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Articles 91 - 120 of 1162
Full-Text Articles in Statistics and Probability
Dynamic Conditional Correlation Garch: A Multivariate Time Series Novel Using A Bayesian Approach, Diego Nascimento, Cleber Xavier, Israel Felipe, Francisco Louzada Neto
Dynamic Conditional Correlation Garch: A Multivariate Time Series Novel Using A Bayesian Approach, Diego Nascimento, Cleber Xavier, Israel Felipe, Francisco Louzada Neto
Journal of Modern Applied Statistical Methods
The Dynamic Conditional Correlation GARCH (DCC-GARCH) mutation model is considered using a Monte Carlo approach via Markov chains in the estimation of parameters, time-dependence variation is visually demonstrated. Fifteen indices were analyzed from the main financial markets of developed and developing countries from different continents. The performances of indices are similar, with a joint evolution. Most index returns, especially SPX and NDX, evolve over time with a higher positive correlation.
Regression When There Are Two Covariates: Some Practical Reasons For Considering Quantile Grids, Rand Wilcox
Regression When There Are Two Covariates: Some Practical Reasons For Considering Quantile Grids, Rand Wilcox
Journal of Modern Applied Statistical Methods
When dealing with the association between some random variable and two covariates, extensive experience with smoothers indicates that often a linear model poorly reflects the nature of the association. A simple approach via quantile grids that reflects the nature of the association is given. The two main goals are to illustrate this approach can make a practical difference, and to describe R functions for applying it. Included are comments on dealing with more than two covariates.
Bivariate Analogs Of The Wilcoxon–Mann–Whitney Test And The Patel–Hoel Method For Interactions, Rand Wilcox
Bivariate Analogs Of The Wilcoxon–Mann–Whitney Test And The Patel–Hoel Method For Interactions, Rand Wilcox
Journal of Modern Applied Statistical Methods
A fundamental way of characterizing how two independent compares compare is in terms of the probability that a randomly sampled observation from the first group is less than a randomly sampled observation from the second group. The paper suggests a bivariate analog and investigates methods for computing confidence intervals. An interaction for a two-by-two design is investigated as well.
Assessing The Accuracy Of Approximate Confidence Intervals Proposed For The Mean Of Poisson Distribution, Alireza Shirvani, Malek Fathizadeh
Assessing The Accuracy Of Approximate Confidence Intervals Proposed For The Mean Of Poisson Distribution, Alireza Shirvani, Malek Fathizadeh
Journal of Modern Applied Statistical Methods
The Poisson distribution is applied as an appropriate standard model to analyze count data. Because this distribution is known as a discrete distribution, representation of accurate confidence intervals for its distribution mean is extremely difficult. Approximate confidence intervals were presented for the Poisson distribution mean. The purpose of this study is to simultaneously compare several confidence intervals presented, according to the average coverage probability and accurate confidence coefficient and the average confidence interval length criteria.
Analytical Closed-Form Solution For General Factor With Many Variables, Stan Lipovetsky, Vladimir Manewitsch
Analytical Closed-Form Solution For General Factor With Many Variables, Stan Lipovetsky, Vladimir Manewitsch
Journal of Modern Applied Statistical Methods
The factor analytic triad method of one-factor solution gives the explicit analytical form for a common latent factor built by three variables. The current work considers analytical presentation of a general latent factor constructed in a closed-form solution for multivariate case. The results can be supportive to theoretical description and practical application of latent variable modeling, especially for big data because the analytical closed-form solution is not prone to data dimensionality.
Regression Modeling And Prediction By Individual Observations Versus Frequency, Stan Lipovetsky
Regression Modeling And Prediction By Individual Observations Versus Frequency, Stan Lipovetsky
Journal of Modern Applied Statistical Methods
A regression model built by a dataset could sometimes demonstrate a low quality of fit and poor predictions of individual observations. However, using the frequencies of possible combinations of the predictors and the outcome, the same models with the same parameters may yield a high quality of fit and precise predictions for the frequencies of the outcome occurrence. Linear and logistical regressions are used to make an explicit exposition of the results of regression modeling and prediction.
