Statistics and Probability Commons^™

Choosing Smoothing Parameters For Exponential Smoothing: Minimizing Sums Of Squared Versus Sums Of Absolute Errors, Terry E. Dielman

Journal of Modern Applied Statistical Methods

When choosing smoothing parameters in exponential smoothing, the choice can be made by either minimizing the sum of squared one-step-ahead forecast errors or minimizing the sum of the absolute onestep- ahead forecast errors. In this article, the resulting forecast accuracy is used to compare these two options.

The Efficiency Of Ols In The Presence Of Auto-Correlated Disturbances In Regression Models, Samir Safi, Alexander White

Journal of Modern Applied Statistical Methods

The ordinary least squares (OLS) estimates in the regression model are efficient when the disturbances have mean zero, constant variance, and are uncorrelated. In problems concerning time series, it is often the case that the disturbances are correlated. Using computer simulations, the robustness of various estimators are considered, including estimated generalized least squares. It was found that if the disturbance structure is autoregressive and the dependent variable is nonstochastic and linear or quadratic, the OLS performs nearly as well as its competitors. For other forms of the dependent variable, rules of thumb are presented to guide practitioners in the choice …

Understanding Eurasian Convergence: Application Of Kohonen Self-Organizing Maps, Joel I. Deichmann, Abdolreza Eshghi, Dominique Haughton, Selin Sayek, Nicholas Teebagy, Heikki Topi

Journal of Modern Applied Statistical Methods

Kohonen self-organizing maps (SOMs) are employed to examine economic and social convergence of Eurasian countries based on a set of twenty-eight socio-economic measures. A core of European Union states is identified that provides a benchmark against which convergence of post-socialist transition economies may be judged. The Central European Visegrád countries and Baltics show the greatest economic convergence to Western Europe, while other states form clusters that lag behind. Initial conditions on the social dimension can either facilitate or constrain economic convergence, as discovered in Central Europe vis-à-vis the Central Asian Republics. Disquiet in the convergence literature is resolved by providing …

Analysis Of Type-Ii Progressively Hybrid Censored Competing Risks Data, Debasis Kundu, Avijit Joarder

Journal of Modern Applied Statistical Methods

A Type-II progressively hybrid censoring scheme for competing risks data is introduced, where the experiment terminates at a pre-specified time. The likelihood inference of the unknown parameters is derived under the assumptions that the lifetime distributions of the different causes are independent and exponentially distributed. The maximum likelihood estimators of the unknown parameters are obtained in exact forms. Asymptotic confidence intervals and two bootstrap confidence intervals are also proposed. Bayes estimates and credible intervals of the unknown parameters are obtained under the assumption of gamma priors on the unknown parameters. Different methods have been compared using Monte Carlo simulations. One …

Jmasm23: Cluster Analysis In Epidemiological Data (Matlab), Andrés M. Alonso

Journal of Modern Applied Statistical Methods

Matlab functions for testing the existence of time, space and time-space clusters of disease occurrences are presented. The classical scan test, the Ederer, Myers and Mantel’s test, the Ohno, Aoki and Aoki’s test, and the Knox’s test are considered.

Properties Of Bound Estimators On Treatment Effect Heterogeneity For Binary Outcomes, Edward J. Mascha, Jeffrey M. Albert

Journal of Modern Applied Statistical Methods

Variability in individual causal effects, treatment effect heterogeneity (TEH), is important to the interpretation of clinical trial results, regardless of the marginal treatment effect. Unfortunately, it is usually ignored. In the setting of two-arm randomized studies with binary outcomes, there are estimators for bounds on the probability of control success and treatment failure for an individual, or the treatment risk. Here, those bounds were refined and the sampling properties were assessed using simulations of correlated multinomial data via the Dirichlet multinomial. Results indicated low bias and mean squared error. Moderate to high intraclass correlation (ICC) and large numbers of clusters …

Two New Unbiased Point Estimates Of A Population Variance, Matthew E. Elam

Journal of Modern Applied Statistical Methods

Two new unbiased point estimates of an unknown population variance are introduced. They are compared to three known estimates using the mean-square error (MSE). A computer program, which is available for download at http://program.20m.com, is developed for performing calculations for the estimates.

