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Methods For The Analysis Of Developmental Respiration Patterns., Justin Tyler Peyton May 2008

Methods For The Analysis Of Developmental Respiration Patterns., Justin Tyler Peyton

Electronic Theses and Dissertations

This thesis looks at the problem of developmental respiration in Sarcophaga crassipalpis Macquart from the biological and instrumental points of view and adapts mathematical and statistical tools in order to analyze the data gathered. The biological motivation and current state of research is given as well as instrumental considerations and problems in the measurement of carbon dioxide production. A wide set of mathematical and statistical tools are used to analyze the time series produced in the laboratory. The objective is to assemble a methodology for the production and analysis of data that can be used in further developmental respiration research.

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Confidence Intervals Based On Robust Estimators, Meral Cetin, Serpil Aktas May 2008

Confidence Intervals Based On Robust Estimators, Meral Cetin, Serpil Aktas

Journal of Modern Applied Statistical Methods

Classical estimation of confidence intervals based on the sample mean and variance is sensitive to outliers. Robust methods were proposed for reducing the influence of outliers. The Minimum Volume Ellipsoid estimator (MVE), having a high breakdown point, is one of the robust estimators for location and scale parameters. The robust confidence interval for location parameter is constructed based on the MVE, and compared with the proposed robust confidence interval estimation methods. The performance of the robust confidence interval based on MVE is illustrated with a simulation study. The lengths of 100(1-α)% confidence intervals were investigated.

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Using Connectionist Models To Evaluate Examinees’ Response Patterns To Achievement Tests, Mark J. Gierl, Ying Cui, Steve Hunka May 2008

Using Connectionist Models To Evaluate Examinees’ Response Patterns To Achievement Tests, Mark J. Gierl, Ying Cui, Steve Hunka

Journal of Modern Applied Statistical Methods

The attribute hierarchy method (AHM) applied to assessment engineering is described. It is a psychometric method for classifying examinees’ test item responses into a set of attribute mastery patterns associated with different components in a cognitive model of task performance. Attribute probabilities, computed using a neural network, can be estimated for each examinee thereby providing specific information about the examinee’s attribute-mastery level. The pattern recognition approach described in this study relies on an explicit cognitive model to produce the expected response patterns. The expected response patterns serve as the input to the neural network. The model also yields the cognitive …

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Coverage Performance Of The Non-Central F-Based And Percentile Bootstrap Confidence Intervals For Root Mean Square Standardized Effect Size In One-Way Fixed-Effects Anova, Guili Zhang, James Algina May 2008

Coverage Performance Of The Non-Central F-Based And Percentile Bootstrap Confidence Intervals For Root Mean Square Standardized Effect Size In One-Way Fixed-Effects Anova, Guili Zhang, James Algina

Journal of Modern Applied Statistical Methods

The coverage performance of the confidence intervals (CIs) for the Root Mean Square Standardized Effect Size (RMSSE) was investigated in a balanced, one-way, fixed-effects, between-subjects ANOVA design. The noncentral F distribution-based and the percentile bootstrap CI construction methods were compared. The results indicated that the coverage probabilities of the CIs for RMSSE were not adequate.

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An Evaluation Of Standard, Alternative, And Robust Slope Test Strategies, Tim Moses, Alan Klockars May 2008

An Evaluation Of Standard, Alternative, And Robust Slope Test Strategies, Tim Moses, Alan Klockars

Journal of Modern Applied Statistical Methods

The robustness and power of nine strategies for testing the differences between two groups’ regression slopes under nonnormality and residual variance heterogeneity are compared. The results showed that three most robust slope test strategies were the combination of the trimmed and Winsorized slopes with the James second order test, the combination of Theil-Sen with James, and Theil-Sen with percentile bootstrapping. The slope tests based on Theil-Sen slopes were more powerful than those based on trimmed and Winsorized slopes.

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Second-Order Latent Growth Models With Shifting Indicators, Gregory R. Hancock, Michelle M. Buehl May 2008

Second-Order Latent Growth Models With Shifting Indicators, Gregory R. Hancock, Michelle M. Buehl

Journal of Modern Applied Statistical Methods

Second-order latent growth models assess longitudinal change in a latent construct, typically employing identical manifest variables as indicators across time. However, the same indicators may be unavailable and/or inappropriate for all time points. This article details methods for second-order growth models in which constructs’ indicators shift over time.

