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Articles 841 - 870 of 1191
Full-Text Articles in Statistical Theory
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.
Performance Of Some Correlation Coefficients When Applied To Zero-Clustered Data, L. W. Huson
Performance Of Some Correlation Coefficients When Applied To Zero-Clustered Data, L. W. Huson
Journal of Modern Applied Statistical Methods
Zero-clustered data occur widely in medical research and are characterised by the presence of a group of observations of value zero in a distribution of otherwise continuous non-negative responses. A simulation study was conducted to investigate the properties of a number of correlation coefficients applied to samples of zero-clustered data.
Covariate Dependent Markov Models For Analysis Of Repeated Binary Outcomes, M.A. Islam, R.I. Chowdhury, K.P. Singh
Covariate Dependent Markov Models For Analysis Of Repeated Binary Outcomes, M.A. Islam, R.I. Chowdhury, K.P. Singh
Journal of Modern Applied Statistical Methods
The covariate dependence in a higher order Markov models is examined. First order Markov models with covariate dependence are discussed and are generalized for higher order. A simple alternative is also proposed. The estimation procedure is discussed for higher order with a number of covariates. The proposed model takes into account the past transitions. Transitions are fitted and are tested in order to examine their influence on the most recent transitions. Applications are illustrated using maternal morbidity during pregnancy. The binary outcome at each visit during pregnancy is observed for each subject and then the covariate dependent Markov models are …
The Correlation Coefficients, Rudy A. Gideon
The Correlation Coefficients, Rudy A. Gideon
Journal of Modern Applied Statistical Methods
A generalized method of defining and interpreting correlation coefficients is given. Seven correlation coefficients are defined — three for continuous data and four on the ranks of the data. A quick calculation of the rank based correlation coefficients using a 0-1 graph-matrix is shown. Examples and comparisons are given.
A Simple Method For Finding Emperical Liklihood Type Intervals For The Roc Curve, Ayman Baklizi
A Simple Method For Finding Emperical Liklihood Type Intervals For The Roc Curve, Ayman Baklizi
Journal of Modern Applied Statistical Methods
Interval estimation of the ROC curve is considered using the empirical likelihood techniques. Suggested is a procedure that is very simple computationally and avoids the constrained optimization problems usually faced with empirical likelihood methods. Various modifications are suggested and the performance of the intervals is evaluated in terms of their coverage probability. The results show that some of the suggested intervals compete well with other intervals known in the literature.
The Effect Of Garch (1,1) On The Granger Causality Test In Stable Var Models, Panagiotis Mantalos, Ghazi Shukur, Pär Sjölander
The Effect Of Garch (1,1) On The Granger Causality Test In Stable Var Models, Panagiotis Mantalos, Ghazi Shukur, Pär Sjölander
Journal of Modern Applied Statistical Methods
Using Monte Carlo methods, the properties of Granger causality test in stable VAR models are studied under the presence of different magnitudes of GARCH effects in the error terms. Analysis reveals that substantial GARCH effects influence the size properties of the Granger causality test, especially in small samples. The power functions of the test are usually slightly lower when GARCH effects are imposed among the residuals compared with the case of white noise residuals.
Optimum Choice Of Covariates For A Series Of Sbibds Obtained Through Projective Geometry, Ganesh Dutta, Premadhis Das, Nripes Kumar Mandal
Optimum Choice Of Covariates For A Series Of Sbibds Obtained Through Projective Geometry, Ganesh Dutta, Premadhis Das, Nripes Kumar Mandal
Journal of Modern Applied Statistical Methods
A block design set up is considered in presence of a number of controllable covariates. The problem is that of choosing the values of the covariates so that for a given block design, it is optimum in the sense of attaining minimum variance for the estimation of each of the covariate parameters. In case of incomplete block designs, the choice of the values of the covariates depends heavily on the allocation of treatments to the plots of blocks; more specifically on the method of construction of the incomplete block design. In this paper the situation where the block design is …
Reply (To Ian R. White), Kung-Jong Lui
Reply (To Ian R. White), Kung-Jong Lui
Journal of Modern Applied Statistical Methods
No abstract provided.
