Comparative estimates of teenage illegitimacy in the United States, 1940–44 to 1970–74

Incompleteness in the reporting of illegitimate births in the U.S. vital registration system due to the consistent nonparticipation of a number of large States has left data published by the National Center for Health Statistics open to considerable criticism. Utilizing retrospective marriage and fertility data from the June 1978 Current Population Survey, a national probability sample of 54,000 interviewed households, a time series on teenage illegitimacy for first births is constructed that permits an evaluation of similar Vital Statistics data on teenage illegitimacy since the 1940s. Although there are some indications of a slight underreporting of white illegitimate first births by Vital Statistics during the 1940s and early 1950s, the overall comparison produces a general consensus between the two data sources on the incidence of illegitimacy among both white and nonwhite teenagers for the period 1940–44 to 1970–74.     

Development of a canonical correlation model involving non linearity and asymmetric variables

The classical canonical correlation analysis (CCA) can characterize, but is limited to, symmetric and linear associations. This study formulated a new model which generalized CCA to non linear associations and asymmetric distributions. Special cases of the proposed model were discussed. The behavior of canonical solutions under varying mixtures of skewness and non linearity (NL) was also examined in a simulation study. In addition, these solutions were compared with some commonly used methods of Hotelling, Spearman, and Kendall. Our empirical findings showed, among others, that for a fixed level of NL, the canonical correlation (ρ) increases as skewness increases. By and large, whether by ρ, likelihood, Akaike information criterion and Bayesian information criterion, the proposed method performed better than the other methods in all degrees of skewness and NL considered. This was further confirmed with real-life data application as Hotelling, Spearman, and Kendall overestimated ρ by 2.08%, 37.81%, and 22.15%, respectively, compared to the proposed technique.     

Bayesian inference for partially observed stochastic epidemics

The analysis of infectious disease data is usually complicated by the fact that real life epidemics are only partially observed. In particular, data concerning the process of infection are seldom available. Consequently, standard statistical techniques can become too complicated to implement effectively. In this paper Markov chain Monte Carlo methods are used to make inferences about the missing data as well as the unknown parameters of interest in a Bayesian framework. The methods are applied to real life data from disease outbreaks.     

A doubly restricted exponential dispersion model

In this paper, we propose and develop a doubly restricted exponential dispersion model, i.e. a varying dispersion generalized linear model with two sets of restrictions, a set of linear restrictions for the mean response, and at the same time, for another set of linear restrictions for the dispersion of the distribution. This model would be useful to consider several situations where it is necessary to control/analyze drug-doses, active effects in factorial experiments, mean-variance relationships, among other situations. A penalized likelihood function is proposed and developed in order to achieve the restricted parameters and to develop the inferential results. Several special cases from the literature are commented on. A simply restricted varying dispersion beta regression model is exemplified by means of real and simulated data. Satisfactory and promising results are found.     

The inverse weighted Lindley distribution: Properties,estimation and an application on a failure time data

In this paper a new distribution is proposed. This new model provides more flexibility to modeling data with upside-down bathtub hazard rate function. A significant account of mathematical properties of the new distribution is presented. The maximum likelihood estimators for the parameters in the presence of complete and censored data are presented. Two corrective approaches are considered to derive modified estimators that are bias-free to second order. A numerical simulation is carried out to examine the efficiency of the bias correction. Finally, an application using a real data set is presented in order to illustrate our proposed distribution.     

Transformation of the bathtub failure rate data in reliability for using Weibull-model analysis

All statistical methods involve basic model assumptions, which if violated render results of the analysis dubious. A solution to such a contingency is to seek an appropriate model or to modify the customary model by introducing additional parameters. Both of these approaches are in general cumbersome and demand uncommon expertise. An alternative is to transform the data to achieve compatibility with a well understood and convenient customary model with readily available software. The well-known example is the Box–Cox data transformation developed in order to make the normal theory linear model usable even when the assumptions of normality and homoscedasticity are not met.In reliability analysis the model appropriateness is determined by the nature of the hazard function. The well-known Weibull distribution is the most commonly employed model for this purpose. However, this model, which allows only a small spectrum of monotone hazard rates, is especially inappropriate if the data indicate bathtub-shaped hazard rates.In this paper, a new model based on the use of data transformation is presented for modeling bathtub-shaped hazard rates. Parameter estimation methods are studied for this new (transformation) approach. Examples and results of comparisons between the new model and other bathtub-shaped models are shown to illustrate the applicability of this new model.     

Proposals for 2001 samples of anonymized records: An assessment of disclosure risk

In 1991 Marsh and co-workers made the case for a sample of anonymized records (SAR) from the 1991 census of population. The case was accepted by the Office for National Statistics (then the Office of Population Censuses and Surveys) and a request was made by the Economic and Social Research Council to purchase the SARs. Two files were released for Great Britain—a 2% sample of individuals and a 1% sample of households. Subsequently similar samples were released for Northern Ireland. Since their release, the files have been heavily used for research and there has been no known breach of confidentiality. There is a considerable demand for similar files from the 2001 census, with specific requests for a larger sample size and lower population threshold for the individual SAR. This paper reassesses the analysis of Marsh and co-workers of the risk of identification of an individual or household in a sample of microdata from the 1991 census and also uses alternative ways of assessing risks with the 1991 SARs. The results of both the reassessment and the new analyses are reassuring and allow us to take the 1991 SARs as a base-line against which to assess proposals for changes to the size and structure of samples from the 2001 census.