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Selection Of Non-Regular Fractional Factorial Designs When Some Two-Factor Interactions Are Important, Weiming Ke, Rui Yao May 2008

Selection Of Non-Regular Fractional Factorial Designs When Some Two-Factor Interactions Are Important, Weiming Ke, Rui Yao

Journal of Modern Applied Statistical Methods

A new method is proposed for selecting the optimal non-regular fractional factorial designs in the situation when some two-factor interactions are potentially important. Searching for the best designs according to this method is discussed and some results for the Plackett-Burman design of 12 runs are presented.

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A Weighted Moving Average Process For Forecasting, Shou Hsing Shih, Chris P. Tsokos May 2008

A Weighted Moving Average Process For Forecasting, Shou Hsing Shih, Chris P. Tsokos

Journal of Modern Applied Statistical Methods

The object of the present study is to propose a forecasting model for a nonstationary stochastic realization. The subject model is based on modifying a given time series into a new k-time moving average time series to begin the development of the model. The study is based on the autoregressive integrated moving average process along with its analytical constrains. The analytical procedure of the proposed model is given. A stock XYZ selected from the Fortune 500 list of companies and its daily closing price constitute the time series. Both the classical and proposed forecasting models were developed and a comparison …

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Comparing Different Methods For Multiple Testing In Reaction Time Data, Massimiliano Pastore, Massimo Nucci, Giovanni Galfano May 2008

Comparing Different Methods For Multiple Testing In Reaction Time Data, Massimiliano Pastore, Massimo Nucci, Giovanni Galfano

Journal of Modern Applied Statistical Methods

Reaction times were simulated for examining the power of six methods for multiple testing, as a function of sample size and departures from normality. Power estimates were low for all methods for non-normal distributions. With normal distributions, even for small sample sizes, satisfactory power estimates were observed, especially for FDR-based procedures.

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Two-Stage Short-Run (X, Mr) Control Charts, Matthew E. Elam, Kenneth E. Case May 2008

Two-Stage Short-Run (X, Mr) Control Charts, Matthew E. Elam, Kenneth E. Case

Journal of Modern Applied Statistical Methods

This article is the first in a series of two articles that applies two-stage short-run control charting to (X, MR) charts. Theory is developed and then used to derive the control chart factor equations. In the sequel, the control chart factor calculations are computerized and an example is presented.

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Systematic Reviews Of Animal Experiments Demonstrate Poor Contributions To Human Healthcare, Andrew Knight May 2008

Systematic Reviews Of Animal Experiments Demonstrate Poor Contributions To Human Healthcare, Andrew Knight

Experimentation Collection

Widespread reliance on animal models during preclinical research and toxicity testing assumes their reasonable predictivity for human outcomes. However, of 20 published systematic reviews examining human clinical utility located during a comprehensive literature search, animal models demonstrated significant potential to contribute toward clinical interventions in only two cases, one of which was contentious. Included were experiments expected by ethics committees to lead to medical advances, highly-cited experiments published in major journals, and chimpanzee experiments—the species most generally predictive of human outcomes. Seven additional reviews failed to demonstrate utility in reliably predicting human toxicological outcomes such as carcinogenicity and teratogenicity. Results …

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Methods Of Assessing And Ranking Probable Sources Of Error, Nataniel Greene May 2008

Methods Of Assessing And Ranking Probable Sources Of Error, Nataniel Greene

Publications and Research

A classical method for ranking n potential events as sources of error is Bayes' theorem. However, a ranking based on Bayes' theorem lacks a fundamental symmetry: the ranking in terms of blame for error will not be the reverse of the ranking in terms of credit for lack of error. While this is not a flaw in Bayes' theorem, it does lead one to inquire whether there are related methods which have such symmetry. Related methods explored here include the logical version of Bayes' theorem based on probabilities of conditionals, probabilities of biconditionals, and ratios or differences of credit to …

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A Monte Carlo Power Analysis Of Traditional Repeated Measures And Hierarchical Multivariate Linear Models In Longitudinal Data Analysis, Hua Fang, Gordon P. Brooks, Maria L. Rizzo, Kimberly A. Espy, Robert S. Barcikowski May 2008

A Monte Carlo Power Analysis Of Traditional Repeated Measures And Hierarchical Multivariate Linear Models In Longitudinal Data Analysis, Hua Fang, Gordon P. Brooks, Maria L. Rizzo, Kimberly A. Espy, Robert S. Barcikowski

