Preliminary Testing For Normality: Is This A Good Practice?,
2013
University of Manitoba, Winnipeg, Manitoba
Preliminary Testing For Normality: Is This A Good Practice?, H. J. Keselman, Abdul R. Othman, Rand R. Wilcox
Journal of Modern Applied Statistical Methods
Normality is a distributional requirement of classical test statistics. In order for the test statistic to provide valid results leading to sound and reliable conclusions this requirement must be satisfied. In the not too distant past, it was claimed that violations of normality would not likely jeopardize scientific findings (See Hsu & Feldt, 1969; Lunney, 1970). Recent revelations suggest otherwise (See e.g., Micceri, 1989; Keselman, Huberty, Lix et al., 1998; Erceg-Hurn, Wilcox, & Keselman, 2013; Wilcox and Keselman, 2003; Wilcox, 2012a, b). Unfortunately the data obtained in psychological investigations rarely, if ever, meet the requirement of normally distributed data (Micceri, …
Front Matter,
2013
Wayne State University
Front Matter, Jmasm Editors
Journal of Modern Applied Statistical Methods
No abstract provided.
The Impact Of Continuity Violation On Anova And Alternative Methods,
2013
Chalmers University of Technology, Gothenburg, Sweden
The Impact Of Continuity Violation On Anova And Alternative Methods, Björn Lantz
Journal of Modern Applied Statistical Methods
The normality assumption behind ANOVA and other parametric methods implies that response variables are measured on continuous scales. A simulation approach is used to explore the impact of continuity violation on the performance of statistical methods commonly used by applied researchers to compare locations across several groups.
Variables Sampling Plan For Correlated Data,
2013
Vikram University, Ujjain, India
Variables Sampling Plan For Correlated Data, J. R. Singh, R. Sankle, M. Ahmad Khanday
Journal of Modern Applied Statistical Methods
The sampling plan for the mean for correlated data is studied. The Operating Characteristic (OC) of the variable sampling plan for mean for correlated data are calculated and compared with the OC of known σ case.
Intrinsically Ties Adjusted Non-Parametric Method For The Analysis Of Two Sampled Data,
2013
Nnamdi Azikiwe University, Awka, Nigeria
Intrinsically Ties Adjusted Non-Parametric Method For The Analysis Of Two Sampled Data, G. U. Ebuh, I. C. A Oyeka
Journal of Modern Applied Statistical Methods
A non-parametric method for the analysis of two sample data is proposed that intrinsically and structurally adjusts the test statistic for the possible presence of tied observations between the sampled populations, thereby obviating the need to require the populations to be continuous. The populations may be measurements on as low as the ordinal scale, and need not be homogeneous. In cases where the null hypotheses are rejected, the test statistic enables the determination of which of the sampled populations is likely to be responsible for the rejection (a determination which the Wilcoxon Mann Whitney test cannot handle). The proposed method …
Case-Control Studies With Jointly Misclassified Exposure And Confounding Variables,
2013
Western Illinois University, Macomb, IL
Case-Control Studies With Jointly Misclassified Exposure And Confounding Variables, Tze-San Lee
Journal of Modern Applied Statistical Methods
The issue of 2 × 2 × 2 case-control studies is addressed when both exposure and confounding variables are jointly misclassified. Two scenarios are considered: the classification errors of exposure and confounding variables are independent or not independent. The bias-adjusted cell probability estimates which account for the misclassification bias are presented. The effect of misclassification on the measure of crude odds ratio either unstratified or stratified by the confounder, Mantel-Haenszel summary odds ratio, the confounding component in the crude odds ratio, the first and second order multiplicative interaction are assessed through the sensitivity analysis from using the data on the …
How Good Is Best? Multivariate Case Of Ehrenberg-Weisberg Analysis Of Residual Errors In Competing Regressions,
2013
GfK Custom Research North America, Minneapolis, MN
How Good Is Best? Multivariate Case Of Ehrenberg-Weisberg Analysis Of Residual Errors In Competing Regressions, Stan Lipovetsky
Journal of Modern Applied Statistical Methods
A.S.C. Ehrenberg first noticed and S. Weisberg then formalized a property of pairwise regression to keep its quality almost at the same level of precision while the coefficients of the model could vary over a wide span of values. This paper generalizes the estimates of the percent change in the residual standard deviation to the case of competing multiple regressions. It shows that in contrast to the simple pairwise model, the coefficients of multiple regression can be changed over a wider range of the values including the opposite by signs coefficients. Consideration of these features facilitates better understanding the properties …
Constructing Confidence Intervals For Effect Sizes In Anova Designs,
2013
Indiana University, Bloomington, IN
Constructing Confidence Intervals For Effect Sizes In Anova Designs, Li-Ting Chen, Chao-Ying Joanne Peng
Journal of Modern Applied Statistical Methods
A confidence interval for effect sizes provides a range of plausible population effect sizes (ES) that are consistent with data. This article defines an ES as a standardized linear contrast of means. The noncentral method, Bonett’s method, and the bias-corrected and accelerated bootstrap method are illustrated for constructing the confidence interval for such an effect size. Results obtained from the three methods are discussed and interpretations of results are offered.
