Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 6 March 2010

F., Konietschke , A.C., Bathke , L.A., Hothorn , E., Brunner

The several sample case of the so-called nonparametric Behrens- Fisher problem in repeated measures designs is considered. That is, even under the null hypothesis, the marginal distribution functions in the different groups may have different shapes, and are not assumed to be equal. Moreover, the continuity of the marginal distribution functions is not required so that data with ties and, particularly, ordered categorical data are covered by this model. A multiple relative treatment effect is defined which can be estimated by using the mid-ranks of the observations within pairwise samples. The asymptotic distribution of this estimator is derived, along with...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 6 March 2010

Xuerong Meggie, Wen

The requirement of constant censoring parameter β in Koziol–Green (KG) model is too restrictive. When covariates are present, the conditional KG model (Veraverbeke and Cadarso-Suárez, 2000) which allows β to be dependent on the covariates is more realistic. In this paper, using sufficient dimension reduction methods, we provide a model-free diagnostic tool to test if β is a function of the covariates. Our method also allows us to conduct a model-free selection of the related covariates. A simulation study and a real data analysis are also included to illustrate our approach.

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 6 March 2010

Juan Carlos, Escanciano , Silvia, Mayoral

A general method for testing the martingale difference hypothesis is proposed. The new tests are data-driven smooth tests based on the principal components of certain marked empirical processes that are asymptotically distribution-free, with critical values that are already tabulated. The smooth tests are shown to be optimal in a semiparametric sense discussed in the paper, and they are robust to conditional heteroscedasticity of unknown form. A simulation study shows that the data-driven smooth tests perform very well for a wide range of realistic alternatives and have more power than omnibus and other competing tests. Finally, an application to the S&P...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 6 March 2010

Megan Dailey, Higgs , Jennifer A., Hoeting

We propose a model for a point-referenced spatially correlated ordered categorical response and methodology for inference. Models and methods for spatially correlated continuous response data are widespread, but models for spatially correlated categorical data, and especially ordered multi-category data, are less developed. Bayesian models and methodology have been proposed for the analysis of independent and clustered ordered categorical data, and also for binary and count point-referenced spatial data. We combine and extend these methods to describe a Bayesian model for point-referenced (as opposed to lattice) spatially correlated ordered categorical data. We include simulation results and show that our model offers...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 2 March 2010

Christopher J., Marley , David C., Woods

Various design and model selection methods are available for supersaturated designs having more factors than runs but little research is available on their comparison and evaluation. Simulated experiments are used to evaluate the use of E(s2)-optimal and Bayesian D-optimal designs, and to compare three analysis strategies representing regression, shrinkage and a novel model-averaging procedure. Suggestions are made for choosing the values of the tuning constants for each approach. Findings include that (i) the preferred analysis is via shrinkage; (ii) designs with similar numbers of runs and factors can be effective for a considerable number of active effects of only moderate...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 2 March 2010

Ying, Chen , Chi Kin, Chan , Bartholomew P.K., Leung

Although three-level factorial designs with quantitative factors are not the most efficient way to fit a second-order polynomial model, they often find some application, when the factors are qualitative. The three-level orthogonal designs with qualitative factors are frequently used, e.g., in agriculture, in clinical trials and in parameter designs. It is practically unavoidable that, because of the limitation of experimental materials or time-related constraint, we often have to keep the number of experiments as small as possible and to consider the effects of many factors and interactions simultaneously so that most of such designs are saturated or nearly saturated. An...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 2 March 2010

Math J.J.M., Candel , Gerard J.P., Van Breukelen

The efficiency loss due to varying cluster sizes in trials where treatments induce clustering of observations in one of two treatment arms is examined. Such designs may arise when comparing group therapy to a condition with only medication or a condition not involving any kind of treatment. For maximum likelihood estimation in a mixed effects linear regression, asymptotic relative efficiencies (RE) of unequal versus equal cluster sizes in terms of the D-criterion and Ds-criteria are derived. A Monte Carlo simulation for small sample sizes shows these asymptotic REs to be very accurate for the Ds-criterion of the fixed effects and...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 2 March 2010

N.K., Unnikrishnan

The present paper develops an outlier model suitable for problems wherein identification of outliers is essential and, applied areas of statistics are abound with such examples. One of the peculiarities of outliers in survey sampling is that there could be observed as well as unobserved outliers; the paper assumes that there are no unobserved outliers. We use a generalized linear model (GLM) with higher variances for the outlying units. Count data are treated through overdispersed GLM of Gelfand and Dalal (1990). Error components of the link function are assumed to have scale-mixtures of normal distributions. The framework covers both standard...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 26 February 2010

Kuo-Chin, Lin

Longitudinal studies involving categorical responses are extensively applied in many fields of research and often fitted by generalized estimating equations (GEE) approach and generalized linear mixed models (GLMMs). The assessment of model fit is an important issue for model inference. The purpose of this article is to extend Pan’s (2002a) goodness-of-fit tests for GEE models with longitudinal binary data to the tests for logistic proportional odds models with longitudinal ordinal data. Two proposed methods based on Pearson chi-squared test and unweighted sum of residual squares are developed, and the approximate expectations and variances of the test statistics are easily computed....