Developmental Cognitive Neuroscience Laboratory: Faculty Publications

The power properties of traditional repeated measures and hierarchical linear models have not been clearly determined in the balanced design for longitudinal studies in the current literature. A Monte Carlo power analysis of traditional repeated measures and hierarchical multivariate linear models are presented under three variance-covariance structures. Results suggest that traditional repeated measures have higher power than hierarchical linear models for main effects, but lower power for interaction effects. Significant power differences are also exhibited when power is compared across different covariance structures. Results also supplement more comprehensive empirical indexes for estimating model precision via bootstrap estimates and the approximate …

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Connectivity In Frame Matroids, Dan Slilaty, Hongxun Qin May 2008

Connectivity In Frame Matroids, Dan Slilaty, Hongxun Qin

Mathematics and Statistics Faculty Publications

We discuss the relationship between the vertical connectivity of a biased graph Ω and the Tutte connectivity of the frame matroid of Ω (also known as the bias matroid of Ω).

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Small Sample Methods For The Analysis Of Clustered Binary Data, Lawrence J. Cook May 2008

Small Sample Methods For The Analysis Of Clustered Binary Data, Lawrence J. Cook

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

There are several solutions for analysis of clustered binary data. However, the two most common tools in use today, generalized estimating equations and random effects or mixed models, rely heavily on asymptotic theory. However, in many situations, such as small or sparse samples, asymptotic assumptions may not be met. For this reason we explore the utility of the quadratic exponential model and conditional analysis to estimate the effect size of a trend parameter in small sample and sparse data settings. Further we explore the computational efficiency of two methods for conducting conditional analysis, the network algorithm and Markov chain Monte …

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The Signed-Graphic Representations Of Wheels And Whirls, Dan Slilaty, Hongxun Qin May 2008

The Signed-Graphic Representations Of Wheels And Whirls, Dan Slilaty, Hongxun Qin

Mathematics and Statistics Faculty Publications

We characterize all of the ways to represent the wheel matroids and whirl matroids using frame matroids of signed graphs. The characterization of wheels is in terms of topological duality in the projective plane and the characterization of whirls is in terms of topological duality in the annulus.

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Confidence Intervals For The Squared Multiple Semipartial Correlation Coefficient, James Algina, H. J. Keselman, Randall D. Penfield May 2008

Confidence Intervals For The Squared Multiple Semipartial Correlation Coefficient, James Algina, H. J. Keselman, Randall D. Penfield

Journal of Modern Applied Statistical Methods

The squared multiple semipartial correlation coefficient is the increase in the squared multiple correlation coefficient that occurs when two or more predictors are added to a multiple regression model. Coverage probability was investigated for two variations of each of three methods for setting confidence intervals for the population squared multiple semipartial correlation coefficient. Results indicated that the procedure that provides coverage probability in the [.925, .975] interval for a 95% confidence interval depends primarily on the number of added predictors. Guidelines for selecting a procedure are presented.

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On A Test Of Independence Via Quantiles That Is Sensitive To Curvature, Rand R. Wilcox May 2008

On A Test Of Independence Via Quantiles That Is Sensitive To Curvature, Rand R. Wilcox

Journal of Modern Applied Statistical Methods

Let (Yi ,Xi ) , i =1,..., n , be a random sample from some p+1 variate distribution where Xi is a vector having length p. Many methods for testing the hypothesis that Y is independent of X are relatively insensitive to a broad class of departures from independence. Power improvements focus on the median of Y or some other quantile and test the hypothesis that the regression surface is a horizontal plane versus some unknown form. A wild bootstrap method (Stute et al. 1998) can be used based on quantiles, but with small or moderate sample …

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A Monte Carlo Power Analysis Of Traditional Repeated Measures And Hierarchical Multivariate Linear Models In Longitudinal Data Analysis, Hua Fang, Gordon P. Brooks, Maria L. Rizzo, Kimberly A. Espy, Robert S. Barcikowski May 2008

A Monte Carlo Power Analysis Of Traditional Repeated Measures And Hierarchical Multivariate Linear Models In Longitudinal Data Analysis, Hua Fang, Gordon P. Brooks, Maria L. Rizzo, Kimberly A. Espy, Robert S. Barcikowski

Journal of Modern Applied Statistical Methods

The power properties of traditional repeated measures and hierarchical linear models have not been clearly determined in the balanced design for longitudinal studies in the current literature. A Monte Carlo power analysis of traditional repeated measures and hierarchical multivariate linear models are presented under three variance-covariance structures. Results suggest that traditional repeated measures have higher power than hierarchical linear models for main effects, but lower power for interaction effects. Significant power differences are also exhibited when power is compared across different covariance structures. Results also supplement more comprehensive empirical indexes for estimating model precision via bootstrap estimates and the approximate …

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Estimating How Many Observations Are Needed To Obtain A Required Level Of Reliability, David A. Walker May 2008

Estimating How Many Observations Are Needed To Obtain A Required Level Of Reliability, David A. Walker

Journal of Modern Applied Statistical Methods

This article provides a detailed table containing estimations of how many observations are needed to obtain an increased reliability coefficient for situations such as observational data collection in the classroom. A SPSS program is provided for users to analyze situations where an initial reliability value is obtained and the user wants to determine how many more observations are needed to reach a required level of reliability.

