Physical Sciences and Mathematics Commons^™

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 …

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 …

An Alternative Approach To Reduce Dimensionality In Data Envelopment Analysis, Grace Lee Ching Yap, Wan Rosmanira Ismail, Zaidi Isa

Journal of Modern Applied Statistical Methods

Principal component analysis reduces dimensionality; however, uncorrelated components imply the existence of variables with weights of opposite signs. This complicates the application in data envelopment analysis. To overcome problems due to signs, a modification to the component axes is proposed and was verified using Monte Carlo simulations.

A Comparative Study Of Exact Versus Propensity Matching Techniques Using Monte Carlo Simulation, Mukaria J. J. Itang'ata

Dissertations

Often researchers face situations where comparative studies between two or more programs are necessary to make causal inferences for informed policy decision-making. Experimental designs employing randomization provide the strongest evidence for causal inferences. However, many pragmatic and ethical challenges may preclude the use of randomized designs. In such situations, subject matching provides an alternative design approach for conducting causal inference studies. This study examined various design conditions hypothesized to affect matching procedures’ bias recovery ability.

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Phase Transitions In Disordered Systems, Fawaz Y. Hrahsheh

Doctoral Dissertations

"Disorder can have a wide variety of consequences for the physics of phase transitions. Some transitions remain unchanged in the presence of disorder while others are completely destroyed. In this thesis we study the effects of disorder on several classical and quantum phase transitions in condensed matter systems. After a brief introduction, we study the ferromagnetic phase transition in a randomly layered Heisenberg magnet using large-scale Monte-Carlo simulations. Our results provide numerical evidence for the exotic infinite-randomness scenario. We study classical and quantum smeared phase transitions in substitutional alloys A₁₋ₓBₓ. Our results show that the disorder completely destroys the phase …