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- ELSEVIER SCIENCE BV
- TAYLOR & FRANCIS INC
- Washington, DC: World Bank
- Elsevier
- The Econometric Society
- Universidade Carlos III de Madrid
- Université de Montréal
- Routledge, Taylor & Francis Group
- Universidade Cornell
- American Statistical Association
- Laboratório Nacional de Energia e Geologia
- Suntory and Toyota International Centres for Economics and Related Disciplines, London School of Economics and Political Science
- Suntory Centre, London School of Economics and Political Science
- Mais Publicadores...

## ‣ On estimation and influence diagnostics for zero-inflated negative binomial regression models

Fonte: ELSEVIER SCIENCE BV
Publicador: ELSEVIER SCIENCE BV

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

27.678203%

#Bootstrap#EM algorithm#Global influence#Local influence#Negative binomial distribution#Zero inflated models#MIXED MODELS#COUNT DATA#POISSON MODEL#CENSORED-DATA#JACKKNIFE

The zero-inflated negative binomial model is used to account for overdispersion detected in data that are initially analyzed under the zero-Inflated Poisson model A frequentist analysis a jackknife estimator and a non-parametric bootstrap for parameter estimation of zero-inflated negative binomial regression models are considered In addition an EM-type algorithm is developed for performing maximum likelihood estimation Then the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and some ways to perform global influence analysis are derived In order to study departures from the error assumption as well as the presence of outliers residual analysis based on the standardized Pearson residuals is discussed The relevance of the approach is illustrated with a real data set where It is shown that zero-inflated negative binomial regression models seems to fit the data better than the Poisson counterpart (C) 2010 Elsevier B V All rights reserved; Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP); CNPq - Brazil

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## ‣ A Log-Linear Regression Model for the Beta-Weibull Distribution

Fonte: TAYLOR & FRANCIS INC
Publicador: TAYLOR & FRANCIS INC

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

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#Beta Weibull distribution#Censored data#Log-Weibull regression#Residual analysis#Sensitivity analysis#Survival function#LOCAL INFLUENCE#CENSORED-DATA#INFLUENCE DIAGNOSTICS#JACKKNIFE#RESIDUALS

We introduce the log-beta Weibull regression model based on the beta Weibull distribution (Famoye et al., 2005; Lee et al., 2007). We derive expansions for the moment generating function which do not depend on complicated functions. The new regression model represents a parametric family of models that includes as sub-models several widely known regression models that can be applied to censored survival data. We employ a frequentist analysis, a jackknife estimator, and a parametric bootstrap for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Further, for different parameter settings, sample sizes, and censoring percentages, several simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We define martingale and deviance residuals to evaluate the model assumptions. The extended regression model is very useful for the analysis of real data and could give more realistic fits than other special regression models.; CNPq; CAPES

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## ‣ The log-exponentiated Weibull regression model for interval-censored data

Fonte: ELSEVIER SCIENCE BV
Publicador: ELSEVIER SCIENCE BV

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

27.688682%

#INFLUENCE DIAGNOSTICS#LOCAL INFLUENCE#MIXED MODELS#FAMILY#SENSITIVITY#JACKKNIFE#RESIDUALS#Computer Science, Interdisciplinary Applications#Statistics & Probability

In interval-censored survival data, the event of interest is not observed exactly but is only known to occur within some time interval. Such data appear very frequently. In this paper, we are concerned only with parametric forms, and so a location-scale regression model based on the exponentiated Weibull distribution is proposed for modeling interval-censored data. We show that the proposed log-exponentiated Weibull regression model for interval-censored data represents a parametric family of models that include other regression models that are broadly used in lifetime data analysis. Assuming the use of interval-censored data, we employ a frequentist analysis, a jackknife estimator, a parametric bootstrap and a Bayesian analysis for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Furthermore, for different parameter settings, sample sizes and censoring percentages, various simulations are performed; in addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to a modified deviance residual in log-exponentiated Weibull regression models for interval-censored data. (C) 2009 Elsevier B.V. All rights reserved.; CNPq; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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## ‣ Bias-corrected estimators for dispersion models with dispersion covariates

Fonte: ELSEVIER SCIENCE BV
Publicador: ELSEVIER SCIENCE BV

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

27.25974%

#Dispersion models#Dispersion covariates#Nonlinear models#Bias-correction#MAXIMUM-LIKELIHOOD ESTIMATORS#GENERALIZED LINEAR-MODELS#FAMILY NONLINEAR MODELS#PARAMETER ORTHOGONALITY#RESIDUALS#Statistics & Probability

