A new destructive Poisson odd log-logistic generalized half-normal cure rate model

We propose a new cure rate survival model by assuming that the initial number of competing causes of the event of interest follows a Poisson distribution and the time to event has the odd log-logistic generalized half-normal distribution. This survival model describes a realistic interpretation for the biological mechanism of the event of interest. We estimate the model parameters using maximum likelihood. For different sample sizes, various simulation scenarios are performed. We propose the diagnostics and residual analysis to verify the model assumptions. The potentiality of the new cure rate model is illustrated by means of a real data.     

The Poisson–exponential distribution: a Bayesian approach

In this paper, we proposed a new two-parameter lifetime distribution with increasing failure rate. The new distribution arises on a latent complementary risk scenario. The properties of the proposed distribution are discussed, including a formal proof of its density function and an explicit algebraic formulae for its quantiles and survival and hazard functions. Also, we have discussed inference aspects of the model proposed via Bayesian inference by using Markov chain Monte Carlo simulation. A simulation study investigates the frequentist properties of the proposed estimators obtained under the assumptions of non-informative priors. Further, some discussions on models selection criteria are given. The developed methodology is illustrated on a real data set.     

Log-Weibull extended regression model: Estimation,sensitivity and residual analysis

The failure rate function commonly has a bathtub shape in practice. In this paper we discuss a regression model considering new Weibull extended distribution developed by Xie et al. (2002) that can be used to model this type of failure rate function. Assuming censored data, we discuss parameter estimation: maximum likelihood method and a Bayesian approach where Gibbs algorithms along with Metropolis steps are used to obtain the posterior summaries of interest. We derive the appropriate matrices for assessing the local influence on the parameter estimates under different perturbation schemes, and we also present some ways to perform global influence. Also, some discussions on case deletion influence diagnostics are developed for the joint posterior distribution based on the Kullback–Leibler divergence. Besides, for different parameter settings, sample sizes and censoring percentages, are performed various simulations and display and compare the empirical distribution of the Martingale-type residual with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to the martingale-type residual in log-Weibull extended models with censored data. Finally, we analyze a real data set under a log-Weibull extended regression model. We perform diagnostic analysis and model check based on the martingale-type residual to select an appropriate model.     

Learning to learn and productivity growth: Evidence from a new car-assembly plant

This paper models learning by experience beyond the experience curve, including the possibility of “learning to learn”: the pace of learning increases over time by building on what has already been learned. We compare the extended deterministic learning model with Jovanovic and Nyarkos' [26] stochastic learning. The theoretical models are tested with data on the total factor productivity of a car-assembly plant in its first months of operation. We find that the deterministic “mixed learning model”, where the speed of learning is equal to a constant plus a learning to learn effect, is the one that best fits the empirical data. The mixed learning model results in a time pattern of total factor productivity growth, first increasing and later decreasing, different from the always decreasing rate of growth of the learning curve, opening new perspectives on the study of learning by experience.     

IMAGE AND MISREPORTING

In this paper we analyze how image utility can lead to misreporting of private information in contexts where truthful reports maximize monetary outcomes. In a controlled experiment, subjects go through a series of quiz questions and subsequently report a performance measure. We vary whether reports are made to an audience or not. In an additional feedback treatment, reports are also stated to an audience and afterwards the experimenter publicly verifies whether reports were correct. We find that in the audience treatment, stated reports are significantly higher relative to the private treatment as well as the feedback treatment. Our findings suggest that overconfident appearance might be a consequence of social approval seeking.     

Population Growth and the Local Provision of Services: The Role of Primary Schools in Portugal

Population Research and Policy Review - The shrinking population in many European regions has led to a debate on the relation between the local provision of services of general interest and the...     

