Comp Stat and Data Analysis (CSDA)

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 1 September 2010

Yoshio, Takane , Kwanghee, Jung , Heungsun, Hwang

The growth curve model (GCM), also known as GMANOVA, is a useful technique for investigating patterns of change in repeated measurement data over time and examining the effects of predictor variables on temporal trajectories. The reduced rank feature had been introduced previously to GCM for capturing redundant information in the criterion variables in a parsimonious way. In this paper, a ridge type of regularization was incorporated to obtain better estimates of parameters. Separate ridge parameters were allowed in column and row regressions, and the generalized singular value decomposition (GSVD) was applied for rank reduction. It was shown that the regularized...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 1 September 2010

Jingyun, Yang , Vernon M., Chinchilli

Cohen’s kappa and weighted kappa statistics are the conventional methods that are used widely in measuring agreement for categorical responses. In this paper, we propose a general framework for the fixed-effects modeling of Cohen’s weighted kappa for bivariate multinomial data which reduces to Cohen’s weighted kappa under certain conditions and hence can be considered as a generalization of the conventional Cohen’s weighted kappa. Properties of the proposed method are investigated followed by an evaluation of the asymptotic performance through bootstrap simulation studies and two illustrative examples. Our derivation shows that the Fleiss-Cohen weights, one of the popular weight schemes for...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 31 August 2010

Rachidi, Kotchoni

A review of the theoretical properties of the GMM with a continuum of moment conditions is presented. Numerical methods for its implementation are discussed. A simulation study based on the stable distribution and an empirical application based on the autoregressive variance Gamma model are performed. Using the Alcoa price data, the findings suggest that investors require a positive premium for bearing the expected risk while a negative penalty is attached to unexpected risk.

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 30 August 2010

Badi H., Baltagi , Georges, Bresson , Alain, Pirotte

Various forecasts using panel data with spatial error correlation are compared using Monte Carlo experiments. The true data generating process is assumed to be a simple error component regression model with spatial remainder disturbances of the autoregressive or moving average type. The best linear unbiased predictor is compared with other forecasts ignoring spatial correlation, or ignoring heterogeneity due to the individual effects. In addition, the root mean squared error performance of these forecasts is examined under misspecification of the spatial error process, various spatial weight matrices, and heterogeneous rather than homogeneous panel data models.

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 30 August 2010

Dongik, Jang , Hee-Seok, Oh

This paper considers the problem of estimating curve and surface functions when the structures of an unknown function vary spatially. Classical approaches such as smoothing splines, which are controlled by a single smoothing parameter, are inefficient in estimating the underlying function that consists of different spatial structures. In this paper, we propose a blockwise method of fitting smoothing splines wherein the smoothing parameter λ varies spatially in order to accommodate possible spatial nonhomogeneity of the regression function. A key feature of the proposed blockwise method is the parameterization of a smoothing parameter function λ(x) that produces a continuous spatially adaptive...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 30 August 2010

Man-Lai, Tang , Wai-Yin, Poon , Leevan, Ling , Yijie, Liao , Hang-Wai, Chui

The asymptotic and exact conditional methods are widely used to compare two ordered multinomials. The asymptotic method is well known for its good performance when the sample size is sufficiently large. However, Brown et al. (2001) gave a contrary example in which this method performed liberally even when the sample size was large. In practice, when the sample size is moderate, the exact conditional method is a good alternative, but it is often criticised for its conservativeness. Exact unconditional methods are less conservative, but their computational burden usually renders them infeasible in practical applications. To address these issues, we develop an...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 27 August 2010

Chi-Chung, Wen , Yi-Hau, Chen

The Cox model with frailties has been popular for regression analysis of clustered event time data under right censoring. However, due to the lack of reliable computation algorithms, the frailty Cox model has been rarely applied to clustered current status data, where the clustered event times are subject to a special type of interval censoring such that we only observe for each event time whether it exceeds an examination (censoring) time or not. Motivated by the cataract dataset from a cross-sectional study, where bivariate current status data were observed for the occurrence of cataracts in the right and left eyes...

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

Christian, Kascha , Carsten, Trenkler

The finite-sample size and power properties of bootstrapped likelihood ratio systems cointegration tests are investigated via Monte Carlo simulations when the true lag order of the data generating process is unknown. Recursive bootstrap schemes are employed which differ in the way the lag order is chosen. The order is estimated by minimizing different information criteria and by combining the corresponding order estimates. In comparison to the standard asymptotic likelihood ratio test based on an estimated lag order, it is found that bootstrapping can lead to improvements in small samples even when the true lag order is unknown while the power...