Multiple Comparison Procedures, Trimmed Means And Transformed Statistics, Rhonda K. Kowalchuk, H. J. Keselman, Rand R. Wilcox, James Algina, James Algina, James Algina

Journal of Modern Applied Statistical Methods

A modification to testing pairwise comparisons that may provide better control of Type I errors in the presence of non-normality is to use a preliminary test for symmetry which determines whether data should be trimmed symmetrically or asymmetrically. Several pairwise MCPs were investigated, employing a test of symmetry with a number of heteroscedastic test statistics that used trimmed means and Winsorized variances. Results showed improved Type I error control than competing robust statistics.

Confidence Intervals On Subsets May Be Misleading, Juliet Popper Shaffer

Journal of Modern Applied Statistical Methods

No abstract provided.

The Effect On Type I Error And Power Of Various Methods Of Resolving Ties For Six Distribution-Free Tests Of Location, Bruce R. Fay

Journal of Modern Applied Statistical Methods

The impact on Type I error robustness and power for nine different methods of resolving ties was assessed for six distribution-free statistics with four empirical data sets using Monte Carlo techniques. These statistics share an underlying assumption of population continuity such that samples are assumed to have no equal data values (no zero difference–scores, no tied ranks). The best results across all tests and combinations of simulation parameters were obtained by randomly resolving ties, although there were exceptions. The method of dropping ties and reducing the sample size performed poorly.

Nonparametric Bayesian Multiple Comparisons For Dependence Parameter In Bivariate Exponential Populations, M. Masoom Ali, J. S. Cho, Munni Begum

Journal of Modern Applied Statistical Methods

A nonparametric Bayesian multiple comparisons problem (MCP) for dependence parameters in I bivariate exponential populations is studied. A simple method for pairwise comparisons of these parameters is also suggested. The methodology by Gopalan and Berry (1998) is extended using Dirichlet process priors, applied in the form of baseline prior and likelihood combination to provide the comparisons. Computation of the posterior probabilities of all possible hypotheses are carried out through a Markov Chain Monte Carlo, Gibbs sampling, due to the intractability of analytic evaluation. The process of MCP for the dependent parameters of bivariate exponential populations is illustrated with a numerical …

Entropy Criterion In Logistic Regression And Shapley Value Of Predictors, Stan Lipovetsky

Journal of Modern Applied Statistical Methods

Entropy criterion is used for constructing a binary response regression model with a logistic link. This approach yields a logistic model with coefficients proportional to the coefficients of linear regression. Based on this property, the Shapley value estimation of predictors’ contribution is applied for obtaining robust coefficients of the linear aggregate adjusted to the logistic model. This procedure produces a logistic regression with interpretable coefficients robust to multicollinearity. Numerical results demonstrate theoretical and practical advantages of the entropy-logistic regression.

Comparison Of Some Simple Estimators Of The Lognormal Parameters Based On Censored Samples, Baklizi Ayman, Mohammed Al-Haj Ebrahem

Journal of Modern Applied Statistical Methods

Point estimation of the parameters of the lognormal distribution with censored data is considered. The often employed maximum likelihood estimator does not exist in closed form and iterative methods that require very good starting points are needed. In this article, some techniques of finding closed form estimators to this situation are presented and extended. An extensive simulation study is carried out to investigate and compare the performance of these techniques. The results show that some of them are highly efficient as compared with the maximum likelihood estimator.

Statistical Pronouncements V, Jmasm Editors

Journal of Modern Applied Statistical Methods

No abstract provided.

Properties Of The Gar(1) Model For Time Series Of Counts, Vasiliki Karioti, Chrys Caroni

Journal of Modern Applied Statistical Methods

Models for time series count data include several proposed by Zeger and Qaqish (1988), subsequently generalized into the GARMA family. The GAR(1) model is examined in detail. The maximum likelihood estimation of the parameters will be discussed and the properties of Pearson and randomized residuals will be examined.