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Tests For Independence In Two-Way Contingency Tables With Small Samples, Stephen Sharp May 2008

Tests For Independence In Two-Way Contingency Tables With Small Samples, Stephen Sharp

Journal of Modern Applied Statistical Methods

When testing the null hypothesis of independence in a two-way contingency table, the likelihood ratio test statistic is approximately distributed as Chi-squared d for large sample sizes (N) but may not be for small samples. This paper presents expressions which match the mean of the statistic to Chi-squared d as far as N−1 and N−2, derives a method of estimating the expressions from observed data and evaluates them using Monte Carlo simulations. It is concluded that using appropriate dividing factors, rejection rates after matching are more accurate than for either the unadjusted likelihood ratio statistic …

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Utility Of Weights For Weighted Kappa As A Measure Of Interrater Agreement On Ordinal Scale, Moonseong Heo May 2008

Utility Of Weights For Weighted Kappa As A Measure Of Interrater Agreement On Ordinal Scale, Moonseong Heo

Journal of Modern Applied Statistical Methods

Kappa statistics, unweighted or weighted, are widely used for assessing interrater agreement. The weights of the weighted kappa statistics in particular are defined in terms of absolute and squared distances in ratings between raters. It is proposed that those weights can be used for assessment of interrater agreements. A closed form expectations and variances of the agreement statistics referred to as AI1 and AI2, functions of absolute and squared distances in ratings between two raters, respectively, are obtained. AI1 and AI2 are compared with the weighted and unweighted kappa statistics in …

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Robustness Of Some Estimators Of Linear Model With Autocorrelated Error Terms When Stochastic Regressors Are Normally Distributed, Kayode Ayinde, J. O. Olaomi May 2008

Robustness Of Some Estimators Of Linear Model With Autocorrelated Error Terms When Stochastic Regressors Are Normally Distributed, Kayode Ayinde, J. O. Olaomi

Journal of Modern Applied Statistical Methods

Performances of estimators of the linear model under different level of autocorrelation (ρ) are known to be affected by different specifications of regressors. The robustness of some methods of parameter estimation of linear model to autocorrelation are examined when stochastic regressors are normally distributed. Monte Carlo experiments were conducted at both low and high replications. Comparison and preference of estimator(s) are based on their performances via bias, absolute bias, variance and more importantly the mean squared error of the estimated parameters of the model. Results show that the performances of the estimators improve with increased replication. In estimating …

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Jacques Salomon Hadamard And The Use Of Symbols In Teaching Differential Calculus, Daniel S. Drucker, Claude Schochet, John Cuzzocrea, Shlomo Sawilowsky May 2008

Jacques Salomon Hadamard And The Use Of Symbols In Teaching Differential Calculus, Daniel S. Drucker, Claude Schochet, John Cuzzocrea, Shlomo Sawilowsky

Journal of Modern Applied Statistical Methods

Scripta Universitatis, edited by Albert Einstein and first published in 1923, played a significant role in the establishment of Hebrew University in Jerusalem. Articles appeared on the left half of the journal in the author’s chosen language and they were translated into Hebrew on the right half. The inaugural issue contained an article by the French mathematician Jacques Hadamard (8 December 1865 – 17 October 1963). Y. Wolfson of Kharkov translated it into Hebrew. An English translation is presented here, along with scans of the original first pages that were published in French and Hebrew. Documents pertaining to the …

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On Measuring The Relative Importance Of Explanatory Variables In A Logistic Regression , D. Roland Thomas, Pengcheng Zhu, Bruno D. Zumbo, Shantanu Dutta May 2008

On Measuring The Relative Importance Of Explanatory Variables In A Logistic Regression , D. Roland Thomas, Pengcheng Zhu, Bruno D. Zumbo, Shantanu Dutta

