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In recent years, some interested data can be recorded only if the values fall within an interval range, and the responses are often subject to censoring. Attempting to perform effective statistical analysis with censored, especially heavy-tailed and asymmetric data, can be difficult. In this paper, we develop a novel linear regression model based on the proposed skewed generalized t distribution for censored data. The likelihood-based inference and diagnostic analysis are established using the Expectation/Conditional Maximization Either algorithm in conjunction with smoothing approximate functions. We derive relevant measures to perform global influence for this novel model and develop local influence analysis based on the conditional expectation of the complete-data log-likelihood function. Some useful perturbation schemes are discussed. We illustrate the finite sample performance and the robustness of the proposed method by simulation studies. The proposed model is compared with other procedures based on a real dataset, and a sensitivity analysis is also conducted. [ABSTRACT FROM AUTHOR]

Researchers in statistical shape analysis often analyze outlines of objects. Even though these contours are infinite-dimensional in theory, they must be discretized in practice. When discretizing, it is important to reduce the number of sampling points considerably to reduce computational costs, but to not use too few points so as to result in too much approximation error. Unfortunately, determining the minimum number of points needed to achieve sufficiently approximate the contours is computationally expensive. In this paper, we fit regression models to predict these lower bounds using characteristics of the contours that are computationally cheap as predictor variables. However, least squares regression is inadequate for this task because it treats overestimation and underestimation equally, but underestimation of lower bounds is far more serious. Instead, to fit the models, we use the LINEX loss function, which allows us to penalize underestimation at an exponential rate while penalizing overestimation only linearly. We present a novel approach to select the shape parameter of the loss function and tools for analyzing how well the model fits the data. Through validation methods, we show that the LINEX models work well for reducing the underestimation for the lower bounds. [ABSTRACT FROM AUTHOR]

Discrete data are collected in many application areas and are often characterised by highly-skewed distributions. An example of this, which is considered in this paper, is the number of visits to a specialist, often taken as a measure of demand in healthcare. A discrete Weibull regression model was recently proposed for regression problems with a discrete response and it was shown to possess desirable properties. In this paper, we propose the first Bayesian implementation of this model. We consider a general parametrization, where both parameters of the discrete Weibull distribution can be conditioned on the predictors, and show theoretically how, under a uniform non-informative prior, the posterior distribution is proper with finite moments. In addition, we consider closely the case of Laplace priors for parameter shrinkage and variable selection. Parameter estimates and their credible intervals can be readily calculated from their full posterior distribution. A simulation study and the analysis of four real datasets of medical records show promises for the wide applicability of this approach to the analysis of count data. The method is implemented in the R package BDWreg. [ABSTRACT FROM AUTHOR]

A special source of difficulty in the statistical analysis is the possibility that some subjects may not have a complete observation of the response variable. Such incomplete observation of the response variable is called censoring. Censorship can occur for a variety of reasons, including limitations of measurement equipment, design of the experiment, and non-occurrence of the event of interest until the end of the study. In the presence of censoring, the dependence of the response variable on the explanatory variables can be explored through regression analysis. In this paper, we propose to examine the censorship problem in context of the class of asymmetric, i.e., we have proposed a linear regression model with censored responses based on skew scale mixtures of normal distributions. We develop a Monte Carlo EM (MCEM) algorithm to perform maximum likelihood inference of the parameters in the proposed linear censored regression models with skew scale mixtures of normal distributions. The MCEM algorithm has been discussed with an emphasis on the skew-normal, skew Student-t-normal, skew-slash and skew-contaminated normal distributions. To examine the performance of the proposed method, we present some simulation studies and analyze a real dataset. [ABSTRACT FROM AUTHOR]

From the prediction viewpoint, mode regression is more attractive since it pay attention to the most probable value of response variable given regressors. On the other hand, high-dimensional data are very prevalent as the advance of the technology of collecting and storing data. Variable selection is an important strategy to deal with high-dimensional regression problem. This paper aims to propose a variable selection procedure for high-dimensional mode regression via combining nonparametric kernel estimation method with sparsity penalty tactics. We also establish the asymptotic properties under certain technical conditions. The effectiveness and flexibility of the proposed methods are further illustrated by numerical studies and the real data application. [ABSTRACT FROM AUTHOR]