Variance Estimation And Construction Of Confidence Intervals For Gee Estimator, Shenghai Zhang, Mary E. Thompson

Journal of Modern Applied Statistical Methods

The sandwich estimator, also known as the robust covariance matrix estimator, has achieved increasing use in the statistical literature as well as with the growing popularity of generalized estimating equations (GEE). A modified sandwich variance estimator is proposed, and its consistency and efficiency are studied. It is compared with other variance estimators, such as a model based estimator, the sandwich estimator and a corrected sandwich estimator. Confidence intervals for regression parameters based on these estimators are discussed. Simulation studies using clustered data to compare the performance of variance estimators are reported.

Jmasm22: A Convenient Way Of Generating Normal Random Variables Using Generalized Exponential Distribution, Debasis Kundu, Anubhav Manglick

Journal of Modern Applied Statistical Methods

A convenient method to generate normal random variable using a generalized exponential distribution is proposed. The new method is compared with the other existing methods and it is observed that the proposed method is quite competitive with most of the existing methods in terms of the K − S distances and the corresponding p-values.

A Combined Individuals And Moving Range Control Chart, Michael B. C. Khoo, S. H. Quah, C. K. Ch'ng

Journal of Modern Applied Statistical Methods

An individuals control chart is usually used to monitor shifts in the process mean when it is not possible to form subgroups. The moving range of two successive process measures is used as the basis for estimating the process variability. Similar to the case of the X − R and X − S charts, the individualsmoving range (I-MR) charts are used simultaneously in the monitoring of the process mean and variance respectively for individual observations, requiring maintaining two different charts. In this article, a new approach is suggested where the measurements of both the process mean and variance are plotted …

A Combined Standard Deviation Based Data Clustering Algorithm, Kuttiannan Thangavel, Durairaj Ashok Kumar

Journal of Modern Applied Statistical Methods

The clustering problem has been widely studied because it arises in many knowledge management oriented applications. It aims at identifying the distribution of patterns and intrinsic correlations in data sets by partitioning the data points into similarity clusters. Traditional clustering algorithms use distance functions to measure similarity centroid, which subside the influences of data points. Hence, in this article a novel non-distance based clustering algorithm is proposed which uses Combined Standard Deviation (CSD) as measure of similarity. The performance of CSD based K-means approach, called K-CSD clustering algorithm, is tested on synthetic data sets. It compared favorably to widely used …

The Use Of Hierarchical Ancova In Curriculum Studies, Show-Mann Liou, Chao-Ying Joanne Peng

Journal of Modern Applied Statistical Methods

Many educational studies are carried out in intact settings, such as classrooms or groups in which individual data were collected before and after a treatment. Researchers advocate either the use of individual scores as the unit of analysis or class means. Both approaches suffer from conceptual and methodological limitations. In this article, the use of hierarchical ANCOVA for analyzing quasiexperimental data including baseline measures is designed and promoted. It is illustrated with a realworld data set collected from a curriculum study. Results showed that the hierarchical ANCOVA is a conceptually and methodologically sound approach, and is better than ANCOVA based …

Profile Likelihood Estimation Of Partially Linear Panel Data Models With Fixed Effects, Liangjun Su, Aman Ullah

Research Collection School Of Economics

We consider consistent estimation of partially linear panel data models with fixed effects. We propose profile-likelihood-based estimators for both the parametric and nonparametric components in the models and establish convergence rates and asymptotic normality for both estimators.

Modeling And Simulation Of Value -At -Risk In The Financial Market Area, Xiangyin Zheng

Doctoral Dissertations

Value-at-Risk (VaR) is a statistical approach to measure market risk. It is widely used by banks, securities firms, commodity and energy merchants, and other trading organizations. The main focus of this research is measuring and analyzing market risk by modeling and simulation of Value-at-Risk for portfolios in the financial market area. The objectives are (1) predicting possible future loss for a financial portfolio from VaR measurement, and (2) identifying how the distributions of the risk factors affect the distribution of the portfolio. Results from (1) and (2) provide valuable information for portfolio optimization and risk management.

