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Goodness-of-fit Test (goodness-of-fit + test)
Selected AbstractsA Bayesian Chi-Squared Goodness-of-Fit Test for Censored Data ModelsBIOMETRICS, Issue 2 2010Jing Cao Summary We propose a Bayesian chi-squared model diagnostic for analysis of data subject to censoring. The test statistic has the form of Pearson's chi-squared test statistic and is easy to calculate from standard output of Markov chain Monte Carlo algorithms. The key innovation of this diagnostic is that it is based only on observed failure times. Because it does not rely on the imputation of failure times for observations that have been censored, we show that under heavy censoring it can have higher power for detecting model departures than a comparable test based on the complete data. In a simulation study, we show that tests based on this diagnostic exhibit comparable power and better nominal Type I error rates than a commonly used alternative test proposed by Akritas (1988,,Journal of the American Statistical Association,83, 222,230). An important advantage of the proposed diagnostic is that it can be applied to a broad class of censored data models, including generalized linear models and other models with nonidentically distributed and nonadditive error structures. We illustrate the proposed model diagnostic for testing the adequacy of two parametric survival models for Space Shuttle main engine failures. [source] A Goodness-of-Fit Test for Multinomial Logistic RegressionBIOMETRICS, Issue 4 2006Jelle J. Goeman Summary This article presents a score test to check the fit of a logistic regression model with two or more outcome categories. The null hypothesis that the model fits well is tested against the alternative that residuals of samples close to each other in covariate space tend to deviate from the model in the same direction. We propose a test statistic that is a sum of squared smoothed residuals, and show that it can be interpreted as a score test in a random effects model. By specifying the distance metric in covariate space, users can choose the alternative against which the test is directed, making it either an omnibus goodness-of-fit test or a test for lack of fit of specific model variables or outcome categories. [source] The Effect of the Estimation on Goodness-of-Fit Tests in Time Series ModelsJOURNAL OF TIME SERIES ANALYSIS, Issue 4 2005Yue Fang Abstract., We analyze, by simulation, the finite-sample properties of goodness-of-fit tests based on residual autocorrelation coefficients (simple and partial) obtained using different estimators frequently used in the analysis of autoregressive moving-average time-series models. The estimators considered are unconditional least squares, maximum likelihood and conditional least squares. The results suggest that although the tests based on these estimators are asymptotically equivalent for particular models and parameter values, their sampling properties for samples of the size commonly found in economic applications can differ substantially, because of differences in both finite-sample estimation efficiencies and residual regeneration methods. [source] A Goodness-of-fit Test for the Marginal Cox Model for Correlated Interval-censored Failure Time DataBIOMETRICAL JOURNAL, Issue 6 2006Lianming Wang Abstract The marginal Cox model approach is perhaps the most commonly used method in the analysis of correlated failure time data (Cai, 1999; Cai and Prentice, 1995; Lin, 1994; Wei, Lin and Weissfeld, 1989). It assumes that the marginal distributions for the correlated failure times can be described by the Cox model and leaves the dependence structure completely unspecified. This paper discusses the assessment of the marginal Cox model for correlated interval-censored data and a goodness-of-fit test is presented for the problem. The method is applied to a set of correlated interval-censored data arising from an AIDS clinical trial. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Calculating census efficiency for river birds: a case study with the White-throated Dipper Cinclus cinclus in the PyrénéesIBIS, Issue 1 2003Frank D'Amico Using the binomial law we modelled field data to estimate the probability (p,) of detecting pairs of breeding White-throated Dippers, and the population size (N,± confidence limits). The model was divided into two parts according to whether the actual size of the population under study was known or not; in the latter case the truncated binomial model was used. Dipper abundance data were collected from three 4-km-long river tracts in the Pyrénées (France) during the breeding seasons of different years. Goodness-of-fit tests indicated that the binomial model fitted the data well. For a given visit during the survey, the estimated probability of detecting any pair of Dippers if they were present was always high (0.63,0.94) and constant from year to year but not between sites. Estimations (N,) of the size of the population provided by the binomial model were very close to that derived from mapping techniques. This study provides the first ever quantification of the number of visits required to detect birds on linear territories: three visits were necessary to detect the whole breeding