Penalized Nonparametric Scalar-On-Function Regression Via Principal Coordinates, 2016 New York University School of Medicine
Penalized Nonparametric Scalar-On-Function Regression Via Principal Coordinates, Philip T. Reiss, David L. Miller, Pei-Shien Wu, Wen-Yu Hua
Philip T. Reiss
A Multi-Indexed Logistic Model For Time Series, 2016 East Tennessee State University
A Multi-Indexed Logistic Model For Time Series, Xiang Liu
Electronic Theses and Dissertations
In this thesis, we explore a multi-indexed logistic regression (MILR) model, with particular emphasis given to its application to time series. MILR includes simple logistic regression (SLR) as a special case, and the hope is that it will in some instances also produce significantly better results. To motivate the development of MILR, we consider its application to the analysis of both simulated sine wave data and stock data. We looked at well-studied SLR and its application in the analysis of time series data. Using a more sophisticated representation of sequential data, we then detail the implementation of MILR. We compare ...
The Reduced Form Of Litigation Models And The Plaintiff's Win Rate, 2016 University of Pennsylvania Law School
The Reduced Form Of Litigation Models And The Plaintiff's Win Rate, Jonah B. Gelbach
In this paper I introduce what I call the reduced form approach to studying the plaintiff's win rate in litigation selection models. A reduced form comprises a joint distribution of plaintiff's and defendant's beliefs concerning the probability that the plaintiff would win in the event a dispute were litigated; a conditional win rate function that tells us the actual probability of a plaintiff win in the event of litigation, given the parties' subjective beliefs; and a litigation rule that provides the probability that a case will be litigated given the two parties' beliefs. I show how models ...
Addition To Pglr Chap 6, 2016 Arizona State University
Addition To Pglr Chap 6, Joseph M. Hilbe
Joseph M Hilbe
Passive Visual Analytics Of Social Media Data For Detection Of Unusual Events, 2016 Purdue University
Passive Visual Analytics Of Social Media Data For Detection Of Unusual Events, Kush Rustagi, Junghoon Chae
The Summer Undergraduate Research Fellowship (SURF) Symposium
Now that social media sites have gained substantial traction, huge amounts of un-analyzed valuable data are being generated. Posts containing images and text have spatiotemporal data attached as well, having immense value for increasing situational awareness of local events, providing insights for investigations and understanding the extent of incidents, their severity, and consequences, as well as their time-evolving nature. However, the large volume of unstructured social media data hinders exploration and examination. To analyze such social media data, the S.M.A.R.T system provides the analyst with an interactive visual spatiotemporal analysis and spatial decision support environment that ...
Newsvendor Models With Monte Carlo Sampling, 2016 East Tennessee State University
Newsvendor Models With Monte Carlo Sampling, Ijeoma W. Ekwegh
Electronic Theses and Dissertations
Newsvendor Models with Monte Carlo Sampling by Ijeoma Winifred Ekwegh The newsvendor model is used in solving inventory problems in which demand is random. In this thesis, we will focus on a method of using Monte Carlo sampling to estimate the order quantity that will either maximizes revenue or minimizes cost given that demand is uncertain. Given data, the Monte Carlo approach will be used in sampling data over scenarios and also estimating the probability density function. A bootstrapping process yields an empirical distribution for the order quantity that will maximize the expected proﬁt. Finally, this method will be used ...
Multilevel Models For Longitudinal Data, 2016 East Tennessee State University
Multilevel Models For Longitudinal Data, Aastha Khatiwada
Electronic Theses and Dissertations
Longitudinal data arise when individuals are measured several times during an ob- servation period and thus the data for each individual are not independent. There are several ways of analyzing longitudinal data when different treatments are com- pared. Multilevel models are used to analyze data that are clustered in some way. In this work, multilevel models are used to analyze longitudinal data from a case study. Results from other more commonly used methods are compared to multilevel models. Also, comparison in output between two software, SAS and R, is done. Finally a method consisting of fitting individual models for each ...
The Influence Of The Electric Supply Industry On Economic Growth In Less Developed Countries, 2016 University of Southern Mississippi
The Influence Of The Electric Supply Industry On Economic Growth In Less Developed Countries, Edward Richard Bee
This study measures the impact that electrical outages have on manufacturing production in 135 less developed countries using stochastic frontier analysis and data from World Bank’s Investment Climate surveys. Outages of electricity, for firms with and without backup power sources, are the most frequently cited constraint on manufacturing growth in these surveys.