The standard regression model designed for real space is not suitable for compositional variables; it should be considered, whether the response and/or covariates are of compositional nature. There are usually three types of multiple regression model with compositional variables: Type 1 refers to the case where all the covariates are compositional data and the response is real; Type 2 is the opposite of Type 1; Type 3 relates to the model with compositional response and covariates. There have been some models for the three types. In this paper, we focus on Type 3 and propose multiple linear regression models including model in the simplex and model in isometric log-ratio (ilr) coordinates. The model in the simplex is based on matrix product, which can project a-part composition to another-part composition, and can deal with different number of parts of compositional variables. Some theorems are given to point out the relationship of parameters between the proposed models. Moreover, the inference for parameters in proposed models is also given. Real example is studied to verify the validity and usefulness of proposed models. [ABSTRACT FROM AUTHOR]

In this paper, we are employing the generalized linear model (GLM) in the form lij=χλ to decompose the symmetry model into the class of models discussed in Tomizawa (1992). In this formulation, the random component would be the observed counts fij with an underlying Poisson distribution. This approach utilizes the non-standard log-linear model and our focus in this paper therefore relates to models that are decompositions of the complete symmetry model. That is, models that are implied by the symmetry models. We develop factor and regression variables required for the implementation of these models in SAS PROC GENMOD and SPSS PROC GENLOG. We apply this methodology to analyse the three 4 × 4 contingency table, one of which is the Japanese Unaided distance vision data. Results obtained in this study are consistent with those from the numerous literature on the subject. We further extend our applications to the 6 × 6 Brazilian social mobility data. We found that both the quasi linear diagonal-parameters symmetry (QLDPS) and the quasi 2-ratios parameter symmetry (Q2RPS) models fit the Brazilian data very well. Parsimonious models being the QLDPS and the quasi-conditional symmetry (QCS) models. The SAS and SPSS programs for implementing the models discussed in this paper are presented in Appendices A, B and C. [ABSTRACT FROM AUTHOR]

The statistical modeling of big data bases constitutes one of the most challenging issues, especially nowadays. The issue is even more critical in case of a complicated correlation structure. Variable selection plays a vital role in statistical analysis of large data bases and many methods have been proposed so far to deal with the aforementioned problem. One of such methods is the Sure Independence Screening which has been introduced to reduce dimensionality to a relatively smaller scale. This method, though simple, produces remarkable results even under both ultra high dimensionality and big scale in terms of sample size problems. In this paper we dealt with the analysis of a big real medical data set assuming a Poisson regression model. We support the analysis by conducting simulated experiments taking into consideration the correlation structure of the design matrix. [ABSTRACT FROM AUTHOR]

In this paper, we consider statistical diagnostic for non-parametric regression models with right-censored data based on empirical likelihood. First, the primary model is transformed to the non-parametric regression model. Then, based on empirical likelihood methodology, we define some diagnostic statistics. At last, some simulation studies show that our proposed procedure can work fairly well. [ABSTRACT FROM PUBLISHER]

Beta regression models provide an adequate approach for modeling continuous outcomes limited to the interval (0, 1). This paper deals with an extension of beta regression models that allow for explanatory variables to be measured with error. The structural approach, in which the covariates measured with error are assumed to be random variables, is employed. Three estimation methods are presented, namely maximum likelihood, maximum pseudo-likelihood and regression calibration. Monte Carlo simulations are used to evaluate the performance of the proposed estimators and the naïve estimator. Also, a residual analysis for beta regression models with measurement errors is proposed. The results are illustrated in a real data set. [ABSTRACT FROM PUBLISHER]

Since the pioneering work by Koenker and Bassett [27], quantile regression models and its applications have become increasingly popular and important for research in many areas. In this paper, a random effects ordinal quantile regression model is proposed for analysis of longitudinal data with ordinal outcome of interest. An efficient Gibbs sampling algorithm was derived for fitting the model to the data based on a location-scale mixture representation of the skewed double-exponential distribution. The proposed approach is illustrated using simulated data and a real data example. This is the first work to discuss quantile regression for analysis of longitudinal data with ordinal outcome. [ABSTRACT FROM AUTHOR]

