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Articles 2071 - 2100 of 2920
Full-Text Articles in Statistics and Probability
Estimating Task Duration In Pert Using The Weibull Probability Distribution, Edward L. Mccombs, Matthew E. Elam, David B. Pratt
Estimating Task Duration In Pert Using The Weibull Probability Distribution, Edward L. Mccombs, Matthew E. Elam, David B. Pratt
Journal of Modern Applied Statistical Methods
The Weibull probability distribution can be used as an alternative model for task time estimates in the PERT estimating methodology. It has the same advantages as the traditional beta distribution for this application. It has additional benefits, however, that make it a preferred option.
Beyond Kappa: Estimating Inter-Rater Agreement With Nominal Classifications, Nol Bendermacher, Pierre Souren
Beyond Kappa: Estimating Inter-Rater Agreement With Nominal Classifications, Nol Bendermacher, Pierre Souren
Journal of Modern Applied Statistical Methods
Cohen’s Kappa and a number of related measures can all be criticized for their definition of correction for chance agreement. A measure is introduced that derives the corrected proportion of agreement directly from the data, thereby overcoming objections to Kappa and its related measures.
Some Estimators For The Population Mean Using Auxiliary Information Under Ranked Set Sampling, Walid A. Abu-Dayyeh, M. S. Ahmed, R. A. Ahmed, Hassen A. Muttlak
Some Estimators For The Population Mean Using Auxiliary Information Under Ranked Set Sampling, Walid A. Abu-Dayyeh, M. S. Ahmed, R. A. Ahmed, Hassen A. Muttlak
Journal of Modern Applied Statistical Methods
Auxiliary information is used along with ranking information to derive several classes of estimators to estimate the population mean of a variable of interest based on RSS (ranked set sample). The properties of these newly suggested estimators were examined. Comparisons between special cases of these estimators and other known estimators are made using a real data set. Some of the new estimators are superior to the old ones in terms of bias and mean square error.
A Heteroscedastic, Rank-Based Approach For Analyzing 2 X 2 Independent Groups Designs, Laura Mills, Robert A. Cribbie, Wei-Ming Luh
A Heteroscedastic, Rank-Based Approach For Analyzing 2 X 2 Independent Groups Designs, Laura Mills, Robert A. Cribbie, Wei-Ming Luh
Journal of Modern Applied Statistical Methods
The ANOVA F is a widely used statistic in psychological research despite its shortcomings when the assumptions of normality and variance heterogeneity are violated. A Monte Carlo investigation compared Type I error and power rates of the ANOVA F, Alexander-Govern with trimmed means and Johnson transformation, Welch-James with trimmed means and Johnson Transformation, Welch with trimmed means, and Welch on ranked data using Johansen’s interaction procedure. Results suggest that the ANOVA F is not appropriate when assumptions of normality and variance homogeneity are violated, and that the Welch/Johansen on ranks offers the best balance of empirical Type I error …
Efficiency Of Canonical Discriminant Function Versus Mahalanobis Distance In Differentiating Groups: Screening Ovarian Cancer In A Multivariate System Analysis Using Enzyme Markers, Chinmoy K. Bose
Journal of Modern Applied Statistical Methods
Due to its low prevalence, high mortality and uniquely hidden intrapelvic position, ovarian cancer remains a subject of intense interest to researchers. Statistical calculation and new technology both have major roles to play in the effort to screen this cancer at an early stage. Advanced statistics, such as multivariate analysis, remain at the root of screening endeavors. Multivariate analysis has the power to combine many tests and to produce better results in terms high specificity and positive predictive value. Multivariate analysis techniques include Mahalanobis distance (D2), canonical stepwise discriminant function (Z) and Posterior Probability. These may have varied …
Applying Census Data For Small Area Estimation In Community And Social Service Planning, Michael Wolf-Branigin, Hyon-Sook Suh, Star Muir, Emily S. Ihara
Applying Census Data For Small Area Estimation In Community And Social Service Planning, Michael Wolf-Branigin, Hyon-Sook Suh, Star Muir, Emily S. Ihara
Journal of Modern Applied Statistical Methods
Small area estimation provides a tool for community analysis. A procedure for accessing, selecting, joining and analyzing US Census data is provided. Skills acquired while completing the procedure include accessing census data, downloading boundary files and displaying themes. Such skills are valuable tools for students to possess as they enter the workforce.
