Improved score tests in symmetric linear regression models

The class of symmetric linear regression models has the normal linear regression model as a special case and includes several models that assume that the errors follow a symmetric distribution with longer-than-normal tails. An important member of this class is the t linear regression model, which is commonly used as an alternative to the usual normal regression model when the data contain extreme or outlying observations. In this article, we develop second-order asymptotic theory for score tests in this class of models. We obtain Bartlett-corrected score statistics for testing hypotheses on the regression and the dispersion parameters. The corrected statistics have chi-squared distributions with errors of order O(n(-3/2)), n being the sample size. The corrections represent an improvement over the corresponding original Rao`s score statistics, which are chi-squared distributed up to errors of order O(n(-1)). Simulation results show that the corrected score tests perform much better than their uncorrected counterparts in samples of small or moderate size.

Small-Sample Corrections for Score Tests in Birnbaum-Saunders Regressions

In this article, we deal with the issue of performing accurate small-sample inference in the Birnbaum-Saunders regression model, which can be useful for modeling lifetime or reliability data. We derive a Bartlett-type correction for the score test and numerically compare the corrected test with the usual score test and some other competitors.

Three Bartlett-type corrections for score statistics in symmetric nonlinear regression models

We present simple matrix formulae for corrected score statistics in symmetric nonlinear regression models. The corrected score statistics follow more closely a chi (2) distribution than the classical score statistic. Our simulation results indicate that the corrected score tests display smaller size distortions than the original score test. We also compare the sizes and the powers of the corrected score tests with bootstrap-based score tests.

Tests of proportional hazards and proportional odds models for grouped survival data

In this paper, we derive score test statistics to discriminate between proportional hazards and proportional odds models for grouped survival data. These models are embedded within a power family transformation in order to obtain the score tests. In simple cases, some small-sample results are obtained for the score statistics using Monte Carlo simulations. Score statistics have distributions well approximated by the chi-squared distribution. Real examples illustrate the proposed tests.

ASSOCIATON TESTS THAT ACCOMMODATE GENOTYPING ERRORS

High-throughput SNP arrays provide estimates of genotypes for up to one million loci, often used in genome-wide association studies. While these estimates are typically very accurate, genotyping errors do occur, which can influence in particular the most extreme test statistics and p-values. Estimates for the genotype uncertainties are also available, although typically ignored. In this manuscript, we develop a framework to incorporate these genotype uncertainties in case-control studies for any genetic model. We verify that using the assumption of a “local alternative” in the score test is very reasonable for effect sizes typically seen in SNP association studies, and show that the power of the score test is simply a function of the correlation of the genotype probabilities with the true genotypes. We demonstrate that the power to detect a true association can be substantially increased for difficult to call genotypes, resulting in improved inference in association studies.

Domestic use of a disclosing solution for denture hygiene: a randomised trial

Objectives: The purpose of this study was to investigate the effect of the domestic use of a disclosing agent for denture hygiene. Materials and methods: Completely edentulous participants wearing maxillary dentures were randomly assigned to one of the three intervention groups: (1) Follow-up only (control; n = 12); (2) Oral and denture hygiene instructions (n = 10); (3) Instructions associated with the home use of a disclosing agent (1% neutral red; n = 10). Biofilm coverage area (%) over internal and external surfaces of the maxillary denture was assessed at baseline and after 14 and 90 days. Data were evaluated by generalised estimating equations based on score tests (alpha = 0.05). Results: The participants presented low changes for areas of biofilm coverage (14 days (%): internal: GI = 1.4 +/- 0.9; GII = 1.5 +/- 1.3; GIII = -0.4 +/- 0.9; external: GI = 1.4 +/- 1.5; GII = 1.5 +/- 1.4; GIII = -0.4 +/- 0.9; 90 days (%): internal: GI = 2.0 +/- 0.9; GII = 2.2 +/- 1.4; GIII = 0.3 +/- 1.0; external: GI = 2.1 +/- 1.4; GII = 2.2 +/- 1.5; GIII = 0.3 +/- 0.9). Changes were similar for the three groups (p = 0.293) and were not influenced by the test time (p = 0.218). Conclusion: It can be concluded that the home use of a disclosing agent for denture hygiene does not improve the removal of the biofilm, particularly for patients with adequate oral hygiene habits.