This paper describes a statistical method for estimating data envelopment analysis (DEA) score confidence intervals for individual organizations or other entities. This method applies statistical panel data analysis, which provides proven and powerful methodologies for diagnostic testing and for estimation of confidence intervals. DEA scores are tested for violations of the standard statistical assumptions including contemporaneous correlation, serial correlation, heteroskedasticity and the absence of a normal distribution. Generalized least squares statistical models are used to adjust for violations that are present and to estimate valid confidence intervals within which the true efficiency of each individual decision-making unit occurs. This method is illustrated with two sets of panel data, one from large US urban transit systems and the other from a group of US hospital pharmacies. [ABSTRACT FROM AUTHOR]

Measurement error (ME) is the difference between the true unknown value of a variable and the data assigned to that variable during the measuring process. The multiple correlation coefficient quantifies the strength of the relationship between the dependent and independent variable(s) in regression modeling. In this paper, we show that ME in the dependent variable results in a negative bias in the multiple correlation coefficient, making the relationship appear weaker than it should. The adjusted R2 provides regression modelers an unbiased estimate of the multiple correlation coefficient. However, due to the ME induced bias in the multiple correlation coefficient, the otherwise unbiased adjusted R2 under-estimates the variance explained by a regression model. This paper proposes two statistics for estimating the multiple correlation coefficient, both of which take into account the ME in the dependent variable. The first statistic uses all unbiased estimators, but may produce values outside the [0,1] interval. The second statistic requires modeling a single data set, created by including descriptive variables on the subjects used in a gage study. Based on sums of squares, the statistic has the properties of an R2: it measures the proportion of variance explained; has values restricted to the [0,1] interval; and the endpoints indicate no variance explained and all variance explained respectively. We demonstrate the methodology using data from a study of cervical spine range of motion in children. [ABSTRACT FROM AUTHOR]

This paper introduces an alternating conditional expectation (ACE) algorithm: a non-parametric approach for estimating the transformations that lead to the maximal multiple correlation of a response and a set of independent variables in regression and correlation analysis. These transformations can give the data analyst insight into the relationships between these variables so that this can be best described and non-linear relationships uncovered. Using the Bayesian information criterion (BIC), we show how to find the best closed-form approximations for the optimal ACE transformations. By means of ACE and BIC, the model fit can be considerably improved compared with the conventional linear model as demonstrated in the two simulated and two real datasets in this paper. [ABSTRACT FROM AUTHOR]

Time series regression models have been widely studied in the literature by several authors. However, statistical analysis of replicated time series regression models has received little attention. In this paper, we study the application of the quasi-least squares method to estimate the parameters in a replicated time series model with errors that follow an autoregressive process of order p. We also discuss two other established methods for estimating the parameters: maximum likelihood assuming normality and the Yule-Walker method. When the number of repeated measurements is bounded and the number of replications n goes to infinity, the regression and the autocorrelation parameters are consistent and asymptotically normal for all three methods of estimation. Basically, the three methods estimate the regression parameter efficiently and differ in how they estimate the autocorrelation. When p=2, for normal data we use simulations to show that the quasi-least squares estimate of the autocorrelation is undoubtedly better than the Yule-Walker estimate. And the former estimate is as good as the maximum likelihood estimate almost over the entire parameter space. [ABSTRACT FROM AUTHOR]

A relatively newer computational technique adopted by statisticians is known as independent component analysis (ICA) which is used to analyze complex multidimensional data with the objective to separate it into components that are independent to each other. Quite often the main interest for conducting ICA is to identify a small number of significant independent components (ICs) to replace the original complex dimensions with. For this, determining the order of identified ICs is a pre-requisite. The area is not unaddressed but it does deserve a careful revisiting. This is the subject matter of the paper which introduces a new method to order ICs. The proposed method is based upon regression approach. It compares the magnitude of the mixing coefficients and regression coefficients of the regression of the original series on ICs. Their compatibility determines the order. [ABSTRACT FROM PUBLISHER]

Recently, the ensemble learning approaches have been proven to be quite effective for variable selection in linear regression models. In general, a good variable selection ensemble should consist of a diverse collection of strong members. Based on the parallel genetic algorithm (PGA) proposed in [41], in this paper, we propose a novel method RandGA through injecting randomness into PGA with the aim to increase the diversity among ensemble members. Using a number of simulated data sets, we show that the newly proposed method RandGA compares favorably with other variable selection techniques. As a real example, the new method is applied to the diabetes data. [ABSTRACT FROM AUTHOR]

