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Articles 1 - 13 of 13
Full-Text Articles in Physical Sciences and Mathematics
The Short-Term Effects Of Fine Airborne Particulate Matter And Climate On Covid-19 Disease Dynamics, El Hussain Shamsa, Kezhong Zhang
The Short-Term Effects Of Fine Airborne Particulate Matter And Climate On Covid-19 Disease Dynamics, El Hussain Shamsa, Kezhong Zhang
Medical Student Research Symposium
Background: Despite more than 60% of the United States population being fully vaccinated, COVID-19 cases continue to spike in a temporal pattern. These patterns in COVID-19 incidence and mortality may be linked to short-term changes in environmental factors.
Methods: Nationwide, county-wise measurements for COVID-19 cases and deaths, fine-airborne particulate matter (PM2.5), and maximum temperature were obtained from March 20, 2020 to March 20, 2021. Multivariate Linear Regression was used to analyze the association between environmental factors and COVID-19 incidence and mortality rates in each season. Negative Binomial Regression was used to analyze daily fluctuations of COVID-19 cases …
An Exploration Of Link Functions Used In Ordinal Regression, Thomas J. Smith, David A. Walker, Cornelius M. Mckenna
An Exploration Of Link Functions Used In Ordinal Regression, Thomas J. Smith, David A. Walker, Cornelius M. Mckenna
Journal of Modern Applied Statistical Methods
The purpose of this study is to examine issues involved with choice of a link function in generalized linear models with ordinal outcomes, including distributional appropriateness, link specificity, and palindromic invariance are discussed and an exemplar analysis provided using the Pew Research Center 25th anniversary of the Web Omnibus Survey data. Simulated data are used to compare the relative palindromic invariance of four distinct indices of determination/discrimination, including a newly proposed index by Smith et al. (2017).
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.
Effective Estimation Strategy Of Finite Population Variance Using Multi-Auxiliary Variables In Double Sampling, Reba Maji, G. N. Singh, Arnab Bandyopadhyay
Effective Estimation Strategy Of Finite Population Variance Using Multi-Auxiliary Variables In Double Sampling, Reba Maji, G. N. Singh, Arnab Bandyopadhyay
Journal of Modern Applied Statistical Methods
Estimation of population variance in two-phase (double) sampling is considered using information on multiple auxiliary variables. An unbiased estimator is proposed and its properties are studied under two different structures. The superiority of the suggested estimator over some contemporary estimators of population variance was established through empirical studies from a natural and an artificially generated dataset.
Efficient And Unbiased Estimation Procedure Of Population Mean In Two-Phase Sampling, Reba Maji, Arnab Bandyopadhyay, G. N. Singh
Efficient And Unbiased Estimation Procedure Of Population Mean In Two-Phase Sampling, Reba Maji, Arnab Bandyopadhyay, G. N. Singh
Journal of Modern Applied Statistical Methods
In this paper, an unbiased regression-ratio type estimator has been developed for estimating the population mean using two auxiliary variables in double sampling. Its properties are studied under two different cases. Empirical studies and graphical simulation have been done to demonstrate the efficiency of the proposed estimator over other estimators.
A Spatial Analytical Framework For Examining Road Traffic Crashes, Grace O. Korter
A Spatial Analytical Framework For Examining Road Traffic Crashes, Grace O. Korter
Journal of Modern Applied Statistical Methods
A number of different modeling techniques have been used to examine road traffic crashes for analytic and predictive purposes. Map-based spatial analysis is introduced. Applications are given which show the power in a combination of existing exploratory and statistical methods.
Contrails: Causal Inference Using Propensity Scores, Dean S. Barron
Contrails: Causal Inference Using Propensity Scores, Dean S. Barron
Journal of Modern Applied Statistical Methods
Contrails are clouds caused by airplane exhausts, which geologists contend decrease daily temperature ranges on Earth. Following the 2001 World Trade Center attack, cancelled domestic flights triggered the first absence of contrails in decades. Resultant exceptional data capacitated causal inference analysis by propensity score matching. Estimated contrail effect was 6.8981°F.