Adaptive filtering for high quality HMM based speech synthesis

In this work an adaptive filtering scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for Hidden Markov Model (HMM) based speech synthesis quality enhancement. The objective is to improve signal smoothness across HMMs and their related states and to reduce artifacts due to acoustic model's limitations. Both speech and artifacts are modelled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. Themodel parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The quality enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. The system's performance has been evaluated using mean opinion score tests and the proposed technique has led to improved results.

A likelihood ratio appropach to family-based association studies with covariates

We introduce a procedure for association based analysis of nuclear families that allows for dichotomous and more general measurements of phenotype and inclusion of covariate information. Standard generalized linear models are used to relate phenotype and its predictors. Our test procedure, based on the likelihood ratio, unifies the estimation of all parameters through the likelihood itself and yields maximum likelihood estimates of the genetic relative risk and interaction parameters. Our method has advantages in modelling the covariate and gene-covariate interaction terms over recently proposed conditional score tests that include covariate information via a two-stage modelling approach. We apply our method in a study of human systemic lupus erythematosus and the C-reactive protein that includes sex as a covariate.

Active-control trials with binary data: a comparison of methods for testing superiority or non-inferiority using the odds ratio

This paper considers methods for testing for superiority or non-inferiority in active-control trials with binary data, when the relative treatment effect is expressed as an odds ratio. Three asymptotic tests for the log-odds ratio based on the unconditional binary likelihood are presented, namely the likelihood ratio, Wald and score tests. All three tests can be implemented straightforwardly in standard statistical software packages, as can the corresponding confidence intervals. Simulations indicate that the three alternatives are similar in terms of the Type I error, with values close to the nominal level. However, when the non-inferiority margin becomes large, the score test slightly exceeds the nominal level. In general, the highest power is obtained from the score test, although all three tests are similar and the observed differences in power are not of practical importance. Copyright (C) 2007 John Wiley & Sons, Ltd.

Choosing between Cox proportional hazards and logistic models for interval-censored data via bootstrap

This work develops a new methodology in order to discriminate models for interval-censored data based on bootstrap residual simulation by observing the deviance difference from one model in relation to another, according to Hinde (1992). Generally, this sort of data can generate a large number of tied observations and, in this case, survival time can be regarded as discrete. Therefore, the Cox proportional hazards model for grouped data (Prentice & Gloeckler, 1978) and the logistic model (Lawless, 1982) can befitted by means of generalized linear models. Whitehead (1989) considered censoring to be an indicative variable with a binomial distribution and fitted the Cox proportional hazards model using complementary log-log as a link function. In addition, a logistic model can be fitted using logit as a link function. The proposed methodology arises as an alternative to the score tests developed by Colosimo et al. (2000), where such models can be obtained for discrete binary data as particular cases from the Aranda-Ordaz distribution asymmetric family. These tests are thus developed with a basis on link functions to generate such a fit. The example that motivates this study was the dataset from an experiment carried out on a flax cultivar planted on four substrata susceptible to the pathogen Fusarium oxysoprum. The response variable, which is the time until blighting, was observed in intervals during 52 days. The results were compared with the model fit and the AIC values.

