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Articles 781 - 810 of 1162
Full-Text Articles in Statistics and Probability
Confidence Intervals For The Squared Multiple Semipartial Correlation Coefficient, James Algina, H. J. Keselman, Randall D. Penfield
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.
On A Test Of Independence Via Quantiles That Is Sensitive To Curvature, Rand R. Wilcox
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 …
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
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 …
Estimating How Many Observations Are Needed To Obtain A Required Level Of Reliability, David A. Walker
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.
Tests For Independence In Two-Way Contingency Tables With Small Samples, Stephen Sharp
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 …
Utility Of Weights For Weighted Kappa As A Measure Of Interrater Agreement On Ordinal Scale, Moonseong Heo
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 …
Robustness Of Some Estimators Of Linear Model With Autocorrelated Error Terms When Stochastic Regressors Are Normally Distributed, Kayode Ayinde, J. O. Olaomi
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 …
Jacques Salomon Hadamard And The Use Of Symbols In Teaching Differential Calculus, Daniel S. Drucker, Claude Schochet, John Cuzzocrea, Shlomo Sawilowsky
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 …
On Measuring The Relative Importance Of Explanatory Variables In A Logistic Regression , D. Roland Thomas, Pengcheng Zhu, Bruno D. Zumbo, Shantanu Dutta
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 …
Using Exploratory Factor Analysis For Locating Invariant Referents In Factor Invariance Studies, W. Holmes Finch, Brian F. French
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.
Probability Of Coverage And Interval Length For Two-Group Techniques Assessing The Median And Trimmed Mean, S. Jonathan Mends-Cole
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.
Test For Spatio-Temporal Counts Being Poisson, Haiyan Chen, Howard H. Stratton
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.
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
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.
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
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.
Log-Linear Model To Assess Socioeconomic And Environmental Factors With Childhood Diarrhea Using Hospital Based Surveillance, Krishnan Rajendran, Thandavarayan Ramamurthy, Sujit Kumar Bhattacharya
Log-Linear Model To Assess Socioeconomic And Environmental Factors With Childhood Diarrhea Using Hospital Based Surveillance, Krishnan Rajendran, Thandavarayan Ramamurthy, Sujit Kumar Bhattacharya
Journal of Modern Applied Statistical Methods
Categorical outcomes with environment factors analyzed by log linear model are frequent in the environmental epidemiological literature. Epidemiological and socio-economical factors were obtained on 1,119 children below the age of 5 from Infectious Diseases Hospital (IDH) at the Kolkata, India. Significant associations of diarrhea were observed in the rural areas with family income, father’s occupation as a daily labor, literacy of parents, non-cemented floor and wall constructed of mud, and type of storage (wide mouthed earthen pot). The results of the study with specific Log linear model confirm environmental factors were important implications for childhood diarrhea in the rural community. …
Robust General Linear Models And Graphics Via A User Interface (Web Rglm), Kimberly Crimin, Asheber Abebe, Joseph W. Mckean
Robust General Linear Models And Graphics Via A User Interface (Web Rglm), Kimberly Crimin, Asheber Abebe, Joseph W. Mckean
Journal of Modern Applied Statistical Methods
Rank-based procedures provide superior estimation and testing techniques when the data deviate from normality or contain gross outliers. However, these robust techniques are rarely incorporated in a nonparametric statistics or methods courses due to the lack of computational tools. One reason for this is the existence of certain unavoidable complexities in the numerical methods due to the absence of a closedform solution for the rank estimation problem. This article introduces a user interface, Web RGLM, which may be used to perform rank-based analyses of linear models across the World Wide Web. These models include simple location problems to complicated ANOVA …
Effect On Recreation Benefit Estimates From Correcting For On-Site Sampling Biases And Heterogeneous Trip Overdispersion In Count Data Recreation Demand Models (Stata), Roberto Martínez-Espiñeira, Joseph M. Hilbe
Effect On Recreation Benefit Estimates From Correcting For On-Site Sampling Biases And Heterogeneous Trip Overdispersion In Count Data Recreation Demand Models (Stata), Roberto Martínez-Espiñeira, Joseph M. Hilbe
Journal of Modern Applied Statistical Methods
Correction procedures (STATA commands NBSTRAT and GNBSTRAT) are applied to simultaneously account for zero-truncation, endogenous stratification, and overdispersion, and also consider heterogeneity in the overdispersion parameter. Their effect is shown on welfare estimates from previous studies, confirming that the routines perform the appropriate correction and only when endogenous stratification is expected.