Robust Regression Methods For Massively Decayed Intelligence Data, Akiva Joachim Lorenz
Robust Regression Methods For Massively Decayed Intelligence Data, Akiva Joachim Lorenz
Wayne State University Dissertations
Homeland Security, sponsored by governmental initiatives, has become a vibrant academic research field. However, most efforts were placed with the recognition of threats (e.g. theory) and response options. Less effort was placed in the analysis of the collected data through statistical modeling. In a field that collects more than 20 terabyte of information per minute though diverse overt and covert means and indexes it for future research, understanding how different statistical models behave when it comes to massively decayed data is of vital importance.
Using Monte Carlo methods, three regression techniques (ordinary least squares, least-trimmed, and maximum likelihood) were tested …
Regression Models For Readmission Prediction Using Electronic Medical Records, Yudi Niu
Regression Models For Readmission Prediction Using Electronic Medical Records, Yudi Niu
Wayne State University Theses
Hospital readmissions are not only expensive but are also potentially harmful, and most importantly, they are often preventable. Providing special care for a targeted group of patients who are at a high risk of readmission can significantly improve the chances of avoiding rehospitalization. Despite the significance of this problem, not many researchers have thoroughly investigated it due to the inherent complexities involved in analyzing and estimating the inherent predictive power of such complex hospitalization records. In this thesis, we propose using support vector machines and survival analysis methods to analyze data collected from Electronic Medical Records (EMR). We define the …
Improved Estimator In The Presence Of Multicollinearity, Ghadban Khalaf
Improved Estimator In The Presence Of Multicollinearity, Ghadban Khalaf
Journal of Modern Applied Statistical Methods
The performances of two biased estimators for the general linear regression model under conditions of collinearity are examined and a new proposed ridge parameter is introduced. Using Mean Square Error (MSE) and Monte Carlo simulation, the resulting estimator’s performance is evaluated and compared with the Ordinary Least Square (OLS) estimator and the Hoerl and Kennard (1970a) estimator. Results of the simulation study indicate that, with respect to MSE criteria, in all cases investigated the proposed estimator outperforms both the OLS and the Hoerl and Kennard estimators.
Number Of Replications Required In Monte Carlo Simulation Studies: A Synthesis Of Four Studies, Daniel J. Mundform, Jay Schaffer, Myoung-Jin Kim, Dale Shaw, Ampai Thongteeraparp, Pornsin Supawan
Number Of Replications Required In Monte Carlo Simulation Studies: A Synthesis Of Four Studies, Daniel J. Mundform, Jay Schaffer, Myoung-Jin Kim, Dale Shaw, Ampai Thongteeraparp, Pornsin Supawan
Journal of Modern Applied Statistical Methods
Monte Carlo simulations are used extensively to study the performance of statistical tests and control charts. Researchers have used various numbers of replications, but rarely provide justification for their choice. Currently, no empirically-based recommendations regarding the required number of replications exist. Twenty-two studies were re-analyzed to determine empirically-based recommendations.
Least Squares Percentage Regression, Chris Tofallis
Least Squares Percentage Regression, Chris Tofallis
Journal of Modern Applied Statistical Methods
In prediction, the percentage error is often felt to be more meaningful than the absolute error. We therefore extend the method of least squares to deal with percentage errors, for both simple and multiple regression. Exact expressions are derived for the coefficients, and we show how such models can be estimated using standard software. When the relative error is normally distributed, least squares percentage regression is shown to provide maximum likelihood estimates. The multiplicative error model is linked to least squares percentage regression in the same way that the standard additive error model is linked to ordinary least squares regression.
Regression By Data Segments Via Discriminant Analysis, Stan Lipovetsky, Michael Conklin
Regression By Data Segments Via Discriminant Analysis, Stan Lipovetsky, Michael Conklin
Journal of Modern Applied Statistical Methods
It is known that two-group linear discriminant function can be constructed via binary regression. In this article, it is shown that the opposite relation is also relevant – it is possible to present multiple regression as a linear combination of a main part, based on the pooled variance, and Fisher discriminators by data segments. Presenting regression as an aggregate of the discriminators allows one to decompose coefficients of the model into sum of several vectors related to segments. Using this technique provides an understanding of how the total regression model is composed of the regressions by the segments with possible …