population. [source] Goodness-of-fit tests of normality for the innovations in ARMA modelsJOURNAL OF TIME SERIES ANALYSIS, Issue 3 2004Gilles R. Ducharme Abstract., In this paper, we propose a goodness-of-fit test of normality for the innovations of an ARMA(p, q) model with known mean or trend. The test is based on the data driven smooth test approach and is simple to perform. An extensive simulation study is conducted to study the behaviour of the test for moderate sample sizes. It is found that our approach is generally more powerful than existing tests while holding its level throughout most of the parameter space and, thus, can be recommended. This agrees with theoretical results showing the superiority of the data driven smooth test approach in related contexts. [source] Goodness-of-fit tests for parametric models in censored regressionTHE CANADIAN JOURNAL OF STATISTICS, Issue 2 2007Juan Carlos Pardo-Fernández Abstract The authors propose a goodness-of-fit test for parametric regression models when the response variable is right-censored. Their test compares an estimation of the error distribution based on parametric residuals to another estimation relying on nonparametric residuals. They call on a bootstrap mechanism in order to approximate the critical values of tests based on Kolmogorov-Smirnov and Cramér-von Mises type statistics. They also present the results of Monte Carlo simulations and use data from a study about quasars to illustrate their work. Tests d'ajustement pour des modèles de régression paramétriques sujets à censure Les auteurs proposent un test permettant de juger de l'adéquation d'un modèle de régression paramétrique dont la variable réponse est sujette à une censure à droite. Leur test compare une estimation de la loi des erreurs déduite de résidus paramétriques à une autre estimation fondée sur des résidus non paramétriques. Ils font appel à une technique de rééchantillonnage pour approximer les valeurs critiques de tests fondés sur des statistiques de type Kolmogorov-Smirnov et Cramér-von Mises. Ils présentent aussi les résultats d'une étude de Monte-Carlo et illustrent leur propos à l'aide de données issues de travaux portant sur les quasars. [source] Goodness-of-fit tests for functional dataTHE ECONOMETRICS JOURNAL, Issue 2009Federico A. Bugni Summary, Economic data are frequently generated by stochastic processes that can be modelled as occurring in continuous time. That is, the data are treated as realizations of a random function (functional data). Sometimes an economic theory model specifies the process up to a finite-dimensional parameter. This paper develops a test of the null hypothesis that a given functional data set was generated by a specified parametric model of a continuous-time process. The alternative hypothesis is non-parametric. A random function is a form of infinite-dimensional random variable, and the test presented here a generalization of the familiar Cramér-von Mises test to an infinite dimensional random variable. The test is illustrated by using it to test the hypothesis that a sample of wage paths was generated by a certain equilibrium job search model. Simulation studies show that the test has good finite-sample performance. [source] Phylogenetic Composition of Angiosperm Diversity in the Cloud Forests of MexicoBIOTROPICA, Issue 4 2010Isolda Luna-Vega ABSTRACT Several members of the most ancient living lineages of flowering plants (angiosperms) inhabit humid, woody, mostly tropical habitats. Here we assess whether one of these forest types, the cloud forests of Mexico (CFM), contain a relatively higher proportion of phylogenetically early-diverging angiosperm lineages. The CFM houses an extraordinary plant species diversity, including members of earliest-diverging angiosperm lineages. The phylogenetic composition of CFM angiosperm diversity was evaluated through the relative representation of orders and families with respect to the global flora, and the predominance of phylogenetically early- or late-diverging lineages. Goodness-of-fit tests indicated significant differences in the proportional local and global representation of angiosperm clades. The net difference between the percentage represented by each order and family in the CFM and the global flora allowed identification of clades that are overrepresented and underrepresented in the CFM. Early-diverging angiosperm orders and families were found to be neither over- nor underrepresented in the CFM. A slight predominance of late-diverging phylogenetic levels among overrepresented clades, however, was encountered in the CFM. The resulting pattern suggests that cloud forests provide habitats where the most ancient angiosperm lineages have survived in the face of accumulating species diversity belonging to phylogenetically late-diverging lineages. Abstract in Spanish is available at http://www.blackwell-synergy.com/loi/btp [source] Adapting the logical basis of tests for Hardy-Weinberg Equilibrium to the real needs of association studies in human and medical geneticsGENETIC EPIDEMIOLOGY, Issue 7 2009Katrina A. B. Goddard Abstract The standard procedure to assess genetic equilibrium is a ,2 test of goodness-of-fit. As is the case with any statistical procedure of