A Comparative Study Of Bayesian Model Selection Criteria For Capture-Recapture Models For Closed Populations, Ross M. Gosky, Sujit K. Ghosh
A Comparative Study Of Bayesian Model Selection Criteria For Capture-Recapture Models For Closed Populations, Ross M. Gosky, Sujit K. Ghosh
Journal of Modern Applied Statistical Methods
Capture-Recapture models estimate unknown population sizes. Eight standard closed population models exist, allowing for time, behavioral, and heterogeneity effects. Bayesian versions of these models are presented and use of Akaike's Information Criterion (AIC) and the Deviance Information Criterion (DIC) are explored as model selection tools, through simulation and real dataset analysis.
Quantile Regression: On Inferences About The Slopes Corresponding To One, Two Or Three Quantiles, Rand R. Wilcox, Kathleen Costa
Quantile Regression: On Inferences About The Slopes Corresponding To One, Two Or Three Quantiles, Rand R. Wilcox, Kathleen Costa
Journal of Modern Applied Statistical Methods
The problem of testing hypotheses about the slope of a quantile regression line when the sample size is small is considered. A modified bootstrap method is suggested that is found to have certain advantages over the inverse rank method recommended by Koenker (1994). A method is suggested that simultaneously controls the probability of at least one Type I error when performing two or three tests corresponding to two or three specific quantiles. Using data from actual studies, it is illustrated that the new method can yield substantially shorter confidence intervals than the rank inverse method and, even with a large …
A Monte Carlo Comparison Of Regression Estimators When The Error Distribution Is Long-Tailed Symmetric, Oya Can Mutan, Birdal Şenoğlu
A Monte Carlo Comparison Of Regression Estimators When The Error Distribution Is Long-Tailed Symmetric, Oya Can Mutan, Birdal Şenoğlu
Journal of Modern Applied Statistical Methods
The performances of the ordinary least squares (OLS), modified maximum likelihood (MML), least absolute deviations (LAD), Winsorized least squares (WIN), trimmed least squares (TLS), Theil’s (Theil) and weighted Theil’s (Weighted Theil) estimators are compared under the simple linear regression model in terms of their bias and efficiency when the distribution of error terms is long-tailed symmetric.
Improved Confidence Intervals For The Difference Between Two Proportions, James F. Reed Iii
Improved Confidence Intervals For The Difference Between Two Proportions, James F. Reed Iii
Journal of Modern Applied Statistical Methods
Wald-z asymptotic methods, with and without a continuity correction, have less than nominal coverage probability characteristics but continue to be used. Newcombe's hybrid method and the Agresti-Caffo methods have coverage probabilities that are near nominal for either equal or unequal samples. Newcombe's hybrid and Agresti-Caffo methods demonstrate superior coverage properties.
Multiple Regression In Pair Correlation Solution, Stan Lipovetsky
Multiple Regression In Pair Correlation Solution, Stan Lipovetsky
Journal of Modern Applied Statistical Methods
Behavior of the coefficients of ordinary least squares (OLS) regression with the coefficients regularized by the one-parameter ridge (Ridge-1) and two-parameter ridge (Ridge-2) regressions are compared. The ridge models are not prone to multicollinearity. The fit quality of Ridge-2 does not decrease with the profile parameter increase, but the Ridge-2 model converges to a solution proportional to the coefficients of pair correlation between the dependent variable and predictors. The Correlation-Regression (CORE) model suggests meaningful coefficients and net effects for the individual impact of the predictors, high quality model fit, and convenient analysis and interpretation of the regression. Simulation with three …
Bayesian Inference On The Variance Of Normal Distribution Using Moving Extremes Ranked Set Sampling, Said Ali Al-Hadhrami, Amer Ibrahim Al-Omari
Bayesian Inference On The Variance Of Normal Distribution Using Moving Extremes Ranked Set Sampling, Said Ali Al-Hadhrami, Amer Ibrahim Al-Omari
Journal of Modern Applied Statistical Methods
Bayesian inference of the variance of the normal distribution is considered using moving extremes ranked set sampling (MERSS) and is compared with the simple random sampling (SRS) method. Generalized maximum likelihood estimators (GMLE), confidence intervals (CI), and different testing hypotheses are considered using simple hypothesis versus simple hypothesis, simple hypothesis versus composite alternative, and composite hypothesis versus composite alternative based on MERSS and compared with SRS. It is shown that modified inferences using MERSS are more efficient than their counterparts based on SRS.