Nutrition And Health Status Of Hemodialysis Patients In Dhaka, Bangladesh, Tanjina Rahman
Nutrition And Health Status Of Hemodialysis Patients In Dhaka, Bangladesh, Tanjina Rahman
Wayne State University Dissertations
Methods to identify patients at risk for End stage renal disease (ESRD) are a high priority in Bangladesh, where kidney transplants/dialysis options are limited and costly. Every year, 35,000 to 40,000 people reach ESRD in Bangladesh, but currently available facilities can hardly accommodate only 9000 to 10,000 new patients with twice weekly dialysis and the remaining 66% have no access to any kind of renal replacement therapy (RRT) in the form of dialysis or transplantation. Nutrition is an important factor in maintaining good health of hemodialysis patients. However, data on nutritional status of Bangladeshi dialysis patients is limited and is …
Economic Design Of Acceptance Sampling Plans For Truncated Life Tests Using Three-Parameter Lindley Distribution, Amer Ibrahim Al-Omari, Enrico Ciavolino, Amjad D. Al-Nasser
Economic Design Of Acceptance Sampling Plans For Truncated Life Tests Using Three-Parameter Lindley Distribution, Amer Ibrahim Al-Omari, Enrico Ciavolino, Amjad D. Al-Nasser
Journal of Modern Applied Statistical Methods
A single acceptance sampling plan for the three-parameter Lindley distribution under a truncated life test is developed. For various consumer’s confidence levels, acceptance numbers, and values of the ratio of the experimental time to the specified average lifetime, the minimum sample size important to assert a certain average lifetime are calculated. The operating characteristic (OC) function values as well as the associated producer’s risks are also provided. A numerical example is presented to illustrate the suggested acceptance sampling plans.
The Estimation Of Missing Values In Rectangular Lattice Designs, Emmanuel Ogochukwu Ossai, Abimibola Victoria Oladugba
The Estimation Of Missing Values In Rectangular Lattice Designs, Emmanuel Ogochukwu Ossai, Abimibola Victoria Oladugba
Journal of Modern Applied Statistical Methods
Algebraic expressions for estimating missing data when one or more observation(s) are missing in Rectangular lattice designs with repetition were derived using the method of minimizing the residual sum of squares. Results showed that the estimated value(s) were significantly approximate to that of the actual value(s).
Measure Of Departure From Marginal Average Point-Symmetry For Two-Way Contingency Tables, Kiyotaka Iki, Sadao Tomizawa
Measure Of Departure From Marginal Average Point-Symmetry For Two-Way Contingency Tables, Kiyotaka Iki, Sadao Tomizawa
Journal of Modern Applied Statistical Methods
For the analysis of two-way contingency tables with ordered categories, Yamamoto, Tahata, Suzuki, and Tomizawa (2011) considered a measure to represent the degree of departure from marginal point-symmetry. The maximum value of the measure cannot distinguish two kinds of marginal complete asymmetry with respect to the midpoint. A measure is proposed which can distinguish two kinds of marginal asymmetry with respect to the midpoint. It also gives large-sample confidence interval for the proposed measure.
The Impact Of Equating On Detection Of Treatment Effects, Youn-Jeng Choi, Seohyun Kim, Allan S. Cohen, Zhenqiu Lu
The Impact Of Equating On Detection Of Treatment Effects, Youn-Jeng Choi, Seohyun Kim, Allan S. Cohen, Zhenqiu Lu
Journal of Modern Applied Statistical Methods
Equating makes it possible to compare performances on different forms of a test. Three different equating methods (baseline selection, subgroup, and subscore equating) using common-item item response theory equating were examined for their impact on detection of treatment effects in multilevel models.