Outages are shown to reduce output below the production frontier by almost five percent in Africa and by a lower percentage in South Asia, Southeast Asia and the Middle East and North Africa. Production response to outages is quadratic in form. Outages also increase labor cost, reduce ...
Well I'Ll Be Damned - Insights Into Predictive Value Of Pedigree Information In Horse Racing, 2016 University of Southampton
Well I'Ll Be Damned - Insights Into Predictive Value Of Pedigree Information In Horse Racing, Timothy Baker Mr, Ming-Chien Sung, Johnnie Johnson Professor, Tiejun Ma
International Conference on Gambling and Risk Taking
Fundamental form characteristics like how fast a horse ran at its last start, are widely used to help predict the outcome of horse racing events. The exception being in races where horses haven’t previously competed, such as Maiden races, where there is little or no publicly available past performance information. In these types of events bettors need only consider a simplified suite of factors however this is offset by a higher level of uncertainty. This paper examines the inherent information content embedded within a horse’s ancestry and the extent to which this information is discounted in the United ...
Examining Cost Functionality And Optimization: A Case Study On Testing The Reasonableness Of New Aircraft Using Historical Aircraft Data, 2016 Washington University in St. Louis
Examining Cost Functionality And Optimization: A Case Study On Testing The Reasonableness Of New Aircraft Using Historical Aircraft Data, Katherine Jozefiak
Arts & Sciences Electronic Theses and Dissertations
When pursuing business by competing for government contracts, proving the submitted price is reasonable is often required. This proof is called a test of reasonableness. This study analyzes data from historical aircraft programs in relation of a new aircraft program in order to demonstrate the estimated cost of the new program is reasonable. The purpose of this study is to investigate three questions. Is the new program cost reasonable using current industry and government parameters? Is it better to look at programs from a total cost perspective or break the total cost into subcategory levels? Finally, this study applies a ...
Automated Sea State Classification From Parameterization Of Survey Observations And Wave-Generated Displacement Data, 2016 University of New Orleans, New Orleans
Automated Sea State Classification From Parameterization Of Survey Observations And Wave-Generated Displacement Data, Jason A. Teichman
University of New Orleans Theses and Dissertations
Sea state is a subjective quantity whose accuracy depends on an observer’s ability to translate local wind waves into numerical scales. It provides an analytical tool for estimating the impact of the sea on data quality and operational safety. Tasks dependent on the characteristics of local sea surface conditions often require accurate and immediate assessment. An attempt to automate sea state classification using eleven years of ship motion and sea state observation data is made using parametric modeling of distribution-based confidence and tolerance intervals and a probabilistic model using sea state frequencies. Models utilizing distribution intervals are not able ...
Takens Theorem With Singular Spectrum Analysis Applied To Noisy Time Series, 2016 East Tennessee State University
Takens Theorem With Singular Spectrum Analysis Applied To Noisy Time Series, Thomas K. Torku
Electronic Theses and Dissertations
The evolution of big data has led to financial time series becoming increasingly complex, noisy, non-stationary and nonlinear. Takens theorem can be used to analyze and forecast nonlinear time series, but even small amounts of noise can hopelessly corrupt a Takens approach. In contrast, Singular Spectrum Analysis is an excellent tool for both forecasting and noise reduction. Fortunately, it is possible to combine the Takens approach with Singular Spectrum analysis (SSA), and in fact, estimation of key parameters in Takens theorem is performed with Singular Spectrum Analysis. In this thesis, we combine the denoising abilities of SSA with the Takens ...
Construction Of Pair-Wise Balanced Design, 2016 Manonmaniam Sundaranar University
Construction Of Pair-Wise Balanced Design, Rajarathinam Arunachalam, Mahalakshmi Sivasubramanian, Dilip Kumar Ghosh
Journal of Modern Applied Statistical Methods
A new procedure for construction of pair wise balanced design with equal replication and un-equal block sizes based on factorial design have been evolved. Numerical illustration also provided. It was found that the constructed pair wise balanced design was found to be universal optimal.