Quantifying Bimodality Part 2: A Likelihood Ratio Test For The Comparison Of A Unimodal Normal Distribution And A Bimodal Mixture Of Two Normal Distributions. Bruno D. Zumbo Is, B. W. Frankland, Bruno D. Zumbo
Quantifying Bimodality Part 2: A Likelihood Ratio Test For The Comparison Of A Unimodal Normal Distribution And A Bimodal Mixture Of Two Normal Distributions. Bruno D. Zumbo Is, B. W. Frankland, Bruno D. Zumbo
Journal of Modern Applied Statistical Methods
Scientists in a variety of fields are often faced with the question of whether a sample is best described as unimodal or bimodal. In an earlier paper (Frankland & Zumbo, 2002), a simple and convenient method for assessing bimodality was described. That method is extended by developing and demonstrating a likelihood ratio test (LRT) for bimodality for the comparison of a unimodal normal distribution and a bimodal mixture of two normal distributions. As in Frankland and Zumbo (2002), the LRT approach is demonstrated using algorithms in SPSS.
Email: A Note On Hypothesis Tests After Correction For Autocorrelation: Solace For The Cochrane-Orcutt Method?, Terry E. Dielman
Email: A Note On Hypothesis Tests After Correction For Autocorrelation: Solace For The Cochrane-Orcutt Method?, Terry E. Dielman
Journal of Modern Applied Statistical Methods
The behavior of the t test in small samples for coefficient significance in time-series regressions is examined after using the Prais-Winsten (PW) and Cochrane-Orcutt (CO) corrections for autocorrelation. Results are compared to ordinary least squares and generalized least squares.
Quel Test For Two Linear Restrictions In The Nonlinear Models, Krishna K. Saha
Quel Test For Two Linear Restrictions In The Nonlinear Models, Krishna K. Saha
Journal of Modern Applied Statistical Methods
An alternative Wald type test called the quel test is developed for two linear restrictions by finding the critical region based on the quel utilizing the repeated values of estimated parameters of interest under the null. Simulation shows evidence that the full quel test performs best in that it holds nominal level well and shows monotonic increasing power properties.
Comparative Power Of The Independent T, Permutation T, And Wilcoxontests, Michèle Weber, Shlomo Sawilowsky
Comparative Power Of The Independent T, Permutation T, And Wilcoxontests, Michèle Weber, Shlomo Sawilowsky
Journal of Modern Applied Statistical Methods
The nonparametric Wilcoxon Rank Sum (also known as the Mann-Whitney U) and the permutation t-tests are robust with respect to Type I error for departures from population normality, and both are powerful alternatives to the independent samples Student’s t-test for detecting shift in location. The question remains regarding their comparative statistical power for small samples, particularly for non-normal distributions. Monte Carlo simulations indicated the rank-based Wilcoxon test was found to be more powerful than both the t and the permutation t-tests.
Industrialization In Animal Agriculture: A Kalman Filter Analysis, Oya S. Erdogdu, Levent Ozbek
Industrialization In Animal Agriculture: A Kalman Filter Analysis, Oya S. Erdogdu, Levent Ozbek
Journal of Modern Applied Statistical Methods
Studies discussing the effects of technological developments on (animal) agricultural production argue that the effective usage of chemicals and genetic engineering increase control over production processes, which in turn decreases seasonality (one significant factor defining agricultural production) significantly and brings standardization to production. Studies on broilery also show that production is not limited by nature determined seasons. Supply side changes accompanied by changes in demand have led to more healthier, standardized products. Using tools of economics and statistics, this study documents this transformation in animal agricultural production of beef, pork and milk. Results indicate decreasing seasonality, thus the industralization of …
Aligned Rank Tests For Interactions In Split-Plot Designs: Distributional Assumptions And Stochastic Heterogeneity, T. Mark Beasley, Bruno D. Zumbo
Aligned Rank Tests For Interactions In Split-Plot Designs: Distributional Assumptions And Stochastic Heterogeneity, T. Mark Beasley, Bruno D. Zumbo
Journal of Modern Applied Statistical Methods
Three aligned rank methods for transforming data from multiple group repeated measures (split-plot) designs are reviewed. Univariate and multivariate statistics for testing the interaction in split-plot designs are elaborated. Computational examples are presented to provide a context for performing these ranking procedures and statistical tests. SAS/IML and SPSS syntax code to perform the procedures is included in the Appendix.