Mixture experiments are commonly encountered in many fields including chemical, pharmaceutical and consumer product industries. Due to their wide applications, mixture experiments, a special study of response surface methodology, have been given greater attention in both model building and determination of designs compared with other experimental studies. In this paper, some new approaches are suggested on model building and selection for the analysis of the data in mixture experiments by using a special generalized linear models, logistic regression model, proposed by Chen et al. [7]. Generally, the special mixture models, which do not have a constant term, are highly affected by collinearity in modeling the mixture experiments. For this reason, in order to alleviate the undesired effects of collinearity in the analysis of mixture experiments with logistic regression, a new mixture model is defined with an alternative ratio variable. The deviance analysis table is given for standard mixture polynomial models defined by transformations and special mixture models used as linear predictors. The effects of components on the response in the restricted experimental region are given by using an alternative representation of Cox's direction approach. In addition, odds ratio and the confidence intervals of odds ratio are identified according to the chosen reference and control groups. To compare the suggested models, some model selection criteria, graphical odds ratio and the confidence intervals of the odds ratio are used. The advantage of the suggested approaches is illustrated on tumor incidence data set. [ABSTRACT FROM AUTHOR]

Failure time models are considered when there is a subpopulation of individuals that is immune, or not susceptible, to an event of interest. Such models are of considerable interest in biostatistics. The most common approach is to postulate a proportion p of immunes or long-term survivors and to use a mixture model [5]. This paper introduces the defective inverse Gaussian model as a cure model and examines the use of the Gibbs sampler together with a data augmentation algorithm to study Bayesian inferences both for the cured fraction and the regression parameters. The results of the Bayesian and likelihood approaches are illustrated on two real data sets. [ABSTRACT FROM AUTHOR]

Automated public health surveillance of disease counts for rapid outbreak, epidemic or bioterrorism detection using conventional control chart methods can be hampered by over-dispersion and background ('in-control') mean counts that vary over time. An adaptive cumulative sum (CUSUM) plan is developed for signalling unusually high incidence in prospectively monitored time series of over-dispersed daily disease counts with a non-homogeneous mean. Negative binomial transitional regression is used to prospectively model background counts and provide 'one-step-ahead' forecasts of the next day's count. A CUSUM plan then accumulates departures of observed counts from an offset (reference value) that is dynamically updated using the modelled forecasts. The CUSUM signals whenever the accumulated departures exceed a threshold. The amount of memory of past observations retained by the CUSUM plan is determined by the offset value; a smaller offset retains more memory and is efficient at detecting smaller shifts. Our approach optimises early outbreak detection by dynamically adjusting the offset value. We demonstrate the practical application of the 'optimal' CUSUM plans to daily counts of laboratory-notified influenza and Ross River virus diagnoses, with particular emphasis on the steady-state situation (i.e. changes that occur after the CUSUM statistic has run through several in-control counts). [ABSTRACT FROM AUTHOR]

The commonly made assumption that all stochastic error terms in the linear regression model share the same variance (homoskedasticity) is oftentimes violated in practical applications, especially when they are based on cross-sectional data. As a precaution, a number of practitioners choose to base inference on the parameters that index the model on tests whose statistics employ asymptotically correct standard errors, i.e. standard errors that are asymptotically valid whether or not the errors are homoskedastic. In this paper, we use numerical integration methods to evaluate the finite-sample performance of tests based on different (alternative) heteroskedasticity-consistent standard errors. Emphasis is placed on a few recently proposed heteroskedasticity-consistent covariance matrix estimators. Overall, the results favor the HC4 and HC5 heteroskedasticity-robust standard errors. We also consider the use of restricted residuals when constructing asymptotically valid standard errors. Our results show that the only test that clearly benefits from such a strategy is the HC0 test. [ABSTRACT FROM AUTHOR]

Variable and model selection problems are fundamental to high-dimensional statistical modeling in diverse fields of sciences. Especially in health studies, many potential factors are usually introduced to determine an outcome variable. This paper deals with the problem of high-dimensional statistical modeling through the analysis of the trauma annual data in Greece for 2005. The data set is divided into the experiment and control sets and consists of 6334 observations and 112 factors that include demographic, transport and intrahospital data used to detect possible risk factors of death. In our study, different model selection techniques are applied to the experiment set and the notion of deviance is used on the control set to assess the fit of the overall selected model. The statistical methods employed in this work were the non-concave penalized likelihood methods, smoothly clipped absolute deviation, least absolute shrinkage and selection operator, and Hard, the generalized linear logistic regression, and the best subset variable selection.The way of identifying the significant variables in large medical data sets along with the performance and the pros and cons of the various statistical techniques used are discussed. The performed analysis reveals the distinct advantages of the non-concave penalized likelihood methods over the traditional model selection techniques. [ABSTRACT FROM AUTHOR]

