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Implementing Propensity Score Matching With Network Data: The Effect Of Gatt On Bilateral Trade, Luca De Benedictis, Bruno Arpino, Alessandra Mattei 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

Motivated by the evaluation of the causal effect of the General Agreement on Tariffs and Trade on bilateral international trade flows, we investigate the role of network structure in propensity score matching under the assumption of strong ignorability. We study the sensitivity of causal inference with respect to the presence of characteristics of the network in the set of confounders conditional on which strong ignorability is assumed to hold. We find that estimates of the average causal effect are highly sensitive to the presence of node-level network statistics in the set of confounders. Therefore, we argue that estimates may suffer ...


The Spatial Dimensions Of State Fiscal Capacity The Mechanisms Of International Influence On Domestic Extractive Efforts, Cameron G. Thies, Olga Chyzh, Mark David Nieman 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, Philip T. Reiss, David L. Miller, Pei-Shien Wu, Wen-Yu Hua 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 number of classical approaches to nonparametric regression have recently been extended to the case of functional predictors. This paper introduces a new method of this type, which extends intermediate-rank penalized smoothing to scalar-on-function regression. The core idea is to regress the response on leading principal coordinates defined by a relevant distance among the functional predictors, while applying a ridge penalty. Our publicly available implementation, based on generalized additive modeling software, allows for fast optimal tuning parameter selection and for extensions to multiple functional predictors, exponential family-valued responses, and mixed-effects models. In an application to signature verification data, the proposed ...


Novel Models Of Visual Topographic Map Alignment In The Superior Colliculus., Ruben A Tikidji-Hamburyan, Tarek A El-Ghazawi, Jason W. Triplett 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, Susan R. Poulter 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, Xiang Liu 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, Jimmie Harold Lenz 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, Achin Jain, Madhur Behl, Rahul Mangharam 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 ...


The Actual Cost Of Food Systems On Roadway Infrastructure, Omar G. Smadi, Inya Nienanya, Marwan Ghandour, Silvina Lopez Barrera 2016 Iowa State University

The Actual Cost Of Food Systems On Roadway Infrastructure, Omar G. Smadi, Inya Nienanya, Marwan Ghandour, Silvina Lopez Barrera

Marwan Ghandour

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, Mark K. Ledbetter, Norou Diawara, Bryan E. Porter 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, Jonah B. Gelbach 2016 University of Pennsylvania Law School

The Reduced Form Of Litigation Models And The Plaintiff's Win Rate, Jonah B. Gelbach

Faculty Scholarship

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, Joseph M. Hilbe 2016 Arizona State University

Addition To Pglr Chap 6, Joseph M. Hilbe

Joseph M Hilbe

Addition to Chapter 6 in Practical Guide to Logistic Regression. Added section on Bayesian logistic regression using Stata.


Passive Visual Analytics Of Social Media Data For Detection Of Unusual Events, Kush Rustagi, Junghoon Chae 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, Ijeoma W. Ekwegh 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, Aastha Khatiwada 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, Edward Richard Bee 2016 University of Southern Mississippi

The Influence Of The Electric Supply Industry On Economic Growth In Less Developed Countries, Edward Richard Bee

Dissertations

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, Timothy Baker Mr, Ming-Chien Sung, Johnnie Johnson Professor, Tiejun Ma 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, Katherine Jozefiak 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, Jason A. Teichman 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, Thomas K. Torku 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 ...


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