Pain As A Predictor Of Depression Treatment Outcomes In Women With Childhood Sexual Abuse, Ellen L. Poleshuck, Nancy L. Talbot, Haiyan Su, Xin Tu, Linda Chaudron, Stephanie Gamble, Donna E. Giles
Pain As A Predictor Of Depression Treatment Outcomes In Women With Childhood Sexual Abuse, Ellen L. Poleshuck, Nancy L. Talbot, Haiyan Su, Xin Tu, Linda Chaudron, Stephanie Gamble, Donna E. Giles
Department of Applied Mathematics and Statistics Faculty Scholarship and Creative Works
Objectives: Childhood sexual abuse (CSA) increases risk for both depression and pain in women. Pain is associated with worse depression treatment response. The contribution of pain to depression treatment outcomes in women with histories of CSA is unknown. This study examined whether clinically significant pain would be associated with worse depression and functioning outcomes among women with CSA histories treated with interpersonal psychotherapy. Method: Participants were 66 women with major depression and CSA who presented to a community mental health center. An interpersonal psychotherapy protocol planned for 14 weekly sessions followed by 2 biweekly sessions. Patients were classified as experiencing …
The Comparison Of Model Selection Criteria When Selecting Among Competing Hierarchical Linear Models, Tiffany A. Whittaker, Carolyn F. Furlow
The Comparison Of Model Selection Criteria When Selecting Among Competing Hierarchical Linear Models, Tiffany A. Whittaker, Carolyn F. Furlow
Journal of Modern Applied Statistical Methods
Little is known about the use and accuracy of model selection criteria when selecting among a set of competing multilevel models. The practices of applied researchers and the performance of five model selection criteria are examined when selecting the correct multilevel model using simulation techniques.
Bias In Stabilized Sieve Sampling, Liming Guan, John P. Wendell
Bias In Stabilized Sieve Sampling, Liming Guan, John P. Wendell
Journal of Modern Applied Statistical Methods
The stabilized sieve sample selection method (SSM) is considered to be a probability proportional to size (PPS) sampling method with an unbiased estimator (Horgan 1997, 1998). This article demonstrates that SSM does not select items with PPS and that the point estimator is biased.
A New Approximate Bayesian Approach For Decision Making About The Variance Of A Gaussian Distribution Versus The Classical Approach, Vincent A. R. Camara
A New Approximate Bayesian Approach For Decision Making About The Variance Of A Gaussian Distribution Versus The Classical Approach, Vincent A. R. Camara
Journal of Modern Applied Statistical Methods
Rules of decision-making about the variance of a Gaussian distribution are obtained and compared. Considering the square error loss function, an approximate Bayesian decision rule for the variance of a normal population is derived. Using normal data and SAS software, the obtained approximate Bayesian test results were compared to their counterparts obtained with the well-known classical decision rule. It is shown that the proposed approximate Bayesian decision rule relies only on observations. The classical decision rule, which uses the Chi-square statistic, does not always yield the best results: the proposed approach often performs better.
Which Is The Best Parametric Statistical Method For Analyzing Delphi Data?, Hiral A. Shah, Sema A. Kalaian
Which Is The Best Parametric Statistical Method For Analyzing Delphi Data?, Hiral A. Shah, Sema A. Kalaian
Journal of Modern Applied Statistical Methods
This study compares the three parametric statistical methods: coefficient of variation, Pearson correlation coefficient, and F-test to obtain reliability in a Delphi study that involved more than 100 participants. The results of this study indicated that coefficient of variation was the best procedure to obtain reliability in such a study.
A Socratic Dialogue, Vance W. Berger
A Socratic Dialogue, Vance W. Berger
Journal of Modern Applied Statistical Methods
Socrates has found some aspects of medical biostatistics a bit confusing, and wishes to discuss some of these issues with Simplicio, a prominent medical researcher. This Socratic dialogue will shed some light on the errant use of parametric analyses in clinical trials.