that type, the null hypothesis is that the distribution underlying the data is in agreement with the model. Thus, a significant result indicates incompatibility of the observed data with the model, which is clearly at variance with the aim in the majority of applications: to exclude the existence of gross violations of the equilibrium condition. In current practice, we try to avoid this basic logical difficulty by increasing the significance bound to the P -value (e.g. from 5 to 10%) and inferring compatibility of the data with Hardy Weinberg Equilibrium (HWE) from an insignificant result. Unfortunately, such direct inversion of a statistical testing procedure fails to produce a valid test of the hypothesis of interest, namely, that the data are in sufficiently good agreement with the model under which the P -value is calculated. We present a logically unflawed solution to the problem of establishing (approximate) compatibility of an observed genotype distribution with HWE. The test is available in one- and two-sided versions. For both versions, we provide tools for exact power calculation. We demonstrate the merits of the new approach through comparison with the traditional ,2 goodness-of-fit test in 2×60 genotype distributions from 43 published genetic studies of complex diseases where departure from HWE was noted in either the case or control sample. In addition, we show that the new test is useful for the analysis of genome-wide association studies. Genet. Epidemiol. 33:569,580, 2009. © 2009 Wiley-Liss, Inc. [source] Goodness-of-fit tests of normality for the innovations in ARMA modelsJOURNAL OF TIME SERIES ANALYSIS, Issue 3 2004Gilles R. Ducharme Abstract., In this paper, we propose a goodness-of-fit test of normality for the innovations of an ARMA(p, q) model with known mean or trend. The test is based on the data driven smooth test approach and is simple to perform. An extensive simulation study is conducted to study the behaviour of the test for moderate sample sizes. It is found that our approach is generally more powerful than existing tests while holding its level throughout most of the parameter space and, thus, can be recommended. This agrees with theoretical results showing the superiority of the data driven smooth test approach in related contexts. [source] Regional daily maximum rainfall estimation for Cekerek Watershed by L-momentsMETEOROLOGICAL APPLICATIONS, Issue 4 2009Kadri Yurekli Abstract The estimation of maximum daily rainfall (PDmax) is usually required for the estimation of design flood (the maximum flood that any hydraulic structure can safely pass). However, PDmax estimation is usually required for watersheds where rainfall data are either not available or only available in short periods from various sites and so are unsuitable for maximum daily rainfall estimation. In this study, the regional PDmax of the Cekerek watershed in Turkey is estimated using the method of l-moments using 17 rainfall stations in the region. The discordant test for outlier stations showed no discordant station in the region. Applying the homogeneity measure, Hi, the homogeneous region was identified. To find the best regional distribution, the ZDIST goodness-of-fit test was applied. This test introduced two distributions as the candidates for regional parent distributions; Generalized Extreme Values (GEV) and 3-parameter Log Normal (LOGN3) distributions. The LOGN3 distribution was selected as the best regional distribution as it has the smaller absolute value of the statistics (ZDIST) based on the goodness-of-fit-test. Copyright © 2009 Royal Meteorological Society [source] Clutter reduction in synthetic aperture radar images with statistical modeling: An application to MSTAR dataMICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 6 2008Sevket Demirci Abstract In this article, an application of clutter modeling and reduction techniques to synthetic aperture radar (SAR) images of moving and stationary target acquisition and recognition data is presented. Statistical modeling of the clutter signal within these particular SAR images is demonstrated. Lognormal, Weibull, and K-distribution models are analyzed for the amplitude distribution of high-resolution land clutter data. Higher-order statistics (moments and cumulants) are utilized to estimate the appropriate statistical distribution models for the clutter. Also, Kolmogorov-Smirnov (K-S) goodness-of-fit test is employed to validate the accuracy of the selected models. With the use of the determined clutter model, constant false-alarm rate detection algorithm is applied to the SAR images of several military targets. Resultant SAR images obtained by using the proposed method show that target signatures are reliably differentiated from the clutter background. