This thesis examines a newly proposed density estimator in order to evaluate its usefulness for government crop insurance programs confronted by the problem of adverse selection. While the Federal Crop Insurance Corporation (FCIC) offers multiple insurance programs including Group Risk Plan (GRP), what is needed is a more accurate method of estimating actuarially fair premium rates in order to eliminate adverse selection. The Empirical Bayes Nonparametric Kernel Density Estimator (EBNKDE) showed a substantial efficiency gain in estimating crop yield densities. The objective of this research was to apply EBNKDE empirically by means of a simulated game wherein I assumed the role of a private insurance company in order to test for profit gains from the greater efficiency and accuracy promised by using EBNKDE. Employing EBNKDE as well as parametric and nonparametric methods, premium insurance rates for 97 Illinois counties for the years 1991 to 2010 were estimated using corn yield data from 1955 to 2010 taken from the National Agricultural Statistics Service (NASS). The results of this research revealed substantial efficiency gain from using EBNKDE as opposed to other estimators such as Normal, Weibull, and Kernel Density Estimator (KDE). Still, further research using other crops yield data from other states will provide greater insight into EBNKDE and its performance in other situations.

Master of Science / Department of Statistics / John E. Boyer Jr / It has been shown that the most straightforward approach to testing for the difference of two independent population proportions, called the Wald procedure, tends to declare differences too often. Because of this poor performance, various researchers have proposed simple adjustments to the Wald approach that tend to provide significance levels closer to the nominal. Additionally, several tests that take advantage of different methodologies have been proposed. This paper extends the work of Tebbs and Roths (2008), who wrote an R program to compare confidence interval coverage for a variety of these procedures when used to estimate a contrast in two or more binomial parameters. Their program has been adapted to generate exact significance levels and power for the two parameter hypothesis testing situation. Several combinations of binomial parameters and sample sizes are considered. Recommendations for a choice of procedure are made for practical situations.

Previous red light camera (RLC) studies have shown reductions in violations and overall and right angle collisions, however, they may also result in increases in rear-end crashes (Retting & Kyrychenko, 2002; Retting & Ferguson, 2003). Despite their apparent effectiveness, many RLC studies have produced imprecise findings due to inappropriate study designs and/or statistical techniques to control for biases (Retting & Kyrychenko, 2002), therefore, a more comprehensive approach is needed to accurately assess whether they reduce motor vehicle injury collisions. The objective of this proposal is to assess whether RLC’s improve safety at signalized intersections within Miami-Dade County, Florida. Twenty signalized intersections with RLC’s initiating enforcement on January 1st, 2011 were matched to two comparison sites located at least two miles from camera sites to minimize spillover effect. An Empirical Bayes analysis was used to account for regression to the mean. Incidences of all injury, red light running related injury, right-angle/turning, and rear-end collisions were examined. An index of effectiveness along with 95% CI’s were calculated. During the first year of camera enforcement, RLC sites experienced a marginal decrease in right-angle/turn collisions, a significant increase in rear-end collisions, and significant decreases in all-injury and red light running-related injury collisions. An increase in right-angle/turning and rear-end collisions at the RLC sites was observed after two years despite camera enforcement. A significant reduction in red light running-related injury crashes, however, was still observed after two years. A non-significant decline in all injury collisions was also noted. Findings of this research indicate RLC’s reduced red light running-related injury collisions at camera sites, yet its tradeoff was a large increase in rear-end collisions. Further, there was inconclusive evidence whether RLC’s affected right-angle/turning and all injury collisions. Statutory changes in crash reporting during the second year of camera enforcement affected the incidence of right-angle and rear-end collisions, nevertheless, a novelty effect could not be ruled out. A limitation of this study was the small number of injury crashes at each site. In conclusion, future research should consider events such as low frequencies of severe injury/fatal collisions and changes in crash reporting requirements when conducting RLC analyses.

