Effect of agricultural policy on succession decisions of farm households

Policymakers, economists, and researchers have recently been interested in assessing the impact of farm program payments on the growth and survival of farm businesses. Planning for succession is an integral part of managing a farm business. This study uses farm-level data to investigate the impact of government farm policy and farm growth on both succession decisions and the likelihood of intra-family transfers of the farm business. Results indicate that succession decisions are significantly influenced by government farm policy, farm wealth, age, and educational attainment of current farm operators. Results show that off-farm work by operators and spouses and regional location are positively correlated with non-family farm succession decisions. On the other hand, farm ownership, educational attainment, and marital status of the operator increase the likelihood of family-based succession decisions. However, in the presence of retirement income from other sources such as pension, parents are less likely to have a family successor.

Human Personality Behavior Based on Family Structure Using a Thematic Apperception Test

This study focuses on analyzing the relationship between the ways an individual projects a story from a picture shown to him or her with the past and the family background of the individual. We have taken the sample of 30 students heterogeneous with respect to gender and age. Each respondent was shown three pictures and wrote one story per picture; the stories were then analyzed on the basis of a thematic apperception test (TAT). A human personality has many aspects and dimensions. We will be trying to throw some light on the impact that the family structure and the bringing up of an individual has on the development of his or her personality.     

Estimation of reliability from a bivariate log-normal data

It has been established that the bivariate log-normal distribution is appropriate for modelling certain paired observations. In this paper, we have developed large-sample confidence intervals of the dependence and reliability R=P(X>Y) parameters from a bivariate log-normal distribution with equal log-normal means. The parameter R provides a general measure of difference between the two populations and has applications in many areas. The performance of these confidence intervals has been examined by extensive simulation studies. The results are illustrated with an example dealing with a quantitative assay problem.     

A generalization of the log-series distribution

A new generalized logarithmic series distribution (GLSD) with two parameters is proposed.The proposed model is flexible enough to describe short-tailed as well as long-tailed data.Some recurence relations for its probabilities and the factorial moments are presente.These recurrence relations are utilized to obtain the minimum chi-square estimators for the parmaters.Maximum likelihood estimators and some other estimators based on first few moments and probabilities are also suggested.Asymptotic relative efficiency of some of these estimators is also obtained and compared.Two test statistics based on the minimum chi-square estimators fo testing some hypotheses regarding the GLSD are proposed.The fit of the model and the application of the test statistics are exemplified by some data sets.Finally, a graphical method is suggested for differentiating between the ordinary logarithmic series distribution and the GLSD.     

Analysis op failure time data by burr distribution

Sometimes it is appropriate to model the survival and failure time data by a non-monotonic failure rate distribution. This may be desirable when the course of disease is such that mortality reaches a peak after some finite period and then slowly declines.In this paper we study Burr, type XII model whose failure rate exhibits the above behavior. The location of the critical points (at which the monotonicity changes) for both the failure rate and the mean residual life function (MRLF) are studied. A procedure is described for estimating these critical points. Necessary and sufficient conditions for the existence and uniqueness of the maximum likelihood estimates are provided and it is shown that the conditions provided by Wingo (1993) are not sufficient. A data set pertaining to fibre failure strengths is analyzed and the maximum likelihood estimates of the critical points are obtained.     

Preface

The appearance of this issue will mark almost two years since its inception by the late Editor, Professor Don B. Owen. Professor Malay Ghosh suggested a special issue on Pitman's Measure of Closeness (PMC) to Professor Owen in autumn of 1989. After a thorough review process the issue was finalized in June 1991. It is with remorse that we publish this special issue in memory of Professor Owen.

The completion of the issue coincided with a special conference, “Pitman's Measure of Cfoseness: Celebrating a Decade of Renaissance,” held on June 15, 1991 at the University of Texas at San Antonio (UTSA). The papers in this special issue, except those of Professor Kubokawa and Drs. Bertuzzi and Gandolfi, were presented and discussed. The conference participants included C.R. Rao (Pennsylvania State University), Colin Blyth (Queen's University), and H.T. David (Iowa State University). Further, P.K. Sen (University of North Carolina) and Malay Ghosh (University of Florida) gave keynote addresses that respectively set the themes for the morning and afternoon sessions.

