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Full-Text Articles in Physical Sciences and Mathematics
Predicting Insulin Pump Therapy Settings, Riccardo L. Ferraro, David Grijalva, Alex Trahan
Predicting Insulin Pump Therapy Settings, Riccardo L. Ferraro, David Grijalva, Alex Trahan
SMU Data Science Review
Millions of people live with diabetes worldwide [7]. To mitigate some of the many symptoms associated with diabetes, an estimated 350,000 people in the United States rely on insulin pumps [17]. For many of these people, how effectively their insulin pump performs is the difference between sleeping through the night and a life threatening emergency treatment at a hospital. Three programmed insulin pump therapy settings governing effective insulin pump function are: Basal Rate (BR), Insulin Sensitivity Factor (ISF), and Carbohydrate Ratio (ICR). For many people using insulin pumps, these therapy settings are often not correct, given their physiological needs. While …
(R1239) A New Type Ii Half Logistic-G Family Of Distributions With Properties, Regression Models, System Reliability And Applications, Emrah Altun, Morad Alizadeh, Haitham M. Yousof, Mahdi Rasekhi, G. G. Hamedani
(R1239) A New Type Ii Half Logistic-G Family Of Distributions With Properties, Regression Models, System Reliability And Applications, Emrah Altun, Morad Alizadeh, Haitham M. Yousof, Mahdi Rasekhi, G. G. Hamedani
Applications and Applied Mathematics: An International Journal (AAM)
This study proposes a new family of distributions based on the half logistic distribution. With the new family, the baseline distributions gain flexibility through additional shape parameters. The important statistical properties of the proposed family are derived. A new generalization of the Weibull distribution is used to introduce a location-scale regression model for the censored response variable. The utility of the introduced models is demonstrated in survival analysis and estimation of the system reliability. Three data sets are analyzed. According to the empirical results, it is observed that the proposed family gives better results than other existing models.
Introduction To Research Statistical Analysis: An Overview Of The Basics, Christian Vandever
Introduction To Research Statistical Analysis: An Overview Of The Basics, Christian Vandever
HCA Healthcare Journal of Medicine
This article covers many statistical ideas essential to research statistical analysis. Sample size is explained through the concepts of statistical significance level and power. Variable types and definitions are included to clarify necessities for how the analysis will be interpreted. Categorical and quantitative variable types are defined, as well as response and predictor variables. Statistical tests described include t-tests, ANOVA and chi-square tests. Multiple regression is also explored for both logistic and linear regression. Finally, the most common statistics produced by these methods are explored.
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).
Visualization And Machine Learning Techniques For Nasa’S Em-1 Big Data Problem, Antonio P. Garza Iii, Jose Quinonez, Misael Santana, Nibhrat Lohia
Visualization And Machine Learning Techniques For Nasa’S Em-1 Big Data Problem, Antonio P. Garza Iii, Jose Quinonez, Misael Santana, Nibhrat Lohia
SMU Data Science Review
In this paper, we help NASA solve three Exploration Mission-1 (EM-1) challenges: data storage, computation time, and visualization of complex data. NASA is studying one year of trajectory data to determine available launch opportunities (about 90TBs of data). We improve data storage by introducing a cloud-based solution that provides elasticity and server upgrades. This migration will save $120k in infrastructure costs every four years, and potentially avoid schedule slips. Additionally, it increases computational efficiency by 125%. We further enhance computation via machine learning techniques that use the classic orbital elements to predict valid trajectories. Our machine learning model decreases trajectory …
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.
Yelp’S Review Filtering Algorithm, Yao Yao, Ivelin Angelov, Jack Rasmus-Vorrath, Mooyoung Lee, Daniel W. Engels
Yelp’S Review Filtering Algorithm, Yao Yao, Ivelin Angelov, Jack Rasmus-Vorrath, Mooyoung Lee, Daniel W. Engels
SMU Data Science Review
In this paper, we present an analysis of features influencing Yelp's proprietary review filtering algorithm. Classifying or misclassifying reviews as recommended or non-recommended affects average ratings, consumer decisions, and ultimately, business revenue. Our analysis involves systematically sampling and scraping Yelp restaurant reviews. Features are extracted from review metadata and engineered from metrics and scores generated using text classifiers and sentiment analysis. The coefficients of a multivariate logistic regression model were interpreted as quantifications of the relative importance of features in classifying reviews as recommended or non-recommended. The model classified review recommendations with an accuracy of 78%. We found that reviews …
Fuel Flow Reduction Impact Analysis Of Drag Reducing Film Applied To Aircraft Wings, Damon Resnick, Chris Donlan, Nimish Sakalle, Cody Pinkerman
Fuel Flow Reduction Impact Analysis Of Drag Reducing Film Applied To Aircraft Wings, Damon Resnick, Chris Donlan, Nimish Sakalle, Cody Pinkerman
SMU Data Science Review
In this paper, we present an analysis of flight data in order to determine whether the application of the Edge Aerodynamix Conformal Vortex Generator (CVG), applied to the wings of aircraft, reduces fuel flow during cruising conditions of flight. The CVG is a special treatment and film applied to the wings of an aircraft to protect the wings and reduce the non-laminar flow of air around the wings during flight. It is thought that by reducing the non-laminar flow or vortices around and directly behind the wings that an aircraft will move more smoothly through the air and provide a …
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.
A Likelihood Ratio Test Approach To Profile Monitoring In Tourism Industry, R. Noorossana, H. Izadbakhsh, M. R. Nayebpour
A Likelihood Ratio Test Approach To Profile Monitoring In Tourism Industry, R. Noorossana, H. Izadbakhsh, M. R. Nayebpour
Applications and Applied Mathematics: An International Journal (AAM)
A new statistical profile monitoring technique to monitor and detect changes in logistic profiles with an application in the tourism industry is presented in this paper. In the statistical process control literature, profile is usually referred to as a relationship between a response variable and one or more explanatory variables. In the tourism case study presented in this paper, time is considered as the explanatory variable and tourism satisfaction as the response variable. The Likelihood ratio test is used as a vehicle to detect any changes in the satisfaction profile in phase II of profile monitoring. The performance of 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 …