In this paper we derive the predictive density function of a future observation when prior distribution for unknown mean of a normal population is a Type-II maximum likelihood ε-contaminated prior. The derived predictive distribution is applied to the problem of optimization of a regression nature in the decisive prediction framework. [ABSTRACT FROM AUTHOR]

The identification of influential observations has drawn a great deal of attention in regression diagnostics. Most of these identification techniques are based on single case deletion and among them DFFITS has become very popular with the statisticians. But this technique along with all other single case diagnostics may be ineffective in the presence of multiple influential observations. In this paper we develop a generalized version of DFFITS based on group deletion and then propose a new technique to identify multiple influential observations using this. The advantage of using the proposed method in the identification of multiple influential cases is then investigated through several well-referred data sets. [ABSTRACT FROM AUTHOR]

This paper is concerned with the use of regression methods to predict values of a response variable when that variable is naturally ordered. An application to the prediction of student examination performance is provided and it is argued that, although individual scores are unlikely to be well predicted at the extremes of the range using the conditional mean, conditional on covariates, it is possible to usefully predict where an individual is likely to feature in the rank order of performance. [ABSTRACT FROM AUTHOR]

Accurate theoretical modelling and simulation of thermal energy storage (TES) by means of phase change materials (PCM) is very complex and its results are not close enough to experimental values. This paper presents the empirical study of a thermal storage unit operating with a commercial PCM called RT25. The study is carried out by means of the statistical procedure, Design of Experiments. This methodology has rarely been used in the analysis of heat transfer problems. The present study has allowed us to investigate the phenomena involved and to design an actual system. We show the whole procedure followed in order to design the set-up, to run the experiments with a 2 3 factorial design, to compare its results with a numerical simulation and to get the empirical model by regression. Its results have been used to design actual installations aimed at free-cooling or maintaining the temperature constant in rooms where thermal security is necessary. [ABSTRACT FROM AUTHOR]

In multiple linear regression analysis, each observation affects the fitted regression equation differently and has varying influences on the regression coefficients of the different variables. Chatterjee & Hadi (1988) have proposed some measures such as DSSE ij (Impact on Residual Sum of Squares of simultaneously omitting the ith observation and the jth variable), F j (Partial F-test for the jth variable) and F j(i) (Partial F-test for the jth variable omitting the ith observation) to show the joint impact and the interrelationship that exists among a variable and an observation. In this paper we have proposed more extended form of those measures DSSE IJ , F J and F J(I) to deal with the interrelationships that exist among the multiple observations and a subset of variables by monitoring the effects of the simultaneous omission of multiple variables and multiple observations. [ABSTRACT FROM AUTHOR]

In this paper we present various diagnostic methods for a linear regression model under a logarithmic Birnbaum-Saunders distribution for the errors, which may be applied for accelerated life testing or to compare the median lives of several populations. Some influence methods, such as the local influence, total local influence of an individual and generalized leverage are derived, analysed and discussed. We also present a connection between the local influence and generalized leverage methods. A discussion of the computation of the likelihood displacement as well as the normal curvature in the local influence method are presented. Finally, an example with real data is given for illustration. [ABSTRACT FROM AUTHOR]

This paper proposes a regression model where the response is beta distributed using a parameterization of the beta law that is indexed by mean and dispersion parameters. The proposed model is useful for situation where the variable of interest is continuous and restricted to the interval (0,1) and is related to other variables through a regression structure. The regression parameters of the beta regression model are interpretable in terms of the mean of the response and, when the logit link is used of an odds ratio, unlike the parameters of linear regression that employs a transformed response. Estimation is performed by maximum likelihood. We provide closed-form expression for the score function, for Fisher's information matrix and its inverse. Hypothesis testing is performed using approximations obtained from the asymptotic normality of the maximum likelihood estimator. Some diagnostic measures are introduced. Finally, practical applications that employ real data are presented and discussed. [ABSTRACT FROM AUTHOR]

