fiducial intervals of parameters as functionals of the population were constructed. Some of their frequentist properties were investigated under some mild conditions. Besides, p-values of some test hypotheses and their asymptotical properties were also given. Three applications of above results and further results were provided. For the mean, simulations on its interval estimator and hypothesis testing were conducted and their results suggest that the fiducial method performs better than others considered here.     

Omnitab-a simple language for statistical computations

The OMNITAB system of computer programming provides a simple, yet extremely versatile means of communicating with the computer using simple English words and phrases, It is written in FORTRAN and is available for implementation on UNIVAC 1108, IBM 360, Burroughs 5500 and CDC 6600 equipment. It is based on a worksheet, stored in the computer, which is a two dimensional array easy to visualize even by beginners. The system has particular use for students of statistics, of all ages, in that it allows them to interact with the data without requiring that they master any diffiicult and esoteric (for statisticians) computer skills. The programming system is described together with several applications to statistical problems. Full program documentation is available from the authors.     

Approximating the distribution of a renewal process using generalized poisson distributions

The exact distribution of a renewal counting process is not easy to compute and is rarely of closed form. In this article, we approximate the distribution of a renewal process using families of generalized Poisson distributions. We first compute approximations to the first several moments of the renewal process. In some cases, a closed form approximation is obtained. It is found that each family considered has its own strengths and weaknesses. Some new families of generalized Poisson distributions are recommended. Theorems are obtained determining when these variance to mean ratios are less than (or exceed) one without having to find the mean and variance. Some numerical comparisons are also made.     

Optimal collapsing of mixture distributions in robust recursive estimation

Several authors have discussed Kalman filtering procedures using a mixture of normals as a model for the distributions of the noise in the observation and/or the state space equations. Under this model, resulting posteriors involve a mixture of normal distributions, and a “collapsing method” must be found in order to keep the recursive procedure simple. We prove that the Kullback-Leibler distance between the mixture posterior and that of a single normal distribution is minimized when we choose the mean and variance of the single normal distribution to be the mean and variance of the mixture posterior. Hence, “collapsing by moments” is optimal in this sense. We then develop the resulting optimal algorithm for “Kalman filtering” for this situation, and illustrate its performance with an example.     

A General Approach for Preservation of Some Aging Classes Under Weighting

In this article, we apply univariate characterizations of some monotonic aging classes to investigate the preservation problem of those classes and stochastic orders under weighting. The results are also examined and discussed for special cases of the weighted distributions, as well.     

The fibonacci distribution revisited

The Fibonacci distributions of Shane (1973) are extended to a family of power series distributions having the higher order Fibonacci numbers as coefficients. The distributions possess a waiting time interpretation that generalizes the geometric distribution and, in a special case, solves a waiting time problem in genetics Hazard function and modal behavior is examined In particular, the distributions can have an unlimited number of modes     

Bayesian forecasting with changing linear models

This investigation considers a general linear model which changes parameters exactly once during the observation period. Assuming all the parameters are unknown and a proper prior distribution, the Bayesian predictive distribution of the future observations is derived.

It is shown that the predictive distribution is a mixture of multivariate t distributions and that the mixing distribution is the marginal posterior mass function of the change point parameter.     

Terminating markov renewal processes

Several kinds of terminating Markov Renewal Processes are defined. Of interest in these processes are the time T until termination and the number of transitions N_T until termination. For several kinds of terminating processes, the distribution and moments of T and N_T are obtained along with their covariance. The distributions of associated cumulative processes are also considered. A Markov Renewal model is compared with results of Markov Chains used to model epidemics, and other examples are examined in compartmental modeling and competing risks.     

Estimation and Confidence Intervals for Clock Offset in Networks with Bivariate Exponential Delays

There is a large and increasing literature on statistical modeling-based estimation of the offset between two clocks. Recent work has focused on the construction of confidence intervals for offset. However, in most of this work it has been assumed that the network delays that occur during the synchronization process are independent. The network delays are often modeled as independent exponential random variables. Thus, we introduce the use of a bivariate exponential distribution to capture the anticipated correlation between the network delays and derive a maximum likelihood estimator and a confidence interval procedure for the offset parameter. We then illustrate how use of the independent model for network delays can lead to improper inference about the offset parameter.