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 1514,1520, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23413 [source] On inference for a semiparametric partially linear regression model with serially correlated errorsTHE CANADIAN JOURNAL OF STATISTICS, Issue 4 2007Jinhong You Abstract The authors consider a semiparametric partially linear regression model with serially correlated errors. They propose a new way of estimating the error structure which has the advantage that it does not involve any nonparametric estimation. This allows them to develop an inference procedure consisting of a bandwidth selection method, an efficient semiparametric generalized least squares estimator of the parametric component, a goodness-of-fit test based on the bootstrap, and a technique for selecting significant covariates in the parametric component. They assess their approach through simulation studies and illustrate it with a concrete application. L'inférence dans le cadre d'un modèle de régression semiparamétrique partiellement linéaire à termes d'erreur corrélés en série Les auteurs s'intéressent à un modèle de régression semiparamétrique partiellement linéaire à termes d'erreur corrélés en série. Ils proposent une façon originale d'estimer la structure d'erreur qui a l'avantage de ne faire intervenir aucune estimation non paramétrique. Ceci leur permet de développer une procédure d'inférence comportant un choix de fen,tre, l'emploi de la méthode des moindres carrés généralisés pour l'estimation semiparamétrique efficace de la composante paramétrique, un test d'adéquation fondé sur le rééchantillonnage et une technique de sélection des covariables significatives de la composante paramétrique. Ils évaluent leur approche par voie de simulation et en donnent une illustration concrète. [source] Goodness-of-fit tests for parametric models in censored regressionTHE CANADIAN JOURNAL OF STATISTICS, Issue 2 2007Juan Carlos Pardo-Fernández Abstract The authors propose a goodness-of-fit test for parametric regression models when the response variable is right-censored. Their test compares an estimation of the error distribution based on parametric residuals to another estimation relying on nonparametric residuals. They call on a bootstrap mechanism in order to approximate the critical values of tests based on Kolmogorov-Smirnov and Cramér-von Mises type statistics. They also present the results of Monte Carlo simulations and use data from a study about quasars to illustrate their work. Tests d'ajustement pour des modèles de régression paramétriques sujets à censure Les auteurs proposent un test permettant de juger de l'adéquation d'un modèle de régression paramétrique dont la variable réponse est sujette à une censure à droite. Leur test compare une estimation de la loi des erreurs déduite de résidus paramétriques à une autre estimation fondée sur des résidus non paramétriques. Ils font appel à une technique de rééchantillonnage pour approximer les valeurs critiques de tests fondés sur des statistiques de type Kolmogorov-Smirnov et Cramér-von Mises. Ils présentent aussi les résultats d'une étude de Monte-Carlo et illustrent leur propos à l'aide de données issues de travaux portant sur les quasars. [source] Population genetic studies of Alouatta caraya (Alouattinae, Primates): inferences on geographic distribution and ecologyAMERICAN JOURNAL OF PRIMATOLOGY, Issue 10 2007Fabrícia F. Do Nascimento Abstract Cytochrome b DNA sequence data (ca. 1,140,bp) of 44 Alouatta caraya, including 42 specimens from three localities of Brazil and two from Bolivia, were used for phylogenetic reconstructions and population studies. Seventeen haplotypes were identified, eight of which were present in more than one individual. Seven of these eight haplotypes were shared by individuals from a same locality and one by individuals from two localities. We found 26 variable sites along the entire gene, consisting of 18 transitions and eight transversions; most replacements occurring at the third codon position (65.39%) in contrast to first and second positions (26.92 and 7.69%, respectively). In the sample collected at Chapada dos Guimarães (Brazil), nucleotide and haplotype diversity estimates were ,=0.002325 and h=0.8772, respectively. Maximum parsimony analysis grouped all haplotypes in two clades, separating Bolivian haplotypes from Brazilian haplotypes, the grouping of which did not show a straightforward correspondence with geographic distribution. Median-joining and TCS network pointed to haplotypes 11 or 12 as the most likely ancestral ones. Mismatch distribution and the goodness-of-fit test (SSD estimate=0.0027; P=0.6999) indicated that the population from Chapada dos Guimarães experienced a demographic expansion, in agreement with the median-joining star-like pattern, although this finding could not be confirmed by Fu's Fs test. Am. J. Primatol. 69:1093,1104, 2007. © 2007 Wiley-Liss, Inc. [source] Construction of integrated genetic linkage maps of the tiger shrimp (Penaeus monodon) using microsatellite and AFLP markersANIMAL GENETICS, Issue 4 2010E.