The validity conditions for univariate or multivariate analyses of repeated measures are highly sensitive to the usual assumptions. In cancer experiments, the data are frequently heteroscedastic and strongly correlated with time, and standard analyses do not perform well. Alternative non-parametric approaches can contribute to an analysis of these longitudinal data. This paper describes a method for such situations, using the results from a comparative experiment in which tumour volume is evaluated over time. First, we apply the non-parametric approach proposed by Raz in constructing a randomization Ftest for comparing treatments. A local polynomial fit is conducted to estimate the growth curves and confidence intervals for each treatment. Finally, this technique is used to estimate the velocity of tumour growth. [ABSTRACT FROM AUTHOR]

Prediction of demand for professional sports is increasingly drawing the attention of economists. We apply linear mixed models for modeling attendance figures at Spanish professional football. We investigate economic variables, such as the price of the tickets or the size of the market, and sporting variables, such as the quality of a team or the level of competition within the league, as potential predictors of attendance. It turns out that a model with temporally correlated random team effects provides good forecasts of attendance at a time horizon of two seasons. Results from this model agree with economic theory. [ABSTRACT FROM AUTHOR]

We develop a multivariate regression model when responses or predictors are on nonlinear manifolds, rather than on Euclidean spaces. The nonlinear constraint makes the problem challenging and needs to be studied carefully. By performing principal component analysis (PCA) on tangent space of manifold, we use principal directions instead in the model. Then, the ordinary regression tools can be utilized. We apply the framework to both shape data (ozone hole contours) and functional data (spectrums of absorbance of meat in Tecator dataset). Specifically, we adopt the square-root velocity function representation and parametrization-invariant metric. Experimental results have shown that we can not only perform powerful regression analysis on the non-Euclidean data but also achieve high prediction accuracy by the constructed model. [ABSTRACT FROM AUTHOR]

Jointness is a Bayesian approach to capturing dependence among regressors in multivariate data. It addresses the general issue of whether explanatory factors for a given empirical phenomenon are complements or substitutes. I ask a number of questions about existing jointness concepts: Are the patterns revealed stable across datasets? Are results robust to prior choice and do data characteristics affect results? And importantly: What do the answers imply from a practical vista? The present study takes an applied, interdisciplinary and comparative perspective, validating jointness concepts on datasets across scientific fields with focus on life sciences (Parkinson's disease) and sociology. Simulations complement the study of real-world data. My findings suggest that results depend on which jointness concept is used: Some concepts deliver jointness patterns remarkably uniform across datasets, while all concepts are fairly robust to the choice of prior structure. This can be interpreted as critique of jointness from a practical perspective, given that the patterns revealed are at times very different and no concept emerges as overall advantageous. The composite indicators approach to combining information across jointness concepts is also explored, suggesting an avenue to facilitate the application of the concepts in future research. [ABSTRACT FROM AUTHOR]

Real-time polymerase chain reaction (PCR) is reliable quantitative technique in gene expression studies. The statistical analysis of real-time PCR data is quite crucial for results analysis and explanation. The statistical procedures of analyzing real-time PCR data try to determine the slope of regression line and calculate the reaction efficiency. Applications of mathematical functions have been used to calculate the target gene relative to the reference gene(s). Moreover, these statistical techniques compare Ct (threshold cycle) numbers between control and treatments group. There are many different procedures in SAS for real-time PCR data evaluation. In this study, the efficiency of calibrated model and delta delta Ct model have been statistically tested and explained. Several methods were tested to compare control with treatment means of Ct. The methods tested included t-test (parametric test), Wilcoxon test (non-parametric test) and multiple regression. Results showed that applied methods led to similar results and no significant difference was observed between results of gene expression measurement by the relative method. [ABSTRACT FROM AUTHOR]

The Jackknife-after-bootstrap (JaB) technique originally developed by Efron [8] has been proposed as an approach to improve the detection of influential observations in linear regression models by Martin and Roberts [12] and Beyaztas and Alin [2]. The method is based on the use of percentile-method confidence intervals to provide improved cut-off values for several single case-deletion influence measures. In order to improve JaB, we propose using robust versions of Efron [7]’s bias-corrected and accelerated (BCa) bootstrap confidence intervals. In this study, the performances of robust BCa–JaB and conventional JaB methods are compared in the cases of DFFITS, Welsch's distance and modified Cook's distance influence diagnostics. Comparisons are based on both real data examples and through a simulation study. Our results reveal that under a variety of scenarios, our proposed method provides more accurate and reliable results, and it is more robust to masking effects. [ABSTRACT FROM PUBLISHER]