Intercultural crossings in a digital age: ICT pathways with migrant and refugee-background youth

This article problematises the uptake and use of digital technologies by migrant and refugee-background young people, through the lens of a site-based arts pedagogy program, Culture Shack (CS), in Melbourne, Australia. It argues that online pedagogies including animation, Facebook, photoshop, mobile phones and Youtube can be used effectively for bridging cultural, gender and educational gaps, if the ways in which they are applied engage with communication preferences and discourses of culture, ethnicity and digital media technology – including issues related to technological determinism. Drawing on Dimitriadis’ attention to the power of public pedagogies and cyberculture theorists such as Leung and Nakamura, this article frames creative ICT use as not merely a tool but a contested, negotiated space in which young participants shape educational transits of being and becoming, and arts-based digital learning as twenty-first century global pedagogies.     

Longitudinal Links Between Gambling Participation and Academic Performance in Youth: A Test of Four Models

Gambling participation and low academic performance are related during adolescence, but the causal mechanisms underlying this link are unclear. It is possible that gambling participation impairs academic performance. Alternatively, the link between gambling participation and low academic performance could be explained by common underlying risk factors such as impulsivity and socio-family adversity. It could also be explained by other current correlated problem behaviors such as substance use. The goal of the present study was to examine whether concurrent and longitudinal links between gambling participation and low academic performance exist from age 14 to age 17 years, net of common antecedent factors and current substance use. A convenience sample of 766 adolescents (50.6% males) from a longitudinal twin sample participated in the study. Analyses revealed significant, albeit modest, concurrent links at both ages between gambling participation and academic performance. There was also a longitudinal link between gambling participation at age 14 and academic performance at age 17, which persisted after controlling for age 12 impulsivity and socio-family adversity as well as current substance use. Gambling participation predicts a decrease in academic performance during adolescence, net of concurrent and antecedent personal and familial risk factors.     

A Longitudinal Empirical Investigation of the Pathways Model of Problem Gambling

The pathways model of problem gambling suggests the existence of three developmental pathways to problem gambling, each differentiated by a set of predisposing biopsychosocial characteristics: behaviorally conditioned (BC), emotionally vulnerable (EV), and biologically vulnerable (BV) gamblers. This study examined the empirical validity of the Pathways Model among adolescents followed up to early adulthood. A prospective-longitudinal design was used, thus overcoming limitations of past studies that used concurrent or retrospective designs. Two samples were used: (1) a population sample of French-speaking adolescents (N = 1033) living in low socio-economic status (SES) neighborhoods from the Greater Region of Montreal (Quebec, Canada), and (2) a population sample of adolescents (N = 3017), representative of French-speaking students in Quebec. Only participants with at-risk or problem gambling by mid-adolescence or early adulthood were included in the main analysis (n = 180). Latent Profile Analyses were conducted to identify the optimal number of profiles, in accordance with participants’ scores on a set of variables prescribed by the Pathways Model and measured during early adolescence: depression, anxiety, impulsivity, hyperactivity, antisocial/aggressive behavior, and drug problems. A four-profile model fit the data best. Three profiles differed from each other in ways consistent with the Pathways Model (i.e., BC, EV, and BV gamblers). A fourth profile emerged, resembling a combination of EV and BV gamblers. Four profiles of at-risk and problem gamblers were identified. Three of these profiles closely resemble those suggested by the Pathways Model.     

Scale mixtures log-Birnbaum–Saunders regression models with censored data: a Bayesian approach

The main objective of this paper is to develop a full Bayesian analysis for the Birnbaum–Saunders (BS) regression model based on scale mixtures of the normal (SMN) distribution with right-censored survival data. The BS distributions based on SMN models are a very general approach for analysing lifetime data, which has as special cases the Student-t-BS, slash-BS and the contaminated normal-BS distributions, being a flexible alternative to the use of the corresponding BS distribution or any other well-known compatible model, such as the log-normal distribution. A Gibbs sample algorithm with Metropolis–Hastings algorithm is used to obtain the Bayesian estimates of the parameters. Moreover, some discussions on the model selection to compare the fitted models are given and case-deletion influence diagnostics are developed for the joint posterior distribution based on the Kullback–Leibler divergence. The newly developed procedures are illustrated on a real data set previously analysed under BS regression models.