Computing Multivariate Process Capability Indices (Excel), Michele Scagliarini, Raffaele Vermiglio
Computing Multivariate Process Capability Indices (Excel), Michele Scagliarini, Raffaele Vermiglio
Journal of Modern Applied Statistical Methods
In manufacturing industry there is growing interest in measures of process capability under multivariate setting. Although there are many statistical packages to assess univariate capability, a current problem with the multivariate measures of capability is the shortage of user friendly software. In this article a Visual Basic program has been developed to realize an Excel spreadsheet that may be used to compute two multivariate measures of capability. The aim of this article is to provide a useful tool for practitioners dealing with multivariate capability assessment problems. The features of the program include easy data entry and clear report format.
Logit Estimation Using Warner’S Randomized Response Model, Zawar Hussain, Javid Shabbir
Logit Estimation Using Warner’S Randomized Response Model, Zawar Hussain, Javid Shabbir
Journal of Modern Applied Statistical Methods
A modified hidden logit estimation procedure is presented based on Warner (1965) randomized response model. Monte Carlo simulations explore the behavior of this estimator and compare its performance with the ordinary logits estimator. Warner’s model is more protective and less jeopardizing.
Estimation Of Covariance Matrix In Signal Processing When The Noise Covariance Matrix Is Arbitrary, Madhusudan Bhandary
Estimation Of Covariance Matrix In Signal Processing When The Noise Covariance Matrix Is Arbitrary, Madhusudan Bhandary
Journal of Modern Applied Statistical Methods
An estimator of the covariance matrix in signal processing is derived when the noise covariance matrix is arbitrary based on the method of maximum likelihood estimation. The estimator is a continuous function of the eigenvalues and eigenvectors of the matrix Σ̂11/2S∗Σ̂11/2, where S∗ is the sample covariance matrix of observations consisting of both noise and signals and Σ̂1 is the estimator of covariance matrix based on observations consisting of noise only. Strong consistency and asymptotic normality of the estimator are briefly discussed.
On The Length Of Nhl Shootouts, W. J. Hurley
On The Length Of Nhl Shootouts, W. J. Hurley
Journal of Modern Applied Statistical Methods
When NHL teams are tied after 60 minutes of regulation time and 5 minutes of sudden-death overtime, they go to a shootout to determine who gets the overtime point. Teams alternate shots until a winner is determined. The probability of observing shootouts of various lengths is calculated.
An Omnibus Test When Using A Regression Estimator With Multiple Predictors, Rand R. Wilcox
An Omnibus Test When Using A Regression Estimator With Multiple Predictors, Rand R. Wilcox
Journal of Modern Applied Statistical Methods
In quantile regression, the goal is to estimate theγ quantile of Y given values for p predictors. Methods for making inferences about the individual slope parameters have been proposed, some of which have been found to perform very well in simulations. But for an omnibus test that all slope parameters are zero, it appears that little is known about how best to proceed. For the special case γ =.5, a drop-in-dispersion test has been recommended, but it requires a large sample size to control the probability of a Type I error and it assumes that the usual error term is …
Bayesian Subset Selection Of Binomial Parameters Using Possibly Misclassified Data, James D. Stamey, Thomas L. Bratcher, Dean M. Young
Bayesian Subset Selection Of Binomial Parameters Using Possibly Misclassified Data, James D. Stamey, Thomas L. Bratcher, Dean M. Young
Journal of Modern Applied Statistical Methods
Three Bayesian approaches are considered for the selection of binomial proportion parameters when data is subject to misclassification. The cases where the misclassification is non-differential and differential were considered, thus extending previous work which considered only non-differential misclassification. In this article, various selection criteria are applied to a simulated data set and a real data set.
A Weighted Moving Average Process For Forcasting, Shou Hsing Shih, Chris P. Tsokos
A Weighted Moving Average Process For Forcasting, Shou Hsing Shih, Chris P. Tsokos
Journal of Modern Applied Statistical Methods
A forecasting model for a nonstationary stochastic realization is proposed based on modifying a given time series into a new k-time moving average time series. 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 of the accuracy of their responses is given.