Publication year: 2010
Source: Computational Statistics & Data Analysis, Volume 54, Issue 6, 1 June 2010, Pages iii-iv

[No author name available]

Publication year: 2010
Source: Computational Statistics & Data Analysis, Volume 54, Issue 6, 1 June 2010, Pages v-vii

[No author name available]

Publication year: 2010
Source: Computational Statistics & Data Analysis, Volume 54, Issue 5, 1 May 2010, Pages iii-v

[No author name available]

Publication year: 2010
Source: Computational Statistics & Data Analysis, Volume 54, Issue 5, 1 May 2010, Pages vi-vii

[No author name available]

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 24 February 2010

Sara Kherad, Pajouh , Olivier, Renaud

The ANOVA method and permutation tests, two inheritages of Fisher have been extensivley studied. Several permutation strategies have been proposed by others to obtain a distribution free test for factors in a fixed effect ANOVA (i.e. single error term ANOVA). The resulting tests are either approximate or exact. However, there exists no universal exact permutation test which can be applied to an arbitrary design to test a desired factor. An exact permutation strategy applicable to fixed effect analysis of variance is presented. The proposed method can be used to test any factor, even in the presence of higher order interactions....

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 23 February 2010

Cédric, Heuchenne , Ingrid, Van Keilegom

Suppose the random vector (X,Y) satisfies the regression model Y=m(X)+σ(X)ε, where m(⋅) is the conditional mean, σ2(⋅) is the conditional variance, and ε is independent of X. The covariate X is d-dimensional (d≥1), the response Y is one-dimensional, and m and σ are unknown but smooth functions. Goodness-of-fit tests for the parametric form of the error distribution are studied under this model, without assuming any parametric form for m or σ. The proposed tests are based on the difference between a nonparametric estimator of the error distribution and an estimator obtained under the null hypothesis of a parametric model. The...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 22 February 2010

Galen I., Papkov , David W., Scott

Data-driven research is often hampered by privacy restrictions in the form of limited datasets or graphical representations without the benefit of raw data. Nonparametric techniques that circumvent these issues by using local moment information, thereby extending the piecewise polynomial histograms, are developed. These methods utilize not only binned data counts, but also their conditional moments in order to better estimate the underlying density of the data. A particular polynomial spline density estimator can be found via penalized least-squares optimization with a roughness penalty. Two issues exist in the original algorithm: (1) local moments for empty bins are undefined and (2)...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 20 February 2010

Anastasios, Panagiotelis , Michael, Smith

We develop a Bayesian approach for the selection of skew in multivariate skew t distributions constructed through hidden conditioning in the manners suggested by either Azzalini and Capitanio (2003) or Sahu, Dey and Branco (2003). We show that the skew coefficients for each margin are the same for the standardized versions of both distributions. We introduce binary indicators to denote whether there is symmetry, or skew, in each dimension. We adopt a proper beta prior on each non-zero skew coefficient, and derive the corresponding prior on the skew parameters. In both distributions we show that as the degrees of freedom...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 20 February 2010

Dong Wan, Shin , Sangun, Park

Robust panel unit root tests are developed for cross-sectionally dependent multiple time series. The tests have limiting null distributions derived from standard normal distributions. A Monte-Carlo experiment shows that the tests have better finite sample robust performance than existing tests. Some Latin American real exchange rates revealing many outlying observations are analyzed to check the purchasing power parity (PPP) theory.

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 20 February 2010

Rajiv S., Menjoge , Roy E., Welsch

A diagnostic method along the lines of forward search is proposed to simultaneously study the effect of individual observations and features on the inferences made in linear regression. The method operates by appending dummy variables to the data matrix and performing backward selection on the augmented matrix. It outputs sequences of feature-outlier combinations which can be evaluated by similar plots to those of forward search and includes the capacity to incorporate prior knowledge, in order to mitigate issues such as collinearity. It also allows for alternative ways to understand the selection of the final model. The method is evaluated on...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 19 February 2010

Marieke E., Timmerman , Eva, Ceulemans , Henk A.L., Kiers , Maurizio, Vichi

Factorial K-means analysis (FKM) and Reduced K-means analysis (RKM) are clustering methods that aim at simultaneously achieving a clustering of the objects and a dimension reduction of the variables. Because a comprehensive comparison between FKM and RKM is lacking in the literature so far, a theoretical and simulation-based comparison between FKM and RKM is provided. It is shown theoretically how FKM’s versus RKM’s performances are affected by the presence of residuals within the clustering subspace and/or within its orthocomplement in the observed data. The simulation study confirmed that for both FKM and RKM, the cluster membership recovery generally deteriorates with...