Upper Record Values From Extended Exponential Distribution, Devendra Kumar, Sanku Dey
Upper Record Values From Extended Exponential Distribution, Devendra Kumar, Sanku Dey
Journal of Modern Applied Statistical Methods
Some recurrence relations are established for the single and product moments of upper record values for the extended exponential distribution by Nadarajah and Haghighi (2011) as an alternative to the gamma, Weibull, and the exponentiated exponential distributions. Recurrence relations for negative moments and quotient moments of upper record values are also obtained. Using relations of single moments and product moments, means, variances, and covariances of upper record values from samples of sizes up to 10 are tabulated for various values of the shape parameter and scale parameter. A characterization of this distribution based on conditional moments of record …
The Andersen Likelihood Ratio Test With A Random Split Criterion Lacks Power, Georg Krammer
The Andersen Likelihood Ratio Test With A Random Split Criterion Lacks Power, Georg Krammer
Journal of Modern Applied Statistical Methods
The Andersen LRT uses sample characteristics as split criteria to evaluate Rasch model fit, or theory driven hypothesis testing for a test. The power and Type I error of a random split criterion was evaluated with a simulation study. Results consistently show a random split criterion lacks power.
Weighted Version Of Generalized Inverse Weibull Distribution, Sofi Mudiasir, S. P. Ahmad
Weighted Version Of Generalized Inverse Weibull Distribution, Sofi Mudiasir, S. P. Ahmad
Journal of Modern Applied Statistical Methods
Weighted distributions are used in many fields, such as medicine, ecology, and reliability. A weighted version of the generalized inverse Weibull distribution, known as weighted generalized inverse Weibull distribution (WGIWD), is proposed. Basic properties including mode, moments, moment generating function, skewness, kurtosis, and Shannon’s entropy are studied. The usefulness of the new model was demonstrated by applying it to a real-life data set. The WGIWD fits better than its submodels, such as length biased generalized inverse Weibull (LGIW), generalized inverse Weibull (GIW), inverse Weibull (IW) and inverse exponential (IE) distributions.
Calibration Of Measurements, Edward Kroc, Bruno D. Zumbo
Calibration Of Measurements, Edward Kroc, Bruno D. Zumbo
Journal of Modern Applied Statistical Methods
Traditional notions of measurement error typically rely on a strong mean-zero assumption on the expectation of the errors conditional on an unobservable “true score” (classical measurement error) or on the data themselves (Berkson measurement error). Weakly calibrated measurements for an unobservable true quantity are defined based on a weaker mean-zero assumption, giving rise to a measurement model of differential error. Applications show it retains many attractive features of estimation and inference when performing a naive data analysis (i.e. when performing an analysis on the error-prone measurements themselves), and other interesting properties not present in the classical or Berkson cases. Applied …
Estimation Of Mean With Two-Parameter Ratio-Product-Ratio Estimator In Double Sampling Using Ancillary Information Under Non-Response, Surya K. Pal, Housila P. Singh
Estimation Of Mean With Two-Parameter Ratio-Product-Ratio Estimator In Double Sampling Using Ancillary Information Under Non-Response, Surya K. Pal, Housila P. Singh
Journal of Modern Applied Statistical Methods
Ratio-product-ratio estimators with two parameters in double sampling under non-response are considered along with their properties. Practical conditions are obtained in which the suggested estimators are more proficient than other existing estimators. An example is given.
Efficient Class Of Estimators For Finite Population Mean Using Auxiliary Information In Two-Occasion Successive Sampling, G. N. Singh, Mohd Khalid
Efficient Class Of Estimators For Finite Population Mean Using Auxiliary Information In Two-Occasion Successive Sampling, G. N. Singh, Mohd Khalid
Journal of Modern Applied Statistical Methods
In the case of sampling on two occasions, a class of estimators is considered which uses information on the first occasion as well as the second occasion in order to estimate the population means on the current (second) occasion. The usefulness of auxiliary information in enhancing the efficiency of this estimation is examined through the class of proposed estimators. Some properties of the class of estimators and a strategy of optimum replacement are discussed. The proposed class of estimators were empirically compared with the sample mean estimator in the case of no matching. The established optimum estimator, which is a …
Jmasm 51: Bayesian Reliability Analysis Of Binomial Model – Application To Success/Failure Data, M. Tanwir Akhtar, Athar Ali Khan
Jmasm 51: Bayesian Reliability Analysis Of Binomial Model – Application To Success/Failure Data, M. Tanwir Akhtar, Athar Ali Khan
Journal of Modern Applied Statistical Methods
Reliability data are generated in the form of success/failure. An attempt was made to model such type of data using binomial distribution in the Bayesian paradigm. For fitting the Bayesian model both analytic and simulation techniques are used. Laplace approximation was implemented for approximating posterior densities of the model parameters. Parallel simulation tools were implemented with an extensive use of R and JAGS. R and JAGS code are developed and provided. Real data sets are used for the purpose of illustration.