A Comparison Of Procedures For The Analysis Of Multivariate Repeated Measurements, Lisa M. Lix, Anita M. Lloyd
A Comparison Of Procedures For The Analysis Of Multivariate Repeated Measurements, Lisa M. Lix, Anita M. Lloyd
Journal of Modern Applied Statistical Methods
Three procedures for analyzing within-subjects effects in multivariate repeated measures designs are compared when group covariances are heterogeneous: the multiple regression model (MRM) with a structured covariance, Johansen’s (1980) procedure, and the multivariate Brown and Forsythe (1974) procedure. A preliminary likelihood ratio test of a Kronecker product covariance structure is sensitive to sample size and derivational assumption violations. Error rates of the procedures are generally well-controlled except when the distribution is skewed. The MRM procedure displayed few power advantages over the other procedures.
Interference On Overlapping Coefficients In Two Exponential Populations, Mohammad Fraiwan Al-Saleh, Hani M. Samawi
Interference On Overlapping Coefficients In Two Exponential Populations, Mohammad Fraiwan Al-Saleh, Hani M. Samawi
Journal of Modern Applied Statistical Methods
Three measures of overlap, namely Matusita’s measureρ , Morisita’s measure λ and Weitzman’s measure Δ are investigated in this article for two exponential populations with different means. It is well that the estimators of those measures of overlap are biased. The bias is of these estimators depends on the unknown overlap parameters. There are no closed-form, exact formulas, for those estimators variances or their exact sampling distributions. Monte Carlo evaluations are used to study the bias and precision of the proposed overlap measures. Bootstrap method and Taylor series approximation are used to construct confidence intervals for the overlap measures
Optimal Trimming And Outlier Elimination, Philip H. Ramsey, Patricia P. Ramsey
Optimal Trimming And Outlier Elimination, Philip H. Ramsey, Patricia P. Ramsey
Journal of Modern Applied Statistical Methods
Five data sets with known true values are used to determine the optimal number of pairs that should be trimmed in order to produce the minimum relative error. The optimal trimming in the five data sets is found to be 1%, 5%, 7%, 10% and 28%. The 28% rate is shown to be an outlier among the five data sets. Results of four data sets are used to establish cutoff values for outlier detection in two robust methods of outlier detection.
The Non-Parametric Difference Score: A Workable Solution For Analyzing Two-Wave Change When The Measures Themselves Change Across Waves, Jennifer E. V. Lloyd, Bruno D. Zumbo
The Non-Parametric Difference Score: A Workable Solution For Analyzing Two-Wave Change When The Measures Themselves Change Across Waves, Jennifer E. V. Lloyd, Bruno D. Zumbo
Journal of Modern Applied Statistical Methods
The non-parametric difference score is introduced. It is a workable solution to the problem of analyzing change over two waves (i.e., a pretest-posttest design) when the measures themselves vary over time. An example highlighting the solution’s implementation is provided, as is a discussion of the solution’s assumptions, strengths, and limitations.
Semi Parametric Estimation Of Some Reliability Measures Of Geometric Distribution, Mathachan Pathiyil, E.S. Jeevanand
Semi Parametric Estimation Of Some Reliability Measures Of Geometric Distribution, Mathachan Pathiyil, E.S. Jeevanand
Journal of Modern Applied Statistical Methods
Semi parametric estimators of the survival function, the hazard function, and the mean residual life function of geometric distribution using uncensored and Type II censored samples are obtained. The accuracy of the estimators so obtained is investigated empirically using simulated samples. The results are applied to a real life data set for illustration.
Tests For 2 X 2 Tables In Clinical Trials, Vic Hasselblad, Yulia Lokhnygina
Tests For 2 X 2 Tables In Clinical Trials, Vic Hasselblad, Yulia Lokhnygina
Journal of Modern Applied Statistical Methods
Five standard tests are compared: chi-squared, Fisher's exact, Yates’ correction, Fisher’s exact mid-p, and Barnard’s. Yates’ is always inferior to Fisher’s exact. Fisher’s exact is so conservative that one should look for alternatives. For certain sample sizes, Fisher’s mid-p or Barnard’s test maintain the nominal alpha and have superior power.