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

Brent D., Burch

Using normal distribution assumptions, one can obtain confidence intervals for variance components in a variety of applications. A normal-based interval, which has exact coverage probability under normality, is usually constructed from a pivot so that the endpoints of the interval depend on the data as well as the distribution of the pivotal quantity. Alternatively, one can employ a point estimation technique to form a large-sample (or approximate) confidence interval. A commonly used approach to estimate variance components is the restricted maximum likelihood (REML) method. The endpoints of a REML-based confidence interval depend on the data and the asymptotic distribution of...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 18 August 2010

Jie, Mao , Zhongyi, Zhu , Wing K., Fung

When the selected parametric model for the covariance structure is far from the true one, the corresponding covariance estimator could have considerable bias. To balance the variability and bias of the covariance estimator, we employ a nonparametric method. In addition, as different mean structures may lead to different estimators of the covariance matrix, we choose a semiparametric model for the mean so as to provide a stable estimate of the covariance matrix. Based on the modified Cholesky decomposition of the covariance matrix, we construct the joint mean-covariance model by modeling the smooth functions using the spline method and estimate the...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 14 August 2010

D.K., Al-Mutairi , M.E., Ghitany , Ramesh C., Gupta

In this paper, we are interested in the estimation of the reliability coefficient R=P(X>Y), when the data on the minimum of two exponential samples, with random sample size, is available. The confidence intervals of R, based on maximum likelihood and bootstrap methods, are developed. The performance of these confidence intervals is studied through extensive simulation. A numerical example, based on a real data, is presented to illustrate the implementation of the proposed procedure.

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 14 August 2010

Elizabeth M., Hashimoto , Edwin M.M., Ortega , Gilberto A., Paula , Mauricio L., Barreto

In this study, regression models are evaluated for grouped survival data when the effect of censoring time is considered in the model and the regression structure is modeled through four link functions. The methodology for grouped survival data is based on life tables, and the times are grouped in k intervals so that ties are eliminated. Thus, the data modeling is performed by considering the discrete models of lifetime regression. The model parameters are estimated by using the maximum likelihood and jackknife methods. To detect influential observations in the proposed models, diagnostic measures based on case deletion, which are denominated...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 13 August 2010

Lei, Shi , Mei, Huang

A new method called stepwise local influence analysis is proposed to detect influential observations and to identify masking effects in dataset. Influential observations are detected step-by-step such that any highly influential observations identified in a previous step are removed from the perturbation in the next step. The process iterates until no further influential observations can be found. It is shown that this new method is very effective to identify the influential observations and has power to uncover the masking effects. Additionally, the issues of constraints on perturbation vectors and bench-mark determination are discussed. Several examples with regression models and linear...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 8 August 2010

Man-Suk, Oh , Dong Wan, Shin

In some applications involving comparison of treatment means, it is known a priori that population means are ordered in a certain way. In such situations, imposing constraints on the treatment means can greatly increase the effectiveness of statistical procedures.This paper proposes a unified Bayesian method which performs simultaneous comparison of treatment means and parameter estimation in ANOVA models with order constraints on the means. A continuous prior restricted to order constraints is employed, and posterior samples of parameters are generated using a Markov chain Monte Carlo method. Posterior probabilities of all possible hypotheses on the equality/inequality of treatment means are...

Publication year: 2010
Source: Computational Statistics & Data Analysis, In Press, Accepted Manuscript, Available online 7 August 2010

Jeong Eun, Min , Matthew D., Whiteside , Fiona S.L., Brinkman , Brad, McNeney , Jinko, Graham

Orthologs are genes in different species that have diverged from a common ancestral gene after speciation. In contrast, paralogs are genes that have diverged after a gene duplication event. For many comparative analyses, it is of interest to identify orthologs with similar function. Such orthologs tend to support species divergence (ssd-orthologs) in the sense that they have diverged only due to speciation, to the same relative degree as their species. However, due to incomplete sequencing or gene loss in a species, predicted orthologs can sometimes be paralogs or other non-ssd-orthologs. To increase the specificity of ssd-ortholog prediction, Fulton et al. (2006, BMC...

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

Jan F., Kiviet , Jerzy, Niemczyk

In designing Monte Carlo simulation studies for analyzing finite sample properties of econometric inference methods, one can use either IID drawings in each replication for any series of exogenous explanatory variables or condition on just one realization of these. The results will usually differ, as do their interpretations. Conditional and unconditional limiting distributions are often equivalent, thus yielding similar asymptotic approximations. However, when an estimator is inconsistent, its limiting distribution may change under conditioning. These phenomena are analyzed and numerically illustrated for OLS (ordinary least-squares) and IV (instrumental variables) estimators in single static linear simultaneous equations. The results obtained supplement–and...

Publication year: 2010
Source: Computational Statistics & Data Analysis, Volume 54, Issue 12, 1 December 2010, Pages iii-v

[No author name available]

Publication year: 2010
Source: Computational Statistics & Data Analysis, Volume 54, Issue 12, 1 December 2010, Pages vi-viii

[No author name available]

Publication year: 2010
Source: Computational Statistics & Data Analysis, Volume 54, Issue 12, 1 December 2010, Pages 2879-2882

Stefan, Van Aelst , Roy, Welsch , Ruben H., Zamar

Publication year: 2010
Source: Computational Statistics & Data Analysis, Volume 54, Issue 12, 1 December 2010, Pages 3379-3380

Jesse, Barlow , Lars, Eldén , Paolo, Foschi