-M. You Summary The linkage maps of male and female tiger shrimp (P. monodon) were constructed based on 256 microsatellite and 85 amplified fragment length polymorphism (AFLP) markers. Microsatellite markers obtained from clone sequences of partial genomic libraries, tandem repeat sequences from databases and previous publications and fosmid end sequences were employed. Of 670 microsatellite and 158 AFLP markers tested for polymorphism, 341 (256 microsatellite and 85 AFLP markers) were used for genotyping with three F1 mapping panels, each comprising two parents and more than 100 progeny. Chi-square goodness-of-fit test (,2) revealed that only 19 microsatellite and 28 AFLP markers showed a highly significant segregation distortion (P < 0.005). Linkage analysis with a LOD score of 4.5 revealed 43 and 46 linkage groups in male and female linkage maps respectively. The male map consisted of 176 microsatellite and 49 AFLP markers spaced every ,11.2 cM, with an observed genome length of 2033.4 cM. The female map consisted of 171 microsatellite and 36 AFLP markers spaced every ,13.8 cM, with an observed genome length of 2182 cM. Both maps shared 136 microsatellite markers, and the alignment between them indicated 38 homologous pairs of linkage groups including the linkage group representing the sex chromosome. The karyotype of P. monodon is also presented. The tentative assignment of the 44 pairs of P. monodon haploid chromosomes showed the composition of forty metacentric, one submetacentric and three acrocentric chromosomes. Our maps provided a solid foundation for gene and QTL mapping in the tiger shrimp. [source] Two-sample Comparison Based on Prediction Error, with Applications to Candidate Gene Association StudiesANNALS OF HUMAN GENETICS, Issue 1 2007K. Yu Summary To take advantage of the increasingly available high-density SNP maps across the genome, various tests that compare multilocus genotypes or estimated haplotypes between cases and controls have been developed for candidate gene association studies. Here we view this two-sample testing problem from the perspective of supervised machine learning and propose a new association test. The approach adopts the flexible and easy-to-understand classification tree model as the learning machine, and uses the estimated prediction error of the resulting prediction rule as the test statistic. This procedure not only provides an association test but also generates a prediction rule that can be useful in understanding the mechanisms underlying complex disease. Under the set-up of a haplotype-based transmission/disequilibrium test (TDT) type of analysis, we find through simulation studies that the proposed procedure has the correct type I error rates and is robust to population stratification. The power of the proposed procedure is sensitive to the chosen prediction error estimator. Among commonly used prediction error estimators, the .632+ estimator results in a test that has the best overall performance. We also find that the test using the .632+ estimator is more powerful than the standard single-point TDT analysis, the Pearson's goodness-of-fit test based on estimated haplotype frequencies, and two haplotype-based global tests implemented in the genetic analysis package FBAT. To illustrate the application of the proposed method in population-based association studies, we use the procedure to study the association between non-Hodgkin lymphoma and the IL10 gene. [source] AN EXACT TEST FOR HAZARD SIMILARITYAUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 2 2009Kais Hamza Summary An exact test is developed for hazard similarity and in particular for exponentiality. This test is distinct from more common goodness-of-fit tests such as the Kolmogorov,Smirnov goodness-of-fit test, as it does not require full specification of the null distribution. This test is obtained through a characterization of hazard-similar distributions and a generalization of Fisher's test for association. [source] A Goodness-of-fit Test for the Marginal Cox Model for Correlated Interval-censored Failure Time DataBIOMETRICAL JOURNAL, Issue 6 2006Lianming Wang Abstract The marginal Cox model approach is perhaps the most commonly used method in the analysis of correlated failure time data (Cai, 1999; Cai and Prentice, 1995; Lin, 1994; Wei, Lin and Weissfeld, 1989). It assumes that the marginal distributions for the correlated failure times can be described by the Cox model and leaves the dependence structure completely unspecified. This paper discusses the assessment of the marginal Cox model for correlated interval-censored data and a goodness-of-fit test is presented for the problem. The method is applied to a set of correlated interval-censored data arising from an AIDS clinical trial. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Partly Functional Temporal Process Regression with Semiparametric Profile Estimating FunctionsBIOMETRICS, Issue 2 2009Jun Yan Summary Marginal mean models of temporal processes in event time data analysis are gaining more attention for their milder assumptions than the traditional intensity models. Recent work on fully functional temporal process regression (TPR) offers great flexibility by allowing all the regression coefficients to be nonparametrically time varying. The existing estimation procedure, however, prevents successive goodness-of-fit test for covariate coefficients in comparing a sequence of nested models. This article proposes a partly functional TPR model in the line of marginal mean models. Some covariate effects are time independent while others are completely unspecified in time. This class of models is very rich, including the fully functional model and the semiparametric model as special cases. To estimate the parameters, we propose semiparametric profile estimating equations, which are solved via an iterative