A computational understanding of partial and part determination coefficients brings additional insight concerning their interpretations in regression. It also enables one to easily identify synergistic combinations. Benefits from the practical interpretation of synergism have yet to be fully explored and exploited. Hence, this study provides a new dimension in the analysis of data. [ABSTRACT FROM AUTHOR]

A typical added variable plot is a commonly used plot in assessing the accuracy of a normal linear model. This plot is often used to evaluate the effect of adding an explanatory variable into the model and to detect possibly high leverage points or influential observations on the added variable. However, this type of plot is generally in doubt, once the normal distributional assumptions are violated. In this article, we extend the robust likelihood technique introduced by Royall and Tsou [11] to propose a robust added variable plot. The validity of this diagnostic plot requires no knowledge of the true underlying distributions so long as their second moments exist. The usefulness of the robust graphical approach is demonstrated through a few illustrations and simulations. [ABSTRACT FROM AUTHOR]

Non-randomized trials can give a biased impression of the effectiveness of any intervention. We consider trials in which incidence rates are compared in two areas over two periods. Typically, one area receives an intervention, whereas the other does not. We outline and illustrate a method to estimate the bias in such trials under two different bivariate models. The illustrations use data in which no particular intervention is operating. The purpose is to illustrate the size of the bias that could be observed purely due to regression towards the mean (RTM). The illustrations show that the bias can be appreciably different from zero, and even when centred on zero, the variance of the bias can be large. We conclude that the results of non-randomized trials should be treated with caution, as interventions which show small effects could be explained as artefacts of RTM. [ABSTRACT FROM AUTHOR]

This article presents a case study of a chemical compound acting as a subsystem of a delay mechanism for starting a rocket engine. The objective of this study was to investigate the proportions of mix components that enable a previously specified burning time. Thus, a linear regression model with normal responses was fitted, but later considered inadequate, as there was evidence that the response variance was not constant. Models fitted by the quasi-likelihood method were tried then. Through the developed model, it was possible to determine the proportion of each component to accomplish the process optimization. For the process optimization, besides considering a specific burning time, it was possible to consider the variance minimization for this time prediction as well. [ABSTRACT FROM AUTHOR]

We investigate the instability problem of the covariance structure of time series by combining the non-parametric approach based on the evolutionary spectral density theory of Priestley [Evolutionary spectra and non-stationary processes, J. R. Statist. Soc., 27 (1965), pp. 204-237; Wavelets and time-dependent spectral analysis, J. Time Ser. Anal., 17 (1996), pp. 85-103] and the parametric approach based on linear regression models of Bai and Perron [Estimating and testing linear models with multiple structural changes, Econometrica 66 (1998), pp. 47-78]. A Monte Carlo study is presented to evaluate the performance of some parametric testing and estimation procedures for models characterized by breaks in variance. We attempt to see whether these procedures perform in the same way as models characterized by mean-shifts as investigated by Bai and Perron [Multiple structural change models: a simulation analysis, in: Econometric Theory and Practice: Frontiers of Analysis and Applied Research, D. Corbea, S. Durlauf, and B.E. Hansen, eds., Cambridge University Press, 2006, pp. 212-237]. We also provide an analysis of financial data series, of which the stability of the covariance function is doubtful. [ABSTRACT FROM AUTHOR]

Kadilar and Cingi [Ratio estimators in simple random sampling, Appl. Math. Comput. 151 (3) (2004), pp. 893-902] introduced some ratio-type estimators of finite population mean under simple random sampling. Recently, Kadilar and Cingi [New ratio estimators using correlation coefficient, Interstat 4 (2006), pp. 1-11] have suggested another form of ratio-type estimators by modifying the estimator developed by Singh and Tailor [Use of known correlation coefficient in estimating the finite population mean, Stat. Transit. 6 (2003), pp. 655-560]. Kadilar and Cingi [Improvement in estimating the population mean in simple random sampling, Appl. Math. Lett. 19 (1) (2006), pp. 75-79] have suggested yet another class of ratio-type estimators by taking a weighted average of the two known classes of estimators referenced above. In this article, we propose an alternative form of ratio-type estimators which are better than the competing ratio, regression, and other ratio-type estimators considered here. The results are also supported by the analysis of three real data sets that were considered by Kadilar and Cingi. [ABSTRACT FROM AUTHOR]