This dissertation consists of three chapters exploring education and crime in the modern economy. The first two chapters focus on inter-district school choice and teacher labor markets in Massachusetts. The third chapter examines the demolition of public housing in Chicago and its interaction with the geospatial distribution of gang territory. In the first chapter, I study the sorting of students to school districts using new lottery data from an inter-district school choice program. I find that moving to a more preferred school district generates benefits to student test scores, coursework quality, high-school graduation, and college attendance. Motivated by these findings, I develop a rich model of treatment effect heterogeneity and estimate it using a new empirical-Bayes-type procedure that leverages non-experimental data to increase precision in quasi-experimental designs. I use the heterogeneous effects to show that nearly all the test score gains from the choice program emerge from Roy selection. In the second chapter (joint with Scott Imberman and Marcus Winters), we describe the relationship between school quality, teacher value-added, and teacher attrition across the public and charter sectors. We begin by documenting important differences in the sources of variation that explain attrition across sectors. Next we demonstrate that while charters are in fact more likely to remove their worst teachers, they are also more likely to lose their best. We conclude by exploring the type and quality of destination schools among teachers who move. In the third chapter, I study the demolition of 22,000 units of public housing on crime in Chicago. Point estimates that incorporate both the direct and spillover effects indicate that in the short run, the average demolition increased city-wide crime by 0.5% per month relative to baseline, with no evidence of offsetting long run reductions. I also provide evidence that spillovers are mediated by demolition-induced migration across gang territorial boundaries. I reconcile my findings with contradictory results from the existing literature by proposing and applying a test for control group contamination. I find that existing results are likely biased by previously unaccounted for spillovers.

As the population grows and the travel demands increase, alternative interchange designs are becoming increasingly popular. The diverging diamond interchange is one alternative design that has been implemented in the United States. This design can accommodate higher flow and unbalanced flow as well as improve safety at the interchange. As the diverging diamond interchange is increasingly considered as a possible solution to problematic interchange locations, it is imperative to investigate the safety effects of this interchange configuration. This report describes the selection of a comparison group of urban diamond interchanges, crash data collection, calibration of functions used to estimate the predicted crash rate in the before and after periods and the Empirical Bayes before and after analysis technique used to determine the safety effectiveness of the diverging diamond interchanges in Utah. A discussion of pedestrian and cyclist safety is also included. The analysis results demonstrated statistically significant decreases in crashes at most of the locations studied. This analysis can be used by UDOT and other transportation agencies as they consider the implementation of the diverging diamond interchanges in the future.

In the present dissertation, we investigate two different nonparametric models; empirical Bayes model and functional deconvolution model. In the case of the nonparametric empirical Bayes estimation, we carried out a complete minimax study. In particular, we derive minimax lower bounds for the risk of the nonparametric empirical Bayes estimator for a general conditional distribution. This result has never been obtained previously. In order to attain optimal convergence rates, we use a wavelet series based empirical Bayes estimator constructed in Pensky and Alotaibi (2005). We propose an adaptive version of this estimator using Lepski’s method and show that the estimator attains optimal convergence rates. The theory is supplemented by numerous examples. Our study of the functional deconvolution model expands results of Pensky and Sapatinas (2009, 2010, 2011) to the case of estimating an (r + 1)-dimensional function or dependent errors. In both cases, we derive minimax lower bounds for the integrated square risk over a wide set of Besov balls and construct adaptive wavelet estimators that attain those optimal convergence rates. In particular, in the case of estimating a periodic (r + 1)-dimensional function, we show that by choosing Besov balls of mixed smoothness, we can avoid the ”curse of dimensionality” and, hence, obtain higher than usual convergence rates when r is large. The study of deconvolution of a multivariate function is motivated by seismic inversion which can be reduced to solution of noisy two-dimensional convolution equations that allow to draw inference on underground layer structures along the chosen profiles. The common practice in seismology is to recover layer structures separately for each profile and then to combine the derived estimates into a two-dimensional function. By studying the two-dimensional version of the model, we demonstrate that this strategy usually leads to estimators which are less accurate than the ones obtained as two-dimensional functional deconvolutions. Finally, we consider a multichannel deconvolution model with long-range dependent Gaussian errors. We do not limit our consideration to a specific type of long-range dependence, rather we assume that the eigenvalues of the covariance matrix of the errors are bounded above and below. We show that convergence rates of the estimators depend on a balance between the smoothness parameters of the response function, the iii smoothness of the blurring function, the long memory parameters of the errors, and how the total number of observations is distributed among the channels.