A New Generalization Of Negative Ploya-Eggenberger Distribution And Its Applications, Anwar Hassan, Sheikh Nilal Ahmad
A New Generalization Of Negative Ploya-Eggenberger Distribution And Its Applications, Anwar Hassan, Sheikh Nilal Ahmad
Journal of Modern Applied Statistical Methods
A new generalization of negative Polya-Eggenberger distribution (GNPED) has been obtained by mixing the negative binomial distribution with generalized beta distribution-Π defined by Nadarajah and Kotz (2003). Some special cases and properties of GNPED have been studied. Further, the proposed model has been fitted to two data sets (used by Gupta & Ong, 2004) that provide a satisfactory fit and better alternative as compared to negative binomial and some of its mixture models and extensions. Also, the negative Polya-Eggenberger distribution (NPED), obtained by mixing negative binomial with beta distribution of I-kind, has been fitted to the same data sets for …
From Information Lost To Knowledge Gained: The Benefits Of Analyzing All The Research Evidence, Joseph L. Balloun, Hilton Barrett
From Information Lost To Knowledge Gained: The Benefits Of Analyzing All The Research Evidence, Joseph L. Balloun, Hilton Barrett
Journal of Modern Applied Statistical Methods
Data analyses should reveal truths about data. To the extent possible analyses should tell a complete picture. Data analyses should not inadvertently ignore phenomena that might be discovered in sample data sets. However, common univariate or multivariate data analysis methods tend to be based on only the means, standard deviations, and Pearson correlations. The result is that many important truths are discovered, but not the whole truth. This article illustrates in a sample data set that (a) data analyses of other properties of variables and groups are feasible and practical, and (b) such analyses may reveal important information not otherwise …
Time-Series Intervention Analysis Using Itsacorr: Fatal Flaws, Bradley E. Huitema, Joseph W. Mckean, Sean Laraway
Time-Series Intervention Analysis Using Itsacorr: Fatal Flaws, Bradley E. Huitema, Joseph W. Mckean, Sean Laraway
Journal of Modern Applied Statistical Methods
The ITSACORR method (Crosbie, 1993, 1995) is evaluated for the analysis of two-phase interrupted time-series designs. It is shown that each component of the ITSACORR framework (including the structural model, the design matrix, the autocorrelation estimator, the ultimate parameter estimation scheme, and the inferential method) contains fatal flaws.
Multiple Comparison Of Medians Using Permutation Tests, Scott J. Richter, Melinda H. Mccann
Multiple Comparison Of Medians Using Permutation Tests, Scott J. Richter, Melinda H. Mccann
Journal of Modern Applied Statistical Methods
A robust method is proposed for simultaneous pairwise comparison using permutation tests and median differences. The new procedure provides strong control of familywise error rate and has better power properties than the median procedure of Nemenyi/Levy. It can be more powerful than the Tukey-Kramer procedure using mean differences, especially for nonnormal distributions and unequal sample sizes.
The Effect Of Different Degrees Of Freedom Of The Chi-Square Distribution On The Statistical Power Of The T, Permutation T, And Wilcoxon Tests, Michèle Weber
Journal of Modern Applied Statistical Methods
The Chi-square distribution is used quite often in Monte Carlo studies to examine statistical power of competing statistics. The power spectrum of the t-test, Wilcoxon test, and permutation t test are compared under various degrees of freedom for this distribution. The two t tests have similar power, which is generally less than the Wilcoxon.
Sensitivity Curves For Asymmetric Trimming Hinge Estimators, D.B. Stark, J.F. Reed Iii
Sensitivity Curves For Asymmetric Trimming Hinge Estimators, D.B. Stark, J.F. Reed Iii
Journal of Modern Applied Statistical Methods
Robust estimators have been developed and tested for symmetric distributions via simulation studies. The primary objective was to show that they are more efficient than the sample mean when used in conjunction with asymmetric distributions. Little attention has been given to how they perform on data that are from asymmetric distributions, or from distributions that have inherent anomalies (messy data). Thus, the behavior of hinge estimators using sensitivity curve are examined.
Large Deviations Techniques For Error Exponents To Multiple Hypothesis Lao Testing, Leader Navaei
Large Deviations Techniques For Error Exponents To Multiple Hypothesis Lao Testing, Leader Navaei
Journal of Modern Applied Statistical Methods
In this article the problem of multiple hypotheses testing using a theory of large deviations is studied. The reliability matrix of Logarithmically Asymptotically Optimal (LAO) tests is introduced and described, and the conditions for the positive of all its elements are indicated.