In this paper we discuss bias-corrected estimators for the regression and the dispersion parameters in an extended class of dispersion models (Jorgensen, 1997b). This class extends the regular dispersion models by letting the dispersion parameter vary throughout the observations, and contains the dispersion models as particular case. General formulae for the O(n(-1)) bias are obtained explicitly in dispersion models with dispersion covariates, which generalize previous results obtained by Botter and Cordeiro (1998), Cordeiro and McCullagh (1991), Cordeiro and Vasconcellos (1999), and Paula (1992). The practical use of the formulae is that we can derive closed-form expressions for the O(n(-1)) biases of the maximum likelihood estimators of the regression and dispersion parameters when the information matrix has a closed-form. Various expressions for the O(n(-1)) biases are given for special models. The formulae have advantages for numerical purposes because they require only a supplementary weighted linear regression. We also compare these bias-corrected estimators with two different estimators which are also bias-free to order O(n(-1)) that are based on bootstrap methods. These estimators are compared by simulation. (C) 2011 Elsevier B.V. All rights reserved.

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## ‣ On estimation and influence diagnostics for zero-inflated negative binomial regression models

Fonte: ELSEVIER SCIENCE BV
Publicador: ELSEVIER SCIENCE BV

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

27.678203%

#Bootstrap#EM algorithm#Global influence#Local influence#Negative binomial distribution#Zero inflated models

The zero-inflated negative binomial model is used to account for overdispersion detected in data that are initially analyzed under the zero-Inflated Poisson model A frequentist analysis a jackknife estimator and a non-parametric bootstrap for parameter estimation of zero-inflated negative binomial regression models are considered In addition an EM-type algorithm is developed for performing maximum likelihood estimation Then the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and some ways to perform global influence analysis are derived In order to study departures from the error assumption as well as the presence of outliers residual analysis based on the standardized Pearson residuals is discussed The relevance of the approach is illustrated with a real data set where It is shown that zero-inflated negative binomial regression models seems to fit the data better than the Poisson counterpart (C) 2010 Elsevier B V All rights reserved; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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## ‣ Quantitative body DW-MRI biomarkers uncertainty estimation using Unscented Wild-bootstrap ⋆

Fonte: PubMed
Publicador: PubMed

Tipo: Artigo de Revista Científica

Publicado em //2011
Português

Relevância na Pesquisa

28.326338%

We present a new method for the uncertainty estimation of diffusion parameters for quantitative body DW-MRI assessment. Diffusion parameters uncertainty estimation from DW-MRI is necessary for clinical applications that use these parameters to assess pathology. However, uncertainty estimation using traditional techniques requires repeated acquisitions, which is undesirable in routine clinical use. Model-based bootstrap techniques, for example, assume an underlying linear model for residuals rescaling and cannot be utilized directly for body diffusion parameters uncertainty estimation due to the non-linearity of the body diffusion model. To offset this limitation, our method uses the Unscented transform to compute the residuals rescaling parameters from the non-linear body diffusion model, and then applies the wild-bootstrap method to infer the body diffusion parameters uncertainty. Validation through phantom and human subject experiments shows that our method identify the regions with higher uncertainty in body DWI-MRI model parameters correctly with realtive error of ~36% in the uncertainty values.

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## ‣ Time-varying effect moderation using the structural nested mean model: estimation using inverse-weighted regression with residuals

Fonte: PubMed
Publicador: PubMed

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

27.688682%

This article considers the problem of examining time-varying causal effect moderation using observational, longitudinal data in which treatment, candidate moderators, and possible confounders are time varying. The structural nested mean model (SNMM) is used to specify the moderated time-varying causal effects of interest in a conditional mean model for a continuous response given time-varying treatments and moderators. We present an easy-to-use estimator of the SNMM that combines an existing regression-with-residuals (RR) approach with an inverse-probability-of-treatment weighting (IPTW) strategy. The RR approach has been shown to identify the moderated time-varying causal effects if the time-varying moderators are also the sole time-varying confounders. The proposed IPTW+RR approach provides estimators of the moderated time-varying causal effects in the SNMM in the presence of an additional, auxiliary set of known and measured time-varying confounders. We use a small simulation experiment to compare IPTW+RR versus the traditional regression approach and to compare small and large sample properties of asymptotic versus bootstrap estimators of the standard errors for the IPTW+RR approach. This article clarifies the distinction between time-varying moderators and time-varying confounders. We illustrate the methodology in a case study to assess if time-varying substance use moderates treatment effects on future substance use.

