An R package for testing goodness of fit: goft

This R package implements three types of goodness-of-fit tests for some widely used probability distributions where there are unknown parameters, namely tests based on data transformations, on the ratio of two estimators of a dispersion parameter, and correlation tests. Most of the considered tests have been proved to be powerful against a wide range of alternatives and some new ones are proposed here. The package's functionality is illustrated with several examples by using some data sets from the areas of environmental studies, biology and finance, among others.     

Degradation modeling based on gamma process models with random effects

The random effects in a gamma process are introduced in terms of its scale parameter. However, the scale parameter affects both its mean and variance. Hence, the variation of the degradation rates and the within degradation increments are expected to be large. For some products, the random effects affect just the rate or just the volatility of the process. Thus, two modifications of the parameters' structure of the gamma process are proposed. One implies that the random effects affect just the volatility and the second just the rate. A Bayesian estimation approach is provided and implemented in two case studies.     

A class of beta regression models with multiplicative log-normal measurement errors

In this article, we propose a beta regression model with multiplicative log-normal measurement errors. Three estimation methods are presented, namely, naive, calibration regression, and pseudo likelihood. The nuisance parameters are estimated from a system of estimation equations using replicated data and these estimates are used to propose a pseudo likelihood function. A simulation study was performed to assess some properties of the proposed methods. Results from an example with a real dataset, including diagnostic tools, are also reported.     

Self-weighted recursive estimation of GARCH models

The generalized autoregressive conditional heteroscedasticity (GARCH) processes are frequently used to investigate and model financial returns. They are routinely estimated by computationally complex off-line estimation methods, for example, by the conditional maximum likelihood procedure. However, in many empirical applications (especially in the context of high-frequency financial data), it seems necessary to apply numerically more effective techniques to calibrate and monitor such models. The aims of this contribution are: (i) to review the previously introduced recursive estimation algorithms and to derive self-weighted alternatives applying general recursive identification instruments, and (ii) to examine these methods by means of simulations and an empirical application.     

The social worlds of immigrant youth

This introductory chapter uses a detailed case study to illustrate the interconnection of multiple social influences on one particular youth's path of migration. It further identifies some of the major influences on immigrant youth development, including the stresses of migration, separations and reunifications, changing networks of relations, poverty and segregation, and identity formation.     

Adaptive Test for Testing the Difference in Survival Distributions

An adaptive test is proposed for the problem of testing the difference in survival distributions when the shape of the hazard ratio is unknown, hence the efficient test is unknown. The proposed adaptive test selects a test statistic from a finite set of the weighted logrank statistics T on the basis of the estimates of the efficiencies of the tests in T for given data. The efficiency estimator uses the length of the test based nonparametric confidence interval for the shift in a time transformed shift model. The suggested adaptive test is shown to be asymptotically efficient among the tests in T under the time transformed shift model and conditions commonly used in survival analysis. Simulations demonstrate that the adaptive test enjoys good small sample properties and in most situations is more powerful than the test using the maximum of the tests in T.     

The interpretation and role of work-associated accelerated childbearing in post-war Britain

The paper examines a variety of data relating to work-associated differentials in birth-spacing and concludes that these may be interpreted as work-motivated. Two effects of employment on fertility are hypothesized: current work exerting a negative, decelerating influence and prospective employment a positive, accelerating influence. It is argued that the post-war rise in fertility to the mid-60s may have been brought about, in part, by the strong trend in women's labour-force participation. The ‘pull’ of future work is argued to have been stronger during the 1950s and 1960s and that of current economic activity to have increased during the 1970s. The changing balance of attractiveness between current and prospective economic activity may therefore initially have had an accelerating and later a decelerating impact on birth rates.