Association between bone mass and functional capacity among elderly people aged 80 years and over

Objetivo:Analisar a associação entre a massa óssea e capacidade funcional de idosos com 80 anos ou mais.Métodos:A amostra foi composta por 93 idosos entre 80 e 91 anos (83,2 ± 2,5 anos), 61 mulheres (83,3 ± 2,7 anos) e 32 homens (83,1 ± 2,2 anos) da cidade de Presidente Prudente. A avaliação da massa óssea foi feita pela absorptiometria de dupla energia de raios X (DXA), na qual foram mensurados os valores de conteúdo mineral ósseo (BMC) e densidade mineral óssea (BMD) do fêmur e da coluna (L1-L4). A capacidade funcional foi avaliada por meio dos testes de velocidade para caminhar, equilíbrio estático e força de membros inferiores contidos no questionário Saúde, Bem-Estar e Envelhecimento (Sabe). As variáveis da massa óssea e capacidade funcional foram categorizadas de acordo com os valores de mediana e a pontuação obtida nos testes, respectivamente. Para tratamento estatístico fez-se o teste qui-quadrado, o software usado foi SPSS (13.0) e o nível de significância estabelecido foi de 5%.Resultados:Os idosos do sexo masculino com maior desempenho nos testes funcionais apresentaram maiores valores de BMC de fêmur comparados com os de menor desempenho, resultado não encontrado quando avaliadas as mulheres.Conclusão:Dessa forma, a massa óssea do fêmur para idosos longevos do sexo masculino está associada à capacidade funcional. A avaliação constante da massa mineral óssea e a prática de atividade física ao longo da vida seriam medidas para prevenção das quedas em idosos.

Assessment of variance components in nonlinear mixed-effects elliptical models

The issue of assessing variance components is essential in deciding on the inclusion of random effects in the context of mixed models. In this work we discuss this problem by supposing nonlinear elliptical models for correlated data by using the score-type test proposed in Silvapulle and Silvapulle (1995). Being asymptotically equivalent to the likelihood ratio test and only requiring the estimation under the null hypothesis, this test provides a fairly easy computable alternative for assessing one-sided hypotheses in the context of the marginal model. Taking into account the possible non-normal distribution, we assume that the joint distribution of the response variable and the random effects lies in the elliptical class, which includes light-tailed and heavy-tailed distributions such as Student-t, power exponential, logistic, generalized Student-t, generalized logistic, contaminated normal, and the normal itself, among others. We compare the sensitivity of the score-type test under normal, Student-t and power exponential models for the kinetics data set discussed in Vonesh and Carter (1992) and fitted using the model presented in Russo et al. (2009). Also, a simulation study is performed to analyze the consequences of the kurtosis misspecification.

The local power of the gradient test

The asymptotic expansion of the distribution of the gradient test statistic is derived for a composite hypothesis under a sequence of Pitman alternative hypotheses converging to the null hypothesis at rate n(-1/2), n being the sample size. Comparisons of the local powers of the gradient, likelihood ratio, Wald and score tests reveal no uniform superiority property. The power performance of all four criteria in one-parameter exponential family is examined.

Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros

Count data with excess zeros relative to a Poisson distribution are common in many biomedical applications. A popular approach to the analysis of such data is to use a zero-inflated Poisson (ZIP) regression model. Often, because of the hierarchical Study design or the data collection procedure, zero-inflation and lack of independence may occur simultaneously, which tender the standard ZIP model inadequate. To account for the preponderance of zero counts and the inherent correlation of observations, a class of multi-level ZIP regression model with random effects is presented. Model fitting is facilitated using an expectation-maximization algorithm, whereas variance components are estimated via residual maximum likelihood estimating equations. A score test for zero-inflation is also presented. The multi-level ZIP model is then generalized to cope with a more complex correlation structure. Application to the analysis of correlated count data from a longitudinal infant feeding study illustrates the usefulness of the approach.

Pédiatrie. 3. Le score de McIsaac: une aide à l'indication des tests de diagnostic rapide dans tes angines à streptocoques A [Pediatrics. Using the McIsaac score for the indication of rapid diagnostic testing in children with streptococcal pharyngitis].

The McIsaac scoring system is a tool designed to predict the probability of streptococcal pharyngitis in children aged 3 to 17 years with a sore throat. Although it does not allow the physician to make the diagnosis of streptococcal pharyngitis, it enables to identify those children with a sore throat in whom rapid antigen detection tests have a good predictive value.