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## ‣ Growth Empirics and Reality

Fonte: Washington, DC: World Bank
Publicador: Washington, DC: World Bank

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

27.688682%

#AUTOREGRESSIVE MOVING AVERAGE#AVERAGE PROCESS#BAYES FACTOR#BAYESIAN STATISTICS#BENCHMARK#BOOTSTRAP#BUSINESS CYCLE#CALCULATION#CIVIL LIBERTIES#COMPUTATION#COUNTRY DATA

This article questions current empirical
practice in the study of growth. It argues that much of the
modern empirical growth literature is based on assumptions
about regressors, residuals, and parameters that are
implausible from the perspective of both economic theory and
the historical experiences of the countries under study.
Many of these problems, it argues, are forms of violations
of an exchangeability assumption that implicitly underlies
standard growth exercises. The article shows that these
implausible assumptions can be relaxed by allowing for
uncertainty in model specification. Model uncertainty
consists of two types: theory uncertainty, which relates to
which growth determinants should be included in a model; and
heterogeneity uncertainty, which relates to which
observations in a data set constitute draw from the same
statistical model. The article proposes ways to account for
both theory and heterogeneity uncertainty. Finally, using an
explicit decision-theoretic framework, the authors describe
how one can engage in policy-relevant empirical analysis.

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## ‣ Nonparametric Tests for Conditional Symmetry in Dynamic Models

Fonte: Elsevier
Publicador: Elsevier

Tipo: Artigo de Revista Científica
Formato: text/plain; application/pdf

Publicado em //2007
Português

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#Omnibus tests#Symmetry tests#Conditional distributions#Time series#Empirical processes#Bootstrap#C12#C14#C15#Economía

This article proposes omnibus tests for conditional symmetry around a parametric function in a dynamic context. Conditional moments may not exist or may depend on the explanatory variables. Test statistics are suitable functionals of the empirical process of residuals and explanatory variables, whose limiting distribution under the null is nonpivotal. The tests are implemented with the assistance of a bootstrap method, which is justified assuming very mild regularity conditions on the specification of the center of symmetry and the underlying serial dependence structure. Finite sample properties are examined by means of a Monte Carlo experiment.

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## ‣ A simple and general test for white noise

Fonte: The Econometric Society
Publicador: The Econometric Society

Tipo: Trabalho em Andamento
Formato: application/pdf

Publicado em /08/2004
Português

Relevância na Pesquisa

27.25974%

This article considers testing that a time series is uncorrelated when it possibly exhibits some form of dependence. Contrary to the currently employed tests that require selecting arbitrary user-chosen numbers to compute the associated tests statistics, we consider a test statistic that is very simple to use because it does not require any user chosen number and because its asymptotic null distribution is standard under general weak dependent conditions, and hence, asymptotic critical values are readily available. We consider the case of testing that the raw data is white noise, and also consider the case of applying the test to the residuals of an ARMA model. Finally, we also study finite sample performance.

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## ‣ An improved bootstrap test of stochastic dominance

Fonte: Universidade Carlos III de Madrid
Publicador: Universidade Carlos III de Madrid

Tipo: Trabalho em Andamento
Formato: application/pdf

Publicado em 17/07/2009
Português

Relevância na Pesquisa

28.032285%

We propose a new method of testing stochastic dominance that improves on existing tests
based on the standard bootstrap or subsampling. The method admits prospects involving infinite
as well as finite dimensional unknown parameters, so that the variables are allowed to be
residuals from nonparametric and semiparametric models. The proposed bootstrap tests have
asymptotic sizes that are less than or equal to the nominal level uniformly over probabilities in
the null hypothesis under regularity conditions. This paper also characterizes the set of
probabilities that the asymptotic size is exactly equal to the nominal level uniformly. As our
simulation results show, these characteristics of our tests lead to an improved power property in
general. The improvement stems from the design of the bootstrap test whose limiting behavior
mimics the discontinuity of the original test’s limiting distribution

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## ‣ Significance testing in nonparametric regression base on the bootstrap

Fonte: Universidade Carlos III de Madrid
Publicador: Universidade Carlos III de Madrid

Tipo: Trabalho em Andamento
Formato: application/pdf

Publicado em /10/1998
Português

Relevância na Pesquisa

37.897397%

#Nonparametric regression#seleccion of variable#higher order kermels#marked empirical processes#Wild bootstrap#restrictions on nonparametric curves#Estadística

We propose a test for selecting explanatory variables in nonparametric regression. The test does not need to estimate the conditional expectation function given all the variables but only those which are significant under the null hypothesis. This feature is compntationally convenient and solves, in part, the problem of the "curse of dimensionality" when selecting regressors in a nonparametric context. The proposed test statistic is based on functionals of an empirical process marked by nonparametric residuals. Contiguous alternatives, converging to the null at a rate n-1I2 can be detected. The asymptotic null distribution of the statistic depends on certain features of the data generating process, and asymptotic tests are difficult to implement except in rare circumstances. We justify the consistency of two bootstrap tests easy to implement, which exhibit good level accuracy for fairly small samples, according to the Monte Carlo simulations reported. These results are also applicable to test other interesting restrictions on nonparametric regression curves, like partial linearity and conditional independence.