A Monte Carlo Comparison Of Robust Manova Test Statistics,
2013
Ball State University, Muncie, IN
A Monte Carlo Comparison Of Robust Manova Test Statistics, Holmes Finch, Brian French
Journal of Modern Applied Statistical Methods
Multivariate Analysis of Variance (MANOVA) is a popular statistical tool in the social sciences, allowing for the comparison of mean vectors across groups. MANOVA rests on three primary assumptions regarding the population: (a) multivariate normality, (b) equality of group population covariance matrices and (c) independence of errors. When these assumptions are violated, MANOVA does not perform well with respect to Type I error and power. There are several alternative test statistics that can be considered including robust statistics and the use of the structural equation modeling (SEM) framework. This simulation study focused on comparing the performance of the P test …
Test For Intraclass Correlation Coefficient Under Unequal Family Sizes,
2013
Columbus State University, Columbus, GA
Test For Intraclass Correlation Coefficient Under Unequal Family Sizes, Madhusudan Bhandary, Koji Fujiwara
Journal of Modern Applied Statistical Methods
Three tests are proposed based on F-distribution, Likelihood Ratio Test (LRT) and large sample Z-test for intraclass correlation coefficient under unequal family sizes based on a single multinormal sample. It has been found that the test based on F-distribution consistently and reliably produces results superior to those of Likelihood Ratio Test (LRT) and large sample Z-test in terms of size for various combinations of intraclass correlation coefficient values. The power of this test based on F-distribution is competitive with the power of the LRT and the power of Z-test is slightly better than the powers of F-test and LRT when …
Generalized Modified Ratio Estimator For Estimation Of Finite Population Mean,
2013
Pondicherry University, Puducherry, India
Generalized Modified Ratio Estimator For Estimation Of Finite Population Mean, Jambulingam Subramani
Journal of Modern Applied Statistical Methods
A generalized modified ratio estimator is proposed for estimating the population mean using the known population parameters. It is shown that the simple random sampling without replacement sample mean, the usual ratio estimator, the linear regression estimator and all the existing modified ratio estimators are the particular cases of the proposed estimator. The bias and the mean squared error of the proposed estimator are derived and are compared with that of existing estimators. The conditions for which the proposed estimator performs better than the existing estimators are also derived. The performance of the proposed estimator is assessed with that of …
Discriminating Between Generalized Exponential Distribution And Some Life Test Models Based On Population Quantiles,
2013
R. V. R. and J. C. College of Engineering, Guntur, India
Discriminating Between Generalized Exponential Distribution And Some Life Test Models Based On Population Quantiles, B. Srinivasa Rao, R. R. L Kantam
Journal of Modern Applied Statistical Methods
A test statistic based on population quantiles using sample order statistics is suggested. The quantiles of the test statistics are evaluated for generalized exponential distribution. Similar test statistic based on moments of sample order statistic is referred and the proposed test formula is compared with it. Between the pairs of the above models it is established that the test formula proposed by us is more effective and useful than the formula based on the moments of order statistics as developed by Sultan (2007).
Comparison Of Parameters Of Lognormal Distribution Based On The Classical And Posterior Estimates,
2013
University of Kashmir, Srinagar, India
Comparison Of Parameters Of Lognormal Distribution Based On The Classical And Posterior Estimates, Raja Sultan, S. P. Ahmad
Journal of Modern Applied Statistical Methods
Lognormal distribution is widely used in scientific field, such as agricultural, entomological, biology etc. If a variable can be thought as the multiplicative product of some positive independent random variables, then it could be modelled as lognormal. In this study, maximum likelihood estimates and posterior estimates of the parameters of lognormal distribution are obtained and using these estimates we calculate the point estimates of mean and variance for making comparisons.