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This report evaluates the possibility of using sequential learning in a material development setting to help predict material properties and speed up the development of new materials. To do this a Random forest model was built incorporating carefully calibrated prediction uncertainty estimates. The idea behind the model is to use the few data points available in this field and leverage that data to build a better representation of the input-output space as each experiment is performed. Having both predictions and uncertainties to evaluate, several different strategies were developed to investigate performance. Promising results regarding feasibility and potential cost-cutting were found using these strategies. It was found that within a specific performance region of the output space, the mean difference in alloying component price between the cheapest and most expensive material could be as high as 100 %. Also, the model performed fast extrapolation to previously unknown output regions, meaning new, differently performing materials could be found even with very poor initial data. / I denna rapport utvärderas möjligheten att använda sekventiell maskininlärning inom materialutveckling för att kunna prediktera materials egenskaper och därigenom förkorta materialutvecklingsprocessen. För att göra detta byggdes en Random forest regressionsmodell som även innehöll en uppskattning av prediktionsosäkerheten. Tanken bakom modellen är att använda de relativt få datapunkter som generellt brukar vara tillgängliga inom materialvetenskap, och med hjälp av dessa bygga en bättre representation av input-output-rummet genom varje experiment som genomförs. Med både förutsägelser och osäkerheter att utvärdera utvecklades flera olika strategier för att undersöka prestanda för de olika kandidatmaterialen. Genom att använda dessa strategier kunde lovande resultat vad gäller genomförbarhet och potentiell kostnadsbesparing hittas. Det visade sig att, för specifika prestandakrav, den genomsnittliga skillnaden i pris mellan den billigaste och den dyraste materialkemin kan vara så hög som 100 %. Vad gäller övriga resultat klarade modellen av att snabbt extrapolera initial data till tidigare okända regioner av output-rummet. Detta innebär att nya material med ny typ av prestanda kunde hittas även med mycket missanpassad initial träningsdata.

This report evaluates the possibility of using sequential learning in a material development setting to help predict material properties and speed up the development of new materials. To do this a Random forest model was built incorporating carefully calibrated prediction uncertainty estimates. The idea behind the model is to use the few data points available in this field and leverage that data to build a better representation of the input-output space as each experiment is performed. Having both predictions and uncertainties to evaluate, several different strategies were developed to investigate performance. Promising results regarding feasibility and potential cost-cutting were found using these strategies. It was found that within a specific performance region of the output space, the mean difference in alloying component price between the cheapest and most expensive material could be as high as 100 %. Also, the model performed fast extrapolation to previously unknown output regions, meaning new, differently performing materials could be found even with very poor initial data. / I denna rapport utvärderas möjligheten att använda sekventiell maskininlärning inom materialutveckling för att kunna prediktera materials egenskaper och därigenom förkorta materialutvecklingsprocessen. För att göra detta byggdes en Random forest regressionsmodell som även innehöll en uppskattning av prediktionsosäkerheten. Tanken bakom modellen är att använda de relativt få datapunkter som generellt brukar vara tillgängliga inom materialvetenskap, och med hjälp av dessa bygga en bättre representation av input-output-rummet genom varje experiment som genomförs. Med både förutsägelser och osäkerheter att utvärdera utvecklades flera olika strategier för att undersöka prestanda för de olika kandidatmaterialen. Genom att använda dessa strategier kunde lovande resultat vad gäller genomförbarhet och potentiell kostnadsbesparing hittas. Det visade sig att, för specifika prestandakrav, den genomsnittliga skillnaden i pris mellan den billigaste och den dyraste materialkemin kan vara så hög som 100 %. Vad gäller övriga resultat klarade modellen av att snabbt extrapolera initial data till tidigare okända regioner av output-rummet. Detta innebär att nya material med ny typ av prestanda kunde hittas även med mycket missanpassad initial träningsdata.