Simulation And Application Of Binary Logic Regression Models, 2016 Florida International University
Simulation And Application Of Binary Logic Regression Models, Jobany J. Heredia Rico
FIU Electronic Theses and Dissertations
Logic regression (LR) is a methodology to identify logic combinations of binary predictors in the form of intersections (and), unions (or) and negations (not) that are linearly associated with an outcome variable. Logic regression uses the predictors as inputs and enables us to identify important logic combinations of independent variables using a computationally efficient tree-based stochastic search algorithm, unlike the classical regression models, which only consider pre-determined conventional interactions (the “and” rules). In the thesis, we focused on LR with a binary outcome in a logistic regression framework. Simulation studies were conducted to examine the performance of LR under the ...
Implementing Propensity Score Matching With Network Data: The Effect Of Gatt On Bilateral Trade, 2016 Universitat Pompeu Fabra
Implementing Propensity Score Matching With Network Data: The Effect Of Gatt On Bilateral Trade, Luca De Benedictis, Bruno Arpino, Alessandra Mattei
Luca De Benedictis
Predicting Financial Distress: A Comparison Of Survival Analysis And Decision Tree Techniques, Adrian Gepp, Kuldeep Kumar
Financial distress and then the consequent failure of a business is usually an extremely costly and disruptive event. Statistical financial distress prediction models attempt to predict whether a business will experience financial distress in the future. Discriminant analysis and logistic regression have been the most popular approaches, but there is also a large number of alternative cutting - edge data mining techniques that can be used. In this paper, a semi-parametric Cox survival analysis model and non-parametric CART decision trees have been applied to financial distress prediction and compared with each other as well as the most popular approaches. This analysis ...
Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, 2016 Fox Chase Cancer Center
Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang
COBRA Preprint Series
Non-negative matrix factorization (NMF) is a widely used machine learning algorithm for dimension reduction of large-scale data. It has found successful applications in a variety of fields such as computational biology, neuroscience, natural language processing, information retrieval, image processing and speech recognition. In bioinformatics, for example, it has been used to extract patterns and profiles from genomic and text-mining data as well as in protein sequence and structure analysis. While the scientific performance of NMF is very promising in dealing with high dimensional data sets and complex data structures, its computational cost is high and sometimes could be critical for ...
Models For Hsv Shedding Must Account For Two Levels Of Overdispersion, 2016 University of Washington - Seattle Campus
Models For Hsv Shedding Must Account For Two Levels Of Overdispersion, Amalia Magaret
UW Biostatistics Working Paper Series
We have frequently implemented crossover studies to evaluate new therapeutic interventions for genital herpes simplex virus infection. The outcome measured to assess the efficacy of interventions on herpes disease severity is the viral shedding rate, defined as the frequency of detection of HSV on the genital skin and mucosa. We performed a simulation study to ascertain whether our standard model, which we have used previously, was appropriately considering all the necessary features of the shedding data to provide correct inference. We simulated shedding data under our standard, validated assumptions and assessed the ability of 5 different models to reproduce the ...
Multi-State Models With Missing Covariates, 2016 University of Kentucky
Multi-State Models With Missing Covariates, Wenjie Lou
Theses and Dissertations--Statistics
Multi-state models have been widely used to analyze longitudinal event history data obtained in medical studies. The tools and methods developed recently in this area require the complete observed datasets. While, in many applications measurements on certain components of the covariate vector are missing on some study subjects. In this dissertation, several likelihood-based methodologies were proposed to deal with datasets with different types of missing covariates efficiently when applying multi-state models.
Firstly, a maximum observed data likelihood method was proposed when the data has a univariate missing pattern and the missing covariate is a categorical variable. The construction of the ...
Topics In Logistic Regression Analysis, 2016 University of Kentucky
Topics In Logistic Regression Analysis, Zhiheng Xie
Theses and Dissertations--Statistics
Discrete-time Markov chains have been used to analyze the transition of subjects from intact cognition to dementia with mild cognitive impairment and global impairment as intervening transient states, and death as competing risk. A multinomial logistic regression model is used to estimate the probability distribution in each row of the one-step transition matrix that correspond to the transient states. We investigate some goodness of fit tests for a multinomial distribution with covariates to assess the fit of this model to the data. We propose a modified chi-square test statistic and a score test statistic for the multinomial assumption in each ...