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## ‣ Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models

Fonte: Université de Montréal
Publicador: Université de Montréal

Tipo: Artigo de Revista Científica
Formato: 219916 bytes; application/pdf

Português

Relevância na Pesquisa

27.678203%

#modèle d’évaluation d’actifs financiers#CAPM#efficacité moyenne-variance#non-normalité#modèle de régression multivarié#hypothèse uniforme linéaire#test de Monte Carlo#boot-strap#paramètre de nuisance#test de spécification#diagnostics

In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowing for unspecified parameters in the error distributions (e.g., the degrees of freedom in the case of the Student t distribution). The multivariate tests are based on properly standardized multivariate residuals to ensure invariance to MLR coefficients and error covariances. We consider tests for serial correlation, tests for multivariate GARCH and sign-type tests against general dependencies and asymmetries. The procedures proposed provide exact versions of those applied in Shanken (1990) which consist in combining univariate specification tests. Specifically, we combine tests across equations using the MC test procedure to avoid Bonferroni-type bounds. Since non-Gaussian based tests are not pivotal, we apply the “maximized MC” (MMC) test method [Dufour (2002)], where the MC p-value for the tested hypothesis (which depends on nuisance parameters) is maximized (with respect to these nuisance parameters) to control the test’s significance level. The tests proposed are applied to an asset pricing model with observable risk-free rates...

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## ‣ Exact Skewness-Kurtosis Tests for Multivariate Normality and Goodness-of-fit in Multivariate Regressions with Application to Asset Pricing Models

Fonte: Université de Montréal
Publicador: Université de Montréal

Tipo: Artigo de Revista Científica
Formato: 225374 bytes; application/pdf

Português

Relevância na Pesquisa

27.897397%

#modèle de régression multivarié#test d’ajustement#test de normalité#normalité multi-variée#t de Student#mélange de lois normales#distribution stable#test de spécification#diagnostic#test exact#test de Monte Carlo

We study the problem of testing the error distribution in a multivariate linear regression (MLR) model. The tests are functions of appropriately standardized multivariate least squares residuals whose distribution is invariant to the unknown cross-equation error covariance matrix. Empirical multivariate skewness and kurtosis criteria are then compared to simulation-based estimate of their expected value under the hypothesized distribution. Special cases considered include testing multivariate normal, Student t; normal mixtures and stable error models. In the Gaussian case, finite-sample versions of the standard multivariate skewness and kurtosis tests are derived. To do this, we exploit simple, double and multi-stage Monte Carlo test methods. For non-Gaussian distribution families involving nuisance parameters, confidence sets are derived for the the nuisance parameters and the error distribution. The procedures considered are evaluated in a small simulation experi-ment. Finally, the tests are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995.; Dans cet article, nous proposons des tests sur la forme de la distribution des erreurs dans un modèle de régression linéaire multivarié (RLM). Les tests que nous développons sont fonction des résidus obtenus par moindres carrés multivariés...

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## ‣ An Evaluation of Bootstrap Methods for Outlier Detection in Least Squares Regression

Fonte: Routledge, Taylor & Francis Group
Publicador: Routledge, Taylor & Francis Group

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

38.331829%

#Keywords: Case-based resampling#Error distribution#Externally Studentized residuals#Internally Studentized residuals#Jackknife-after-bootstrap#Residual-based resampling#RSTUDENT

Outlier detection is a critical part of data analysis, and the use of Studentized residuals from regression models fit using least squares is a very common approach to identifying discordant observations in linear regression problems. In this paper we pro

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## ‣ Resampling Residuals: Robust Estimators of Error and Fit for Evolutionary Trees and Phylogenomics