A Random Forests Approach To Assess Determinants Of Central Bank Independence, Maddalena Cavicchioli, Angeliki Papana, Ariadni Papana Dagiasis, Barbara Pistoresi
A Random Forests Approach To Assess Determinants Of Central Bank Independence, Maddalena Cavicchioli, Angeliki Papana, Ariadni Papana Dagiasis, Barbara Pistoresi
Journal of Modern Applied Statistical Methods
A non-parametric efficient statistical method, Random Forests, is implemented for the selection of the determinants of Central Bank Independence (CBI) among a large database of economic, political, and institutional variables for OECD countries. It permits ranking all the determinants based on their importance in respect to the CBI and does not impose a priori assumptions on potential nonlinear relationships in the data. Collinearity issues are resolved, because correlated variables can be simultaneously considered.
Maximum Likelihood Estimation For The Generalized Pareto Distribution And Goodness-Of-Fit Test With Censored Data, Minh H. Pham, Chris Tsokos, Bong-Jin Choi
Maximum Likelihood Estimation For The Generalized Pareto Distribution And Goodness-Of-Fit Test With Censored Data, Minh H. Pham, Chris Tsokos, Bong-Jin Choi
Journal of Modern Applied Statistical Methods
The generalized Pareto distribution (GPD) is a flexible parametric model commonly used in financial modeling. Maximum likelihood estimation (MLE) of the GPD was proposed by Grimshaw (1993). Maximum likelihood estimation of the GPD for censored data is developed, and a goodness-of-fit test is constructed to verify an MLE algorithm in R and to support the model-validation step. The algorithms were composed in R. Grimshaw’s algorithm outperforms functions available in the R package ‘gPdtest’. A simulation study showed the MLE method for censored data and the goodness-of-fit test are both reliable.
Bayesian Approximation Techniques For Scale Parameter Of Laplace Distribution, Uzma Jan, S. P. Ahmad
Bayesian Approximation Techniques For Scale Parameter Of Laplace Distribution, Uzma Jan, S. P. Ahmad
Journal of Modern Applied Statistical Methods
The Bayesian estimation of the scale parameter of a Laplace Distribution is obtained using two approximation techniques, like Normal approximation and Tierney and Kadane (T-K) approximation, under different informative priors.
Can One Test Fit All? Responses To The Article “Striving For Simple But Effective Advice For Comparing The Central Tendency Of Two Populations” (Ruxton & Neuhäuser, 2018), Diep Nguyen, Eun Sook Kim, Yi-Hsin Chen
Can One Test Fit All? Responses To The Article “Striving For Simple But Effective Advice For Comparing The Central Tendency Of Two Populations” (Ruxton & Neuhäuser, 2018), Diep Nguyen, Eun Sook Kim, Yi-Hsin Chen
Journal of Modern Applied Statistical Methods
Responses to suggestions made by Ruxton & Neuhäuser (2018) regarding Nguyen et al. (2016) are given.
On The Conditional And Unconditional Type I Error Rates And Power Of Tests In Linear Models With Heteroscedastic Errors, Patrick J. Rosopa, Alice M. Brawley, Theresa P. Atkinson, Stephen A. Robertson
On The Conditional And Unconditional Type I Error Rates And Power Of Tests In Linear Models With Heteroscedastic Errors, Patrick J. Rosopa, Alice M. Brawley, Theresa P. Atkinson, Stephen A. Robertson
Journal of Modern Applied Statistical Methods
Preliminary tests for homoscedasticity may be unnecessary in general linear models. Based on Monte Carlo simulations, results suggest that when testing for differences between independent slopes, the unconditional use of weighted least squares regression and HC4 regression performed the best across a wide range of conditions.