The model systems chosen …

The Longitudinal Effect Of Self-Monitoring And Locus Of Control On Social Network Position In Friendship Networks, Gary J. Moore

Theses and Dissertations

The purpose of this research was to identify how enduring personality characteristics predict a person's location in a network, locations which in turn affect outcomes such as performance. Specifically, this thesis examines how self-monitoring and locus of control influence an individual's location in a friendship social network over time. Hierarchical Linear Modeling (HLM) was used to analyze 28 groups of students and instructors at a military training course over six and one half weeks. Self-monitoring predicted betweenness centrality in five of six time periods while locus of control predicted betweenness centrality in three of six time periods. The moderation of …

Foreign-Born Population In Selected Ohio Cities, 1870 To 2000 A Brief Descriptive Report, Mark Salling, Ellen Cyran

All Maxine Goodman Levin School of Urban Affairs Publications

No abstract provided.

Using The Census Bureau's Public Use Microdata For Migration Analysis, Mark Salling, Ellen Cyran

All Maxine Goodman Levin School of Urban Affairs Publications

Using the Census Bureau's Public Use Microdata for Migration Analysis, Proceedings of the annual conference of the Urban and Regional Information Systems Association, Vancouver, BC, Canada, September 2006, pp.336-348.

A Class Of Nonlinear Stochastic Volatility Models, Jun Yu, Zhenlin Yang

Research Collection School Of Economics

This paper proposes a class of nonlinear stochastic volatility models based on the Box-Cox transformation which offers an alternative to the one introduced in Andersen (1994). The proposed class encompasses many parametric stochastic volatility models that have appeared in the literature, including the well known lognormal stochastic volatility model, and has an advantage in the ease with which different specifications on stochastic volatility can be tested. In addition, the functional form of transformation which induces marginal normality of volatility is obtained as a byproduct of this general way of modeling stochastic volatility. The efficient method of moments approach is used …

Foreign Migration To The Cleveland-Akron-Lorain Metropolitan Area From 1995 To 2000, Mark Salling, Ellen Cyran

All Maxine Goodman Levin School of Urban Affairs Publications

This report is one of a series on migration to and from the region using the five percent Public Use Microdata Sample (PUMS) of the 2000 Census of Population and Housing and provides a description of foreign migrants moving to the Cleveland-Akron-Lorain (CAL) Consolidated Metropolitan Area (CMSA) from 1995 to 2000.* The report identifies the countries of origin of migrants and compares the demographic, socioeconomic, and housing characteristics of the foreign migrants to the CAL with other groups, including foreign migrants to Ohio and the nation, and, at times, to domestic migrants to and from the CAL.

Obesity, Self-Complexity, And Compartmentalization: On The Implications Of Obesity For Self-Concept Organization, Bruce E. Blaine, C. E. Johnson

Statistics Faculty/Staff Publications

The relationship between obesity and structural aspects of the self-concept was examined in adult women. Participants were 119 adult women [age range: 18-73, M=26.9; body mass index (BMI) range: 16.2-54.7, M=27.3] who completed measures of self-esteem, self-complexity, and the spontaneous self-concept. BMI was associated with less complex and more compartmentalized self-knowledge and more frequent mention of weight-stereotypic traits as self-descriptive. The findings are discussed in the context of research on obesity- related stigma.

Biomass Estimation Using Statistical And Neural Network Analysis Of Aster Data, Vijay O. Lulla

All-Inclusive List of Electronic Theses and Dissertations

This study assessed the performance of different biomass estimation methods using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) lll data in a temperate forest. Multiple linear regression statistics of spectral band data and derived indices with biomass values were compared with advanced neural network models created with spectral band data and indices to model biomass values. Biomass for the study area was estimated using both of these methods and the results were discussed. The data were analyzed using multivariate statistical analysis. Correlation analysis and regression analysis were employed to understand the relationship of biomass to the spectral data and …

Autism And Parental Marital Satisfaction: The Role Of Adequacy Of Resources, Geneeta Kaliah Chambers

Loma Linda University Electronic Theses, Dissertations & Projects

The goal of the present study was to expand on the existing literature exploring families with children who have developmental disabilities, particularly autism. Previous studies have been constrained by univariate approaches that have failed to adequately capture the nuances of family functioning. Using an ecological/context approach, stemming from an ongoing research program conducted within a university-based treatment center, the present study attempted to improve on the conceptualization of interrelationships among family members and the role that contextual factors play within that dynamic. Specifically, the present study explored the influence of children’s level of autism on parents’ reports of their marital …