Implementing Propensity Score Matching With Network Data: The Effect Of Gatt On Bilateral Trade, 2017 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
The Battle Against Malaria: A Teachable Moment, 2017 Schoolcraft College
The Battle Against Malaria: A Teachable Moment, Randy K. Schwartz
Journal of Humanistic Mathematics
Malaria has been humanity’s worst public health problem throughout recorded history. Mathematical methods are needed to understand which factors are relevant to the disease and to develop counter-measures against it. This article and the accompanying exercises provide examples of those methods for use in lower- or upper-level courses dealing with probability, statistics, or population modeling. These can be used to illustrate such concepts as correlation, causation, conditional probability, and independence. The article explains how the apparent link between sickle cell trait and resistance to malaria was first verified in Uganda using the chi-squared probability distribution. It goes on to ...
Data Predictive Control For Building Energy Management, 2017 University of Pennsylvania
Data Predictive Control For Building Energy Management, Achin Jain, Madhur Behl, Rahul Mangharam
Real-Time and Embedded Systems Lab (mLAB)
Decisions on how to best optimize energy systems operations are becoming ever so complex and conflicting, that model-based predictive control (MPC) algorithms must play an important role. However, a key factor prohibiting the widespread adoption of MPC in buildings, is the cost, time, and effort associated with learning first-principles based dynamical models of the underlying physical system. This paper introduces an alternative approach for implementing finite-time receding horizon control using control-oriented data-driven models. We call this approach Data Predictive Control (DPC). Specifically, by utilizing separation of variables, two novel algorithms for implementing DPC using a single regression tree and with ...
The Spatial Dimensions Of State Fiscal Capacity The Mechanisms Of International Influence On Domestic Extractive Efforts, 2017 Arizona State University
The Spatial Dimensions Of State Fiscal Capacity The Mechanisms Of International Influence On Domestic Extractive Efforts, Cameron G. Thies, Olga Chyzh, Mark David Nieman
Mark David Nieman
This paper expands traditional predatory theory approaches to state fiscal capacity by adopting spatial analytical reasoning and methods. While previous work in the predatory theory tradition has often incorporated interdependent external influences, such as war and trade, it has often done so in a way that maintains a theoretical and empirical autonomy of the state. Theoretically, we suggest four mechanisms (coercion, competition, learning, and emulation) that operate to channel information through interstate rivalry and territorial contiguity, trade networks, and the political space associated with regime type and intergovernmental organization membership. We test our predictions using a multi-parametric spatio-temporal autoregressive model ...
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
Novel Models Of Visual Topographic Map Alignment In The Superior Colliculus., 2016 George Washington University
Novel Models Of Visual Topographic Map Alignment In The Superior Colliculus., Ruben A Tikidji-Hamburyan, Tarek A El-Ghazawi, Jason W. Triplett
Pediatrics Faculty Publications
The establishment of precise neuronal connectivity during development is critical for sensing the external environment and informing appropriate behavioral responses. In the visual system, many connections are organized topographically, which preserves the spatial order of the visual scene. The superior colliculus (SC) is a midbrain nucleus that integrates visual inputs from the retina and primary visual cortex (V1) to regulate goal-directed eye movements. In the SC, topographically organized inputs from the retina and V1 must be aligned to facilitate integration. Previously, we showed that retinal input instructs the alignment of V1 inputs in the SC in a manner dependent on ...
Monte Carlo Simulation In Environmental Risk Assessment--Science, Policy And Legal Issues, 2016 University of New Hampshire
Monte Carlo Simulation In Environmental Risk Assessment--Science, Policy And Legal Issues, Susan R. Poulter
RISK: Health, Safety & Environment
Dr. Poulter notes that agencies should anticipate judicial requirements for justification of Monte Carlo simulations and, meanwhile, should consider, e.g., whether their use will make risk assessment policy choices more opaque or apparent.
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 ...
A Traders Guide To The Predictive Universe- A Model For Predicting Oil Price Targets And Trading On Them, 2016 Washington University in St. Louis
A Traders Guide To The Predictive Universe- A Model For Predicting Oil Price Targets And Trading On Them, Jimmie Harold Lenz
Doctor of Business Administration Dissertations
At heart every trader loves volatility; this is where return on investment comes from, this is what drives the proverbial “positive alpha.” As a trader, understanding the probabilities related to the volatility of prices is key, however if you could also predict future prices with reliability the world would be your oyster. To this end, I have achieved three goals with this dissertation, to develop a model to predict future short term prices (direction and magnitude), to effectively test this by generating consistent profits utilizing a trading model developed for this purpose, and to write a paper that anyone with ...
Data Predictive Control For Peak Power Reduction, 2016 University of Pennsylvania
Data Predictive Control For Peak Power Reduction, Achin Jain, Madhur Behl, Rahul Mangharam
Real-Time and Embedded Systems Lab (mLAB)
Decisions on how best to optimize today's energy systems operations are becoming ever so complex and conflicting such that model-based predictive control algorithms must play a key role. However, learning dynamical models of energy consuming systems such as buildings, using grey/white box approaches is very cost and time prohibitive due to its complexity. This paper presents data-driven methods for making control-oriented model for peak power reduction in buildings. Specifically, a data predictive control with regression trees (DPCRT) algorithm, is presented. DPCRT is a finite receding horizon method, using which the building operator can optimally trade off peak power ...
Towards Deeper Understanding In Neuroimaging, 2016 Mind Research Network
Towards Deeper Understanding In Neuroimaging, Rex Devon Hjelm
Computer Science ETDs
Neuroimaging is a growing domain of research, with advances in machine learning having tremendous potential to expand understanding in neuroscience and improve public health. Deep neural networks have recently and rapidly achieved historic success in numerous domains, and as a consequence have completely redefined the landscape of automated learners, giving promise of significant advances in numerous domains of research. Despite recent advances and advantages over traditional machine learning methods, deep neural networks have yet to have permeated significantly into neuroscience studies, particularly as a tool for discovery. This dissertation presents well-established and novel tools for unsupervised learning which aid in ...
The Actual Cost Of Food Systems On Roadway Infrastructure, 2016 Iowa State University
The Actual Cost Of Food Systems On Roadway Infrastructure, Omar G. Smadi, Inya Nienanya, Marwan Ghandour, Silvina Lopez Barrera
The variations among transportation costs for local, regional and conventional food production and distribution systems were investigated for three Iowa counties.
Exploring New Models For Seatbelt Use In Survey Data, 2016 Old Dominion University
Exploring New Models For Seatbelt Use In Survey Data, Mark K. Ledbetter, Norou Diawara, Bryan E. Porter
Virginia Journal of Science
Problem: Several approaches to analyze seatbelt use have been proposed in the literature. Two methods that has not been explored are the use of unweighted and weighted logistic regression model and the use of item response theory (IRT) or the Rasch model. Since accurate methods to predict seatbelt use behavior based upon observed data must include a built-in design method and model, and overcome computation challenges, weighted and IRT method deem to be other options for an observational survey of seat belt use in the state of Virginia.
Method: The observed data from 136 sites within the Commonwealth of Virginia ...
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 ...