algorithm, starting at a consistent estimate from a fully functional model in the existing work. No smoothing is needed, in contrast to other varying-coefficient methods. The weak convergence of the resultant estimators are developed using the empirical process theory. Successive tests of time-varying effects and backward model selection procedure can then be carried out. The practical usefulness of the methodology is demonstrated through a simulation study and a real example of recurrent exacerbation among cystic fibrosis patients. [source] Statistical Reconstruction of Transcription Factor Activity Using Michaelis,Menten KineticsBIOMETRICS, Issue 3 2007R. Khanin Summary The basic building block of a gene regulatory network consists of a gene encoding a transcription factor (TF) and the gene(s) it regulates. Considerable efforts have been directed recently at devising experiments and algorithms to determine TFs and their corresponding target genes using gene expression and other types of data. The underlying problem is that the expression of a gene coding for the TF provides only limited information about the activity of the TF, which can also be controlled posttranscriptionally. In the absence of a reliable technology to routinely measure the activity of regulators, it is of great importance to understand whether this activity can be inferred from gene expression data. We here develop a statistical framework to reconstruct the activity of a TF from gene expression data of the target genes in its regulatory module. The novelty of our approach is that we embed the deterministic Michaelis,Menten model of gene regulation in this statistical framework. The kinetic parameters of the gene regulation model are inferred together with the profile of the TF regulator. We also obtain a goodness-of-fit test to verify the fit of the model. The model is applied to a time series involving the Streptomyces coelicolor bacterium. We focus on the transcriptional activator cdaR, which is partly responsible for the production of a particular type of antibiotic. The aim is to reconstruct the activity profile of this regulator. Our approach can be extended to include more complex regulatory relationships, such as multiple regulatory factors, competition, and cooperativity. [source] A Goodness-of-Fit Test for Multinomial Logistic RegressionBIOMETRICS, Issue 4 2006Jelle J. Goeman Summary This article presents a score test to check the fit of a logistic regression model with two or more outcome categories. The null hypothesis that the model fits well is tested against the alternative that residuals of samples close to each other in covariate space tend to deviate from the model in the same direction. We propose a test statistic that is a sum of squared smoothed residuals, and show that it can be interpreted as a score test in a random effects model. By specifying the distance metric in covariate space, users can choose the alternative against which the test is directed, making it either an omnibus goodness-of-fit test or a test for lack of fit of specific model variables or outcome categories. [source] Paired comparisons for the evaluation of crispness of cereal flakes by untrained assessors: correlation with descriptive analysis and acoustic measurementsJOURNAL OF CHEMOMETRICS, Issue 3 2005Philippe Courcoux Abstract This study investigates the effectiveness of the paired comparison method in the evaluation of a complex sensory attribute by untrained assessors. The crispness perception of cereal flakes by a panel of 100 consumers is measured using a complete block design, and the fitting of the Bradley,Terry,Luce model leads to a ranking of the samples on a crispness intensity scale. A log,linear formulation of the Bradley model provides insight into goodness-of-fit tests and allows the effects of covariates to be incorporated in the prediction of the sensory scores. Results show a high correlation between crispness assessment by consumers and rating of texture attributes by trained assessors. Acoustic emission is shown to have a significant effect on crispness perception, and the power spectra of signals recorded during compression provide a prediction of the crispness of cereal flakes. Copyright © 2005 John Wiley & Sons, Ltd. [source] The Effect of the Estimation on Goodness-of-Fit Tests in Time Series ModelsJOURNAL OF TIME SERIES ANALYSIS, Issue 4 2005Yue Fang Abstract., We analyze, by simulation, the finite-sample properties of goodness-of-fit tests based on residual autocorrelation coefficients (simple and partial) obtained using different estimators frequently used in the analysis of autoregressive moving-average time-series models. The estimators considered are unconditional least squares, maximum likelihood and conditional least squares. The results suggest that although the tests based on these estimators are asymptotically equivalent for particular models and parameter values, their sampling properties for samples of the size commonly found in economic applications can differ substantially, because of differences in both finite-sample estimation efficiencies and residual regeneration methods. [source] Weaknesses of goodness-of-fit tests for evaluating propensity score models: the case of the omitted