On Bayesian Estimation And Predictions For Two-Component Mixture Of The Gompertz Distribution,
2013
Allama Iqbal Open University, Islamabad, Pakistan
On Bayesian Estimation And Predictions For Two-Component Mixture Of The Gompertz Distribution, Navid Feroze, Muhammad Aslam
Journal of Modern Applied Statistical Methods
Mixtures models have received sizeable attention from analysts in the recent years. Some work on Bayesian estimation of the parameters of mixture models have appeared. However, the were restricted to the Bayes point estimation The methodology for the Bayesian interval estimation of the parameters for said models is still to be explored. This paper proposes the posterior interval estimation (along with point estimation) for the parameters of a two-component mixture of the Gompertz distribution. The posterior predictive intervals are also derived and evaluated. Different informative and non-informative priors are assumed under a couple of loss functions for the posterior analysis. …
A Comparison Between Biased And Unbiased Estimators In Ordinary Least Squares Regression,
2013
King Khalid University, Saudi Arabia
A Comparison Between Biased And Unbiased Estimators In Ordinary Least Squares Regression, Ghadban Khalaf
Journal of Modern Applied Statistical Methods
During the past years, different kinds of estimators have been proposed as alternatives to the Ordinary Least Squares (OLS) estimator for the estimation of the regression coefficients in the presence of multicollinearity. In the general linear regression model, Y = Xβ + e, it is known that multicollinearity makes statistical inference difficult and may even seriously distort the inference. Ridge regression, as viewed here, defines a class of estimators of β indexed by a scalar parameter k. Two methods of specifying k are proposed and evaluated in terms of Mean Square Error (MSE) by …
Parameter Estimations Based On Kumaraswamy Progressive Type Ii Censored Data With Random Removals,
2013
Government Post Graduate College Muzaffarabad, Azad Kashmir, Pakistan
Parameter Estimations Based On Kumaraswamy Progressive Type Ii Censored Data With Random Removals, Navid Feroze, Ibrahim El-Batal
Journal of Modern Applied Statistical Methods
The estimation of two parameters of the Kumaraswamy distribution is considered under Type II progressive censoring with random removals, where the number of units removed at each failure time has a binomial distribution. The MLE was used to obtain the estimators of the unknown parameters, and the asymptotic variance - covariance matrix was also obtained. The formula to compute the expected test time was derived. A numerical study was carried out for different combinations of model parameters. Different censoring schemes were used for the estimation, and performance of these schemes was compared.
The Single-Case Data Analysis Package: Analysing Single-Case Experiments With R Software,
2013
KU Leuven, Belgium
The Single-Case Data Analysis Package: Analysing Single-Case Experiments With R Software, Isis Bulté, Patrick Onghena
Journal of Modern Applied Statistical Methods
The RcmdrPlugin.SCDA plug-in package is discussed. It integrates three R packages in the R commander interface: SCVA (for Single-Case Visual Analysis), SCRT (for Single-Case Randomization Tests), and SCMA (for Single-Case Meta-Analysis). This way the plug-in package covers three important steps in the analysis of single-case data.
Joint Estimation Of Multiple Graphical Models From High Dimensional Time Series,
2013
Johns Hopkins University
Joint Estimation Of Multiple Graphical Models From High Dimensional Time Series, Huitong Qiu, Fang Han, Han Liu, Brian Caffo
Johns Hopkins University, Dept. of Biostatistics Working Papers
In this manuscript the problem of jointly estimating multiple graphical models in high dimensions is considered. It is assumed that the data are collected from n subjects, each of which consists of m non-independent observations. The graphical models of subjects vary, but are assumed to change smoothly corresponding to a measure of the closeness between subjects. A kernel based method for jointly estimating all graphical models is proposed. Theoretically, under a double asymptotic framework, where both (m,n) and the dimension d can increase, the explicit rate of convergence in parameter estimation is provided, thus characterizing the strength one can borrow …
Combining Functions And The Closure Principle For Performing Follow-Up Tests In Functional Analysis Of Variance,
2013
University of Kentucky
Combining Functions And The Closure Principle For Performing Follow-Up Tests In Functional Analysis Of Variance, Olga A. Vsevolozhskaya, Mark C. Greenwood, G. J. Bellante, S. L. Powell, R. L. Lawrence, K. S. Repasky
Olga A. Vsevolozhskaya
Functional analysis of variance involves testing for differences in functional means across kk groups in nn functional responses. If a significant overall difference in the mean curves is detected, one may want to identify the location of these differences. Cox and Lee (2008) proposed performing a point-wise test and applying the Westfall–Young multiple comparison correction. We propose an alternative procedure for identifying regions of significant difference in the functional domain. Our procedure is based on a region-wise test and application of a combining function along with the closure multiplicity adjustment principle. We give an explicit formulation of how to implement …
Assessing Protein Conformational Sampling Methods Based On Bivariate Lag-Distributions Of Backbone Angles,
2013
Marquette University
Assessing Protein Conformational Sampling Methods Based On Bivariate Lag-Distributions Of Backbone Angles, Mehdi Maadooliat, Xin Gao, Jianhua Z. Huang
Mathematics, Statistics and Computer Science Faculty Research and Publications
Despite considerable progress in the past decades, protein structure prediction remains one of the major unsolved problems in computational biology. Angular-sampling-based methods have been extensively studied recently due to their ability to capture the continuous conformational space of protein structures. The literature has focused on using a variety of parametric models of the sequential dependencies between angle pairs along the protein chains. In this article, we present a thorough review of angular-sampling-based methods by assessing three main questions: What is the best distribution type to model the protein angles? What is a reasonable number of components in a mixture model …
