An extension of the generalized half-normal distribution

In this paper we propose an extension of the generalized half-normal distribution studied in Cooray and Ananda (Commun Stat 37:1323–1337, 2008). This new distribution is defined by considering the quotient of two random variables, the one in the numerator being a generalized half normal distribution and the one in the denominator being a power of the uniform distribution on \((0,1)\) , respectively. The resulting distribution has greater kurtosis than the generalized half normal distribution. The density function of this more general distribution is derived jointly with some of its properties and moments. We discuss stochastic representation, maximum likelihood and moments estimation. Applications to real data sets are reported revealing that the proposed distribution can fit real data better than the slashed half-normal, generalized half-normal and Birnbaum–Saunders distributions.     

Core/periphery scientific collaboration networks among very similar researchers

Empirical studies such as Goyal et al. (J Polit Econ 114(2):403–412, 2006) or Newman (Proc Natl Acad Sci USA 101(Suppl. 1):5200–5205, 2004) show that scientific collaboration networks present a highly unequal and hierarchical distribution of links. This implies that some researchers can be much more active and productive than others and, consequently, they can enjoy a much better scientific reputation. One may think that big intrinsical differences among researchers can constitute the main driving force behind these inequalities. Nevertheless, this model shows that, under specific circumstances, very similar individuals may self-organize themselves forming unequal and hierarchical structures.     

The Family Context of Cohabitation and Single Motherhood in Latin America

The dramatic shift from marriage to cohabitation during the last four decades in most Latin American countries, even among the upper social strata, begs the question as to the living arrangements of cohabiting couples and single mothers. The new “Family Interrelationships Variables” in the IPUMS samples of Latin American censuses facilitated the construction of an enlarged LIPRO typology. LIPRO classifies individuals with respect to the types of households in which they are living. The results indicate that cohabiting women and single mothers aged 25 to 29 are frequently found in their parental households or in other extended or composite households. However, there are large variations depending on country and education. For instance, cohabitation is mainly in nuclear households, as in Europe, in Puerto Rico, Costa Rica, Brazil, and Argentina. It occurs mainly in the context of extended households in Peru, Bolivia, Colombia, Venezuela, Panama, and Cuba. Mexico and Chile occupy intermediate positions. In all instances co‐residence of cohabiting couples with other kin drops significantly upon the transition to parenthood, at which point there are no longer any differences between cohabiting and married couples. Single mothers, however, continue to co‐reside in extended or composite households to a larger extent, and this holds particularly for the better‐educated among them. This analysis illustrates that cohabitation of the traditional type and of the “Second Demographic Transition” type are found alongside each other, with one being more important than the other depending on country and on educational level or social class within each country.     

Estimation of the parameters of a truncated gamma distribution

This paper deals with the estimation of the parameters of a truncated gamma distribution over (0,τ), where τ is assumed to be a real number. We obtain a necessary and sufficient condition for the existence of the maximum likelihood estimator(MLE). The probability of nonexistence of MLE is observed to be positive. A simulation study indicates that the modified maximum likelihood estimator and the mixed estimator, which exist with probability one,are to be preferred over MLE. The bias, the mean square error, and the probability of nearness form a basis of our simulation study.     

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.     

Estimation of the parameters in a truncated normal distribution

This paper deals with the estimation of the parameters of doubly truncated and singly truncated normal distributions when truncation points are known. We derive, for these families, a necessary and sufficient condition for the maximum likelihood estimator(MLE) to be finite. Furthermore, the probability of the MLE being infinite is positive. A simulation study for single truncation is carried out to compare the modified maximum likelihood estimator, and the mixed estimator.     

A conjugate family for ar(1) processes with exponential errors

In this article estimation of autoregressive processes AR(1) with exponential errors, denoted by ARE(1), is considered from a Bayesian perspective. For these processes a new family of conjugate distributions, denoted by GBTP, is shown to exist which follows for recursive estimation of the parameters in the model. Further extensions of the model are also considered.     

Nonparametrie recursive estimator for mean residual life and vitality function under dependence conditions

In this paper, we develop a nonparametrie recursive estimator for the vitality and mena residual life function, based on kernel density estimators under mixing dependence conditions. The consistency and asymptotic normality of the estimator are established, under suitable regularity conditions. It is also shown that the Integrated Mean Squared Error converges to zero. The paper is concluyed with some simulation results.