Journal of Modern Applied Statistical Methods

A search is described for valid methods of assessing the importance of explanatory variables in logistic regression, motivated by earlier work on the relationship between corporate governance variables and the issuance of restricted voting shares (RSF). The methods explored are adaptations of Pratt’s (1987) approach for measuring variable importance in simple linear regression, which is based on a special partition of R2. Pseudo-R2 measures for logistic regression are briefly reviewed, and two measures are selected which can be partitioned in a manner analogous to that used by Pratt. One of these is ultimately selected for the variable …

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Using Exploratory Factor Analysis For Locating Invariant Referents In Factor Invariance Studies, W. Holmes Finch, Brian F. French May 2008

Using Exploratory Factor Analysis For Locating Invariant Referents In Factor Invariance Studies, W. Holmes Finch, Brian F. French

Journal of Modern Applied Statistical Methods

Model identification in multi-group confirmatory factor analysis (MCFA) requires an equality constraint of referent variables across groups. Invariance assumption violations make it difficult to locate parameters that actually differ. Suggested procedures for locating invariant referents are cumbersome, complex, and provide imperfect results. Exploratory factor analysis (EFA) may be an alternative because of its ease of use, yet empirical evaluation of its effectiveness is lacking. EFAs accuracy for distinguishing invariant from non-invariant referents was examined.

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Probability Of Coverage And Interval Length For Two-Group Techniques Assessing The Median And Trimmed Mean, S. Jonathan Mends-Cole May 2008

Probability Of Coverage And Interval Length For Two-Group Techniques Assessing The Median And Trimmed Mean, S. Jonathan Mends-Cole

Journal of Modern Applied Statistical Methods

The purpose of the present study was to assess the probability of coverage and interval length of selected statistical techniques that have a higher finite sample breakdown point than the mean and appropriate levels of probability of coverage when using Bradley’s (1978) criterion. The techniques were examined using real education and psychology datasets (Sawilowsky & Fahoome, 2003, Sawilowsky & Blair, 1992). Welch’s test exhibited appropriate coverage for the smooth symmetric, mass at zero, digit preference, and extreme bimodal distributions. Yuen’s technique performed well under an extreme bimodal distribution. Results concerning the Maritz-Jarrett and the McKean-Schrader techniques are also presented.

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Test For Spatio-Temporal Counts Being Poisson, Haiyan Chen, Howard H. Stratton May 2008

Test For Spatio-Temporal Counts Being Poisson, Haiyan Chen, Howard H. Stratton

Journal of Modern Applied Statistical Methods

The new Log-Linear Test (TL) is proposed to identify when the Poisson model fails for a collection of count random variables. TL is shown to have better rejection rate with small sample size and essentially the same power compared to a classical Fisher-Bohning’s Statistic TF for standard alternatives to Poisson.

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Measuring Overall Heterogeneity In Meta-Analyses: Application To Csf Biomarker Studies In Alzheimer’S Disease, Chengjie Xiong, Feng Gao, Yan Yan, Jingqin Luo, Yunju Sung, Gang Shi May 2008

Measuring Overall Heterogeneity In Meta-Analyses: Application To Csf Biomarker Studies In Alzheimer’S Disease, Chengjie Xiong, Feng Gao, Yan Yan, Jingqin Luo, Yunju Sung, Gang Shi

Journal of Modern Applied Statistical Methods

The interpretations of statistical inferences from meta-analyses depend on the degree of heterogeneity in the meta-analyses. Several new indices of heterogeneity in meta-analyses are proposed, and assessed the variation/difference of these indices through a large simulation study. The proposed methods are applied to biomakers of Alzheimer’s disease.

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When Sensitivity Is A Function Of Age And Time Spent In The Preclinical State In Periodic Cancer Screening, Dongfeng Wu, Ricolindo L. Cariño, Xiaoqin Wu May 2008

When Sensitivity Is A Function Of Age And Time Spent In The Preclinical State In Periodic Cancer Screening, Dongfeng Wu, Ricolindo L. Cariño, Xiaoqin Wu

Journal of Modern Applied Statistical Methods

Probability models are extended for periodic cancer screening trials to model sensitivity when it is changing with an individual’s age and time spent in the preclinical state. Wu et al. (2005) showed that sensitivity is monotone increasing with age, but intuitively, sensitivity is also a function of the time one has spent in the preclinical stage. This allows us to infer sensitivity at a late stage, just before symptoms manifest. We developed the probability model and applied Bayesian inference to the HIP study group data. The methodology we developed is also applicable to other kinds of chronic diseases.

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