The conference banquet held in the Regent's Room at the University of Texas at San Antonio, featured stimulating addresses by C.R. Rao and Colin Blyth on some of the major controversies of PMC such as intransitiveness and Berkson's conjecture. We are grateful to the University of Texas at San Antonio for making this conference a reality. In particular, we thank Professor Shair Ahmad, Director of the Division of Mathematics, Computer Science, and Statistics at UTSA for funding travel expenses not only for the invited speakers but also for many of the younger researchers. We also acknowledge University President Samuel Kirkpatrick who made the KIVA room available for the technical sessions and the Regent's Room for the Banquet. We also acknowledge the financial support of Bell Helicopter Textron, Inc. as a cosponsor of the conference.     

A New Approach to the Estimation of Inter-Variable Correlation

The use of different measures of similarity between observed vectors for the purposes of classifying or clustering them has been expanding dramatically in recent years. One result of this expansion has been the use of many new similarity measures, designed for the purpose of satisfying various criteria. A noteworthy application involves estimating the relationships between genes using microarray experimental data. We consider the class of ‘correlation-type’ similarity measures. The use of these new measures of similarity suggest that the whole problem needs to be formulated in statistical terms to clarify their relative benefits. Pursuant to this need, we define, for each given observed vector, a baseline representing the ‘true’ value common to each of the component observations. These ‘true’ values are taken to be parameters. We define the ‘true correlation’ between each two observed vectors as the average (over the distribution of the observations for given baseline parameters) of Pearson's correlation with sample means replaced by the corresponding baseline parameters. Estimators of this true correlation are assessed using their mean squared error (MSE). Proper Bayes estimators of this true correlation, being based on the predictive posterior distribution of the data, are both difficult to calculate/analyze and highly non robust. By constrast, empirical Bayes estimators are: (i) close to their Bayesian counterparts; (ii) easy to analyze; and (iii) strongly robust. For these reasons, we employ empirical Bayes estimators of correlation in place of their Bayesian counterparts. We show how to construct two different kinds of simultaneous Bayes correlation estimators: the first assumes no apriori correlation between baseline parameters; the second assumes a common unknown correlation between them. Estimators of the latter type frequently have significantly smaller MSE than those of the former type which, in turn, frequently have significantly smaller MSE than their Pearson estimator counterparts. For purposes of illustrating our results, we examine the problem of inferring the relationships between gene expression level vectors, in the context of observing microarray experimental data.     

Improved estimator of population total in PPS sampling

In this paper, we have considered an estimation of the population total Y of the study variable y, making use of information on an auxiliary variable x. A class of estimators for the population total Y using transformation on both the variables study as well as auxiliary has been suggested based on the probability proportional to size with replacement (PPSWR). In addition to many the usual PPS estimator, Reddy and Rao's (1977) estimator and Srivenkataramana and Tracy's (1979, 1984, 1986) estimators are shown to be members of the proposed class of estimators. The variance of the proposed class of estimators has been obtained. In particular, the properties of 75 estimators based on different known population parameters of the study as well as auxiliary variables have been derived from the proposed class of estimators. In support of the present study, numerical illustrations are given.     

Analysis of survival data by a Weibull–Bessel distribution

In survival analysis and reliability studies, problems with random sample size arise quite frequently. More specifically, in cancer studies, the number of clonogens is unknown and the time to relapse of the cancer is defined by the minimum of the incubation times of the various clonogenic cells. In this article, we have proposed a new model where the distribution of the incubation time is taken as Weibull and the distribution of the random sample size as Bessel, giving rise to a Weibull–Bessel distribution. The maximum likelihood estimation of the model parameters is studied and a score test is developed to compare it with its special submodel, namely, exponential–Bessel distribution. To illustrate the model, two real datasets are examined, and it is shown that the proposed model, presented here, fits better than several other existing models in the literature. Extensive simulation studies are also carried out to examine the performance of the estimates.