We provide a method for simultaneous variable selection and outlier identification using the mean-shift outlier model. The procedure consists of two steps: the first step is to identify potential outliers, and the second step is to perform all possible subset regressions for the mean-shift outlier model containing the potential outliers identified in step 1. This procedure is helpful for model selection while simultaneously considering outlier identification, and can be used to identify multiple outliers. In addition, we can evaluate the impact on the regression model of simultaneous omission of variables and interesting observations. In an example, we provide detailed output from the R system, and compare the results with those using posterior model probabilities as proposed by Hoeting et al. [Comput. Stat. Data Anal. 22 (1996), pp. 252-270] for simultaneous variable selection and outlier identification. [ABSTRACT FROM AUTHOR]

Digits in statistical data produced by natural or social processes are often distributed in a manner described by 'Benford's law'. Recently, a test against this distribution was used to identify fraudulent accounting data. This test is based on the supposition that first, second, third, and other digits in real data follow the Benford distribution while the digits in fabricated data do not. Is it possible to apply Benford tests to detect fabricated or falsified scientific data as well as fraudulent financial data? We approached this question in two ways. First, we examined the use of the Benford distribution as a standard by checking the frequencies of the nine possible first and ten possible second digits in published statistical estimates. Second, we conducted experiments in which subjects were asked to fabricate statistical estimates (regression coefficients). The digits in these experimental data were scrutinized for possible deviations from the Benford distribution. There were two main findings. First, both digits of the published regression coefficients were approximately Benford distributed or at least followed a pattern of monotonic decline. Second, the experimental results yielded new insights into the strengths and weaknesses of Benford tests. Surprisingly, first digits of faked data also exhibited a pattern of monotonic decline, while second, third, and fourth digits were distributed less in accordance with Benford's law. At least in the case of regression coefficients, there were indications that checks for digit-preference anomalies should focus less on the first (i.e. leftmost) and more on later digits. [ABSTRACT FROM AUTHOR]

A prognostic index (PI) is usually derived from a regression model as a weighted mean of the covariates, with weights partial scores) proportional to the parameter estimates. When a PI is applied to patients other than those considered for its development, the issue of assessing its validity on the new case series is crucial. For this purpose, Van Houwelingen (200) proposed a method of validation by calibration, which limits overfitting by embedding the original model into a new one, so that only a few parameters will have to be estimated. Here we address the problem of PI radiation and revision with the above approach when the PI has classification purposes and it represents the linear predictor of a Weilbull model, derived from an accelerated failure time parameterization instead of a proportional hazards one, as originally described by Van Houwelingen. We show that the Van Houwelingen method can be applied in a straightforward manner, provided that the parameterization originally used in the PI model is appropriately taken into account. We also show that model validation and revision can be carried out by modifying th cut-off values used for prognostic grouping without affecting the partial scores of the original PI. This procedure can be applied to simplify the clinician's use of an established PI for classification purposed. [ABSTRACT FROM AUTHOR]

We propose methods for monitoring the residuals of a fitted ARIMA or an autoregressive fractionally integrated moving average (ARFIMA) model in order to detect changes of the parameters in that model. We extend the procedures of Box & Ramirez (1992) and Ramirez (1992) and allow the differencing parameter, d to be fractional or integer. Test statistics are approximated by Wiener processes. We carry out simulations and also apply our method to several real time series. The results show that our method is effective for monitoring all parameters in ARFIMA models. [ABSTRACT FROM AUTHOR]

We analyze tidal data from Port Mansfield, TX, using Kunsch's blockwise bootstrap in the regression setting. In particular, we estimate the variability of parameter estimates in a harmonic analysis via block subsampling of residuals from a least-squares fit. We see that naive least-squares variance estimates can be either too large or too small, depending on the strength of correlation and the design matrix. We argue that the block bootstrap is a simple, omnibus method of accounting for correlation in a regression model with correlated errors. [ABSTRACT FROM AUTHOR]

Compares and expounds the feed-forward neural networks method for regression and classification, a family of methods in multivariate statistics, and tree-induction methods.