Browsing by Subject "Nonparametric regression"
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Item Nonparametric regression analysis(2015-05) Malloy, Shuling Guo; Lin, Lizhen, Ph. D.; Myers, MaggieNonparametric regression uses nonparametric and flexible methods in analyzing complex data with unknown regression relationships by imposing minimum assumptions on the regression function. The theory and applications of nonparametric regression methods with an emphasis on kernel regression, smoothing spines and Gaussian process regression are reviewed in this report. Two datasets