A Comparison Of Maximum Likelihood And Expected A Posteriori Estimation For Polychoric Correlation Using Monte Carlo Simulation, Jinsong Chen, Jaehwa Choi
A Comparison Of Maximum Likelihood And Expected A Posteriori Estimation For Polychoric Correlation Using Monte Carlo Simulation, Jinsong Chen, Jaehwa Choi
Journal of Modern Applied Statistical Methods
This study aims to compare the maximum likelihood (ML) and expected a posterior (EAP) estimation for polychoric correlation (PCC) under diverse conditions, especially when considering a sample size. As the ML is the classical solution to estimate PCC, the EAP is a new method based on Bayes’ theorem. Different types of prior distributions are also adapted to investigate the sensitivity of prior distribution onto the PCC estimate for the EAP case. The Monte Carlo simulation is used for this comparison by a specialized program code in MATLAB.
Detecting Near-Earth Objects Using Cross-Correlation With A Point Spread Function, Anthony P. O'Dell
Detecting Near-Earth Objects Using Cross-Correlation With A Point Spread Function, Anthony P. O'Dell
Theses and Dissertations
This thesis describes a process to help discover Near-Earth Objects (NEOs) of larger than 140 meters in diameter from ground based telescopes. The process involves using Nyquist sampling rate to take data from a ground-based telescope and measuring the atmospheric seeing parameter, r0, at the time of data collection. r0 is then used to create a point spread function (PSF) for a NEO at the visual magnitude limit of the telescope and exposure time. This PSF is cross-correlated with the Nyquist sampling rate image from the telescope to reduce the noise and therefore increase the detection probability of …
A Berry-Esseen Theorem For Sample Quantiles Under Weak Dependence, S. N. Lahiri, Shuxia Sun
A Berry-Esseen Theorem For Sample Quantiles Under Weak Dependence, S. N. Lahiri, Shuxia Sun
Mathematics and Statistics Faculty Publications
This paper proves a Berry-Esseen theorem for sample quantiles of strongly-mixing random variables under a polynomial mixing rate. The rate of normal approximation is shown to be O(n-1/2) as n -> infinity, where n denotes the sample size. This result is in sharp contrast to the case of the sample mean of strongly-mixing random variables where the rate O(n-1/2) is not known even under an exponential strong mixing rate. The main result of the paper has applications in finance and econometrics as financial time series important data often are heavy-tailed and quantile …
Comments On "Getting Scarred And Winning Lotteries: Effects Of Exemplar Cuing And Statistical Format On Imagining Low-Probability Events," By Newell, Mitchell, And Hayes (2008), Jonathan Koehler, Laura Macchi
Comments On "Getting Scarred And Winning Lotteries: Effects Of Exemplar Cuing And Statistical Format On Imagining Low-Probability Events," By Newell, Mitchell, And Hayes (2008), Jonathan Koehler, Laura Macchi
Faculty Working Papers
Newell, Mitchell, and Hayes (NMH) conduct three experiments designed to test whether exemplar cuing (EC) theory or a statistical format theory provides a more accurate account for how people make judgments about low-probability events. They report finding support for the statistical format theory and little or no support for EC. However, NMH misstate the requirements for the production of exemplars in EC theory. As a result, they confuse non-exemplar conditions with exemplar conditions in their experiments, and find results that are virtually irrelevant to EC theory.
Why Study Applied/Agricultural Economics, Matt Bogard
Why Study Applied/Agricultural Economics, Matt Bogard
Agriculture Department Seminar Series
Agricultural Economics is a very applied field covering many topics beyond those stereotypically thought of as pertaining to agriculture. These may include finance and risk management, environmental and natural resource economics, game theory, or public policy analysis to name a few.
A Direct Solution Of The Robin Inverse Problem, Weifu Fang, Suxing Zeng
A Direct Solution Of The Robin Inverse Problem, Weifu Fang, Suxing Zeng
Mathematics and Statistics Faculty Publications
We present a direct, linear boundary integral equation method for the inverse problem of recovering the Robin coefficient from a single partial boundary measurement of the solution to the Laplace equation.