confounder,PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, Issue 4 2005Sherry Weitzen PhD Abstract Purpose Propensity scores are used in observational studies to adjust for confounding, although they do not provide control for confounders omitted from the propensity score model. We sought to determine if tests used to evaluate logistic model fit and discrimination would be helpful in detecting the omission of an important confounder in the propensity score. Methods Using simulated data, we estimated propensity scores under two scenarios: (1) including all confounders and (2) omitting the binary confounder. We compared the propensity score model fit and discrimination under each scenario, using the Hosmer,Lemeshow goodness-of-fit (GOF) test and the c-statistic. We measured residual confounding in treatment effect estimates adjusted by the propensity score omitting the confounder. Results The GOF statistic and discrimination of propensity score models were the same for models excluding an important predictor of treatment compared to the full propensity score model. The GOF test failed to detect poor model fit for the propensity score model omitting the confounder. C-statistics under both scenarios were similar. Residual confounding was observed from using the propensity score excluding the confounder (range: 1,30%). Conclusions Omission of important confounders from the propensity score leads to residual confounding in estimates of treatment effect. However, tests of GOF and discrimination do not provide information to detect missing confounders in propensity score models. Our findings suggest that it may not be necessary to compute GOF statistics or model discrimination when developing propensity score models. Copyright © 2004 John Wiley & Sons, Ltd. [source] AFLP-based genetic linkage maps of the blue mussel (Mytilus edulis)ANIMAL GENETICS, Issue 4 2007D. Lallias Summary We report the construction of the first genetic linkage map in the blue mussel, Mytilus edulis. AFLP markers were used in 86 full-sib progeny from a controlled pair mating, applying a double pseudo-test cross strategy. Thirty-six primer pairs generated 2354 peaks, of which 791 (33.6%) were polymorphic in the mapping family. Among those, 341 segregated through the female parent, 296 through the male parent (type 1:1) and 154 through both parents (type 3:1). Chi-square goodness-of-fit tests revealed that 71% and 73% of type 1:1 and 3:1 markers respectively segregated according to Mendelian inheritance. Sex-specific linkage maps were built with mapmaker 3.0 software. The female framework map consisted of 121 markers ordered into 14 linkage groups, spanning 862.8 cM, with an average marker spacing of 8.0 cM. The male framework map consisted of 116 markers ordered into 14 linkage groups, spanning 825.2 cM, with an average marker spacing of 8.09 cM. Genome coverage was estimated to be 76.7% and 75.9% for the female and male framework maps respectively, rising to 85.8% (female) and 86.2% (male) when associated markers were included. Twelve probable homologous linkage group pairs were identified and a consensus map was built for nine of these homologous pairs based on multiple and parallel linkages of 3:1 markers, spanning 816 cM, with joinmap 4.0 software. [source] DATA-DRIVEN SMOOTH TESTS AND A DIAGNOSTIC TOOL FOR LACK-OF-FIT FOR CIRCULAR DATAAUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 4 2009Heidi Wouters Summary Two contributions to the statistical analysis of circular data are given. First we construct data-driven smooth goodness-of-fit tests for the circular von Mises assumption. As a second method, we propose a new graphical diagnostic tool for the detection of lack-of-fit for circular distributions. We illustrate our methods on two real datasets. [source] AN EXACT TEST FOR HAZARD SIMILARITYAUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 2 2009Kais Hamza Summary An exact test is developed for hazard similarity and in particular for exponentiality. This test is distinct from more common goodness-of-fit tests such as the Kolmogorov,Smirnov goodness-of-fit test, as it does not require full specification of the null distribution. This test is obtained through a characterization of hazard-similar distributions and a generalization of Fisher's test for association. [source] Residual-Based Diagnostics for Structural Equation ModelsBIOMETRICS, Issue 1 2009B. N. Sánchez Summary Classical diagnostics for structural equation models are based on aggregate forms of the data and are ill suited for checking distributional or linearity assumptions. We extend recently developed goodness-of-fit tests for correlated data based on subject-specific residuals to structural equation models with latent variables. The proposed tests lend themselves to graphical displays and are designed to detect misspecified distributional or linearity assumptions. To complement graphical displays, test statistics are defined; the null distributions of the test statistics are approximated using computationally efficient simulation techniques. The properties of the proposed tests are examined via simulation studies. We illustrate the methods using data from a study of in utero lead exposure. [source] | |||||||||||||||||||