On bootstrap testing inference in cure rate models

Survival models deal with the time until the occurrence of an event of interest. However, in some situations the event may not occur in part of the studied population. The fraction of the population that will never experience the event of interest is generally called cure rate. Models that consider this fact (cure rate models) have been extensively studied in the literature. Hypothesis testing on the parameters of these models can be performed based on likelihood ratio, gradient, score or Wald statistics. Critical values of these tests are obtained through approximations that are valid in large samples and may result in size distortion in small or moderate sample sizes. In this sense, this paper proposes bootstrap corrections to the four mentioned tests and bootstrap Bartlett correction for the likelihood ratio statistic in the Weibull promotion time model. Besides, we present an algorithm for bootstrap resampling when the data presents cure fraction and right censoring time (random and non-informative). Simulation studies are conducted to compare the finite sample performances of the corrected tests. The numerical evidence favours the corrected tests we propose. We also present an application in an actual data set.     

Estimation of a nonlinear discriminant function from a mixture of two GEV distributions

In this paper, the identifiability of finite mixture of generalized extreme value (GEV) distributions is proved. Next, a procedure for finding maximum likelihood estimates (MLEs) of the parameters of a finite mixture of two generalized extreme value (MGEV) distributions is presented by using classified and unclassified observations. Then, a nonlinear discriminant function for a mixture of two GEV distributions is derived and the performance of the corresponding estimated discriminant function is investigated through a series of simulation experiments. Finally, the methodology is applied to real data.     

Bayesian significance test for discriminating between survival distributions

An evaluation of FBST, Fully Bayesian Significance Test, restricted to survival models is the main objective of the present paper. A Survival distribution should be chosen among the tree celebrated ones, lognormal, gamma, and Weibull. For this discrimination, a linear mixture of the three distributions is an important tool: the FBST is used to test the hypotheses defined on the mixture weights space. Another feature of the paper is that all three distributions are reparametrized in that all the six parameters are written as functions of the mean and the variance of the population been studied. Some numerical results from simulations with some right-censored data are considered.     

Zero-truncated compound Poisson integer-valued GARCH models for time series

Starting from the compound Poisson INGARCH models, we introduce in this paper a new family of integer-valued models suitable to describe count data without zeros that we name zero-truncated CP-INGARCH processes. For such class of models, a probabilistic study concerning moments existence, stationarity and ergodicity is developed. The conditional quasi-maximum likelihood method is introduced to consistently estimate the parameters of a wide zero-truncated compound Poisson subclass of models. The conditional maximum likelihood method is also used to estimate the parameters of ZTCP-INGARCH processes associated with well-specified conditional laws. A simulation study that compares some of those estimators and illustrates their finite distance behaviour as well as a real-data application conclude the paper.     

About an adaptively weighted Kaplan-Meier estimate

The minimum averaged mean squared error nonparametric adaptive weights use data from m possibly different populations to infer about one population of interest. The definition of these weights is based on the properties of the empirical distribution function. We use the Kaplan-Meier estimate to let the weights accommodate right-censored data and use them to define the weighted Kaplan-Meier estimate. The proposed estimate is smoother than the usual Kaplan-Meier estimate and converges uniformly in probability to the target distribution. Simulations show that the performances of the weighted Kaplan-Meier estimate on finite samples exceed that of the usual Kaplan-Meier estimate. A case study is also presented.     

Trajectories of team and individual sports participation in childhood and links with internalizing problems

The purpose of this 5-year longitudinal study is to identify trajectories of team and individual sports participation in childhood, and to compare these trajectories on different types of internalizing problems. Parents reported their children's annual sports participation from the ages of 6–10 (n = 785) and teachers reported internalizing problems (depression, anxiety, social withdrawal) at the beginning and at the end of this period. The study used latent class growth analyses to identify two trajectories of team sports and two trajectories of individual sports participation were identified: (1) high participation, and (2) low participation. Considering baseline level of internalizing problems and gender, children in the high participation team sports trajectory show significantly fewer depression, anxiety, and social withdrawal behaviors at age 10 than those in the low participation team sports trajectory. No differences between individual sports trajectories were observed. Comparison with joint trajectory groups partially confirmed these results but also showed that sustained participation in multiple individual sports could also be linked to decreased anxiety. Our findings suggest that participation in team sports should be encouraged to promote certain aspects of children's mental health.     

Inflation targeting and the exchange rate: Does it matter in Turkey?

This paper investigates the monetary policy reaction functions of the Central Bank of Republic of Turkey (CBRT) over the periods 1987:01–2001:12 and 2002:01–2009:05. We specifically attempt to shed light on question to what extent exchange rate developments still continue to influence monetary policy, even under inflation targeting. This study seeks to investigate how the monetary policy responded to the exchange rate shocks before and after adoption of inflation targeting regime. How large the effect of exchange rate shocks is accounted for in forecast error variances decompositions for monetary policy as compared to other shocks? Using the VAR model, this study shows that there has been strong pass-through during whole period. Moreover, in the postcrisis period, exchange rate has been the main reaction variable for the CBRT.     

Methylmercury Risks and EPA + DHA Benefits Associated with Seafood Consumption in Europe

The intake of methyl‐Hg and EPA + DHA through consumption of seafood in Europe as well as the associated probability of exceeding the provisional tolerable weekly intake (PTWI) and the recommended daily intake (RDI), respectively, were estimated by combining methyl‐Hg and EPA + DHA contents in the five most consumed seafood species with hypothesized consumption distributions for eight European countries, chosen on the basis of size and representative significance. Two estimators were used: plug‐in (PI) and tail estimation (TE). The latter was based on the application of the extreme value theory to the intakes distribution curves. Whereas contents data were collected from own database and published scientific papers, consumption data were obtained from statistical sources of the various countries. Seafood consumption levels varied considerably between countries, from 140 in the United Kingdom to 628.5 g/(person.week) in Iceland. The main consumed species were also different between countries. The probability of exceeding the methyl‐Hg PTWI ranged from 0.04% in the United Kingdom to 9.61% in Iceland. Concerning the probability of exceeding the RDI of EPA + DHA, Iceland was third, after Portugal (66.05%) and Spain (61.05%) and the United Kingdom was the last (0.32%). While TE was most accurate for small probabilities, PI yielded best estimates for larger probabilities.     

Feasibility Study on the Use of Probabilistic Migration Modeling in Support of Exposure Assessment from Food Contact Materials

The use of probabilistic approaches in exposure assessments of contaminants migrating from food packages is of increasing interest but the lack of concentration or migration data is often referred as a limitation. Data accounting for the variability and uncertainty that can be expected in migration, for example, due to heterogeneity in the packaging system, variation of the temperature along the distribution chain, and different time of consumption of each individual package, are required for probabilistic analysis. The objective of this work was to characterize quantitatively the uncertainty and variability in estimates of migration. A Monte Carlo simulation was applied to a typical solution of the Fick's law with given variability in the input parameters. The analysis was performed based on experimental data of a model system (migration of Irgafos 168 from polyethylene into isooctane) and illustrates how important sources of variability and uncertainty can be identified in order to refine analyses. For long migration times and controlled conditions of temperature the affinity of the migrant to the food can be the major factor determining the variability in the migration values (more than 70% of variance). In situations where both the time of consumption and temperature can vary, these factors can be responsible, respectively, for more than 60% and 20% of the variance in the migration estimates. The approach presented can be used with databases from consumption surveys to yield a true probabilistic estimate of exposure.