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 29/12/2009
Português

Relevância na Pesquisa

27.913367%

Phylogenomics, even more so than traditional phylogenetics, needs to
represent the uncertainty in evolutionary trees due to systematic error. Here
we illustrate the analysis of genome-scale alignments of yeast, using robust
measures of the additivity of the fit of distances to tree when using flexi
Weighted Least Squares. A variety of DNA and protein distances are used. We
explore the nature of the residuals, standardize them, and then create
replicate data sets by resampling these residuals. Under the model, the results
are shown to be very similar to the conventional sequence bootstrap. With real
data they show up uncertainty in the tree that is either due to underestimating
the stochastic error (hence massively overestimating the effective sequence
length) and/or systematic error. The methods are extended to the very fast BME
criterion with similarly promising results.; Comment: 29 pages, including 11 figures and 2 tables

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## ‣ Outlier Robust Model Selection in Linear Regression

Fonte: American Statistical Association
Publicador: American Statistical Association

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

28.189656%

#Keywords: Bootstrap model selection#Outlier#Robust model selection#Schwarz bayesian information criterion#Stratified bootstrap

We propose a new approach to the selection of regression models based on combining a robust penalized criterion and a robust conditional expected prediction loss function that is estimated using a stratified bootstrap. Both components of the procedure use robust criteria (i.e., robust p-functions) rather than squared error loss to reduce the effects of large residuals and poor bootstrap samples. A key idea is to separate estimation from model selection by choosing estimators separately from the p-function. Using the stratified bootstrap further reduces the likelihood of obtaining poor bootstrap samples. We show that the model selection procedure is consistent under some conditions and works well in our simulations. In particular, we find that simultaneous minimization of prediction error and conditional expected prediction loss is better than separate minimization of the prediction error or the conditional expected prediction loss.

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## ‣ Electrochemical impedance spectroscopy modeling using the dis-tribution of relaxation times and error analysis for fuel cells

Fonte: Laboratório Nacional de Energia e Geologia
Publicador: Laboratório Nacional de Energia e Geologia

Tipo: Conferência ou Objeto de Conferência

Publicado em //2013
Português

Relevância na Pesquisa

27.25974%

#Fuel-cell #Impedance spectroscopy#Distribution of relaxation times#Parameter estimation#Residuals bootstrap

This paper proposes a new approach to determine the distribution of relaxation times (DRT) directly from the electro-chemical impedance spectroscopy (EIS) data, i.e. without the use of an equivalent electrical circuit model. The method uses a generalized fractional-order Laguerre basis to represent EIS where both the parameters of the basis and their co-efficients are estimated by solving a nonconvex minimization problem. Furthermore, the DRT confidence region is de-termined to assess the accuracy and precision of the DRT estimate. The approach is applied to analyze the dominant dynamic properties of an open-cathode hydrogen fuel-cell under different current and air-flow conditions. Results showed that the estimated DRT closely reconstructs EIS data even when there is a higher variance at smaller relaxation times.

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## ‣ An alternative bootstrap to moving blocks for time series regression models

Fonte: Suntory and Toyota International Centres for Economics and Related Disciplines, London School of Economics and Political Science
Publicador: Suntory and Toyota International Centres for Economics and Related Disciplines, London School of Economics and Political Science

Tipo: Monograph; NonPeerReviewed
Formato: application/pdf

Publicado em /05/2003
Português

Relevância na Pesquisa

28.107144%

The purpose of this paper is to introduce and examine two alternative, although similar, approaches to the Moving Blocks and subsampling Bootstraps to bootstrapping the estimator of the parameters for time series regression models. More specifically, the first bootstrap is based on resampling from the normalised discrete Fourier transform of the residuals of the model, whereas the second is from the residuals of the model itself. It is shown that the bootstraps are asymptotically valid under quite mild conditions. As a consequence of the result we are able to eleminate the apparent drawback of choosing the block length in empirical examples. A small Monte Carlo study of finite sample performance is included.

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## ‣ Bootstrap tests of stochastic dominance with asymptotic similarity on the boundary

Fonte: Suntory Centre, London School of Economics and Political Science
Publicador: Suntory Centre, London School of Economics and Political Science

Tipo: Monograph; NonPeerReviewed
Formato: application/pdf

Publicado em /02/2008
Português

Relevância na Pesquisa

27.897397%

We propose a new method of testing stochastic dominance which improves on existing tests based on bootstrap or subsampling. Our test requires estimation of the contact sets between the marginal distributions. Our tests have asymptotic sizes that are exactly equal to the nominal level uniformly over the boundary points of the null hypothesis and are therefore valid over the whole null hypothesis. We also allow the prospects to be indexed by infinite as well as finite dimensional unknown parameters, so that the variables may be residuals from nonparametric and semiparametric models. Our simulation results show that our tests are indeed more powerful than the existing subsampling and recentered bootstrap.

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