Φ-Divergence Loss-Based Artificial Neural Network, R. L. Salamwade, D. M. Sakate, S. K. Mathur
Φ-Divergence Loss-Based Artificial Neural Network, R. L. Salamwade, D. M. Sakate, S. K. Mathur
Journal of Modern Applied Statistical Methods
Artificial Neural Networks (ANNs) can fit non-linear functions and recognize patterns better than several standard techniques. Performance of ANNs is measured by using loss functions. Phi-divergence estimator is generalization of maximum likelihood estimator and it possesses all its properties. A neural network is proposed which is trained using phi-divergence loss.
Robust Ancova, Curvature, And The Curse Of Dimensionality, Rand Wilcox
Robust Ancova, Curvature, And The Curse Of Dimensionality, Rand Wilcox
Journal of Modern Applied Statistical Methods
There is a substantial collection of robust analysis of covariance (ANCOVA) methods that effectively deals with non-normality, unequal population slope parameters, outliers, and heteroscedasticity. Some are based on the usual linear model and others are based on smoothers (nonparametric regression estimators). However, extant results are limited to one or two covariates. A minor goal here is to extend a recently-proposed method, based on the usual linear model, to situations where there are up to six covariates. The usual linear model might provide a poor approximation of the true regression surface. The main goal is to suggest a method, based on …
A Strategy For Using Bias And Rmse As Outcomes In Monte Carlo Studies In Statistics, Michael Harwell
A Strategy For Using Bias And Rmse As Outcomes In Monte Carlo Studies In Statistics, Michael Harwell
Journal of Modern Applied Statistical Methods
To help ensure important patterns of bias and accuracy are detected in Monte Carlo studies in statistics this paper proposes conditioning bias and root mean square error (RMSE) measures on estimated Type I and Type II error rates. A small Monte Carlo study is used to illustrate this argument.
A Robust Nonparametric Measure Of Effect Size Based On An Analog Of Cohen's D, Plus Inferences About The Median Of The Typical Difference, Rand Wilcox
Journal of Modern Applied Statistical Methods
The paper describes a nonparametric analog of Cohen's d, Q. It is established that a confidence interval for Q can be computed via a method for computing a confidence interval for the median of D = X1 − X2, which in turn is related to making inferences about P(X1 < X2).
Should We Give Up On Causality?, Tom Knapp
Should We Give Up On Causality?, Tom Knapp
Journal of Modern Applied Statistical Methods
No abstract provided.
Striving For Simple But Effective Advice For Comparing The Central Tendency Of Two Populations, Graeme Ruxton, Markus Neuhäuser
Striving For Simple But Effective Advice For Comparing The Central Tendency Of Two Populations, Graeme Ruxton, Markus Neuhäuser
Journal of Modern Applied Statistical Methods
Nguyen et al. (2016) offered advice to researchers in the commonly-encountered situation where they are interested in testing for a difference in central tendency between two populations. Their data and the available literature support very simple advice that strikes the best balance between ease of implementation, power and reliability. Specifically, apply Satterthwaite’s test, with preliminary ranking of the data if a strong deviation from normality is expected, or is suggested by visual inspection of the data. This simple guideline will serve well except when dealing with small samples of discrete data, when more sophisticated treatment may be required.
Logistic Regression: An Inferential Method For Identifying The Best Predictors, Rand Wilcox
Logistic Regression: An Inferential Method For Identifying The Best Predictors, Rand Wilcox
Journal of Modern Applied Statistical Methods
When dealing with a logistic regression model, there is a simple method for estimating the strength of the association between the jth covariate and the dependent variable when all covariates are entered into the model. There is the issue of determining whether the jth independent variable has a stronger or weaker association than the kth independent variable. This note describes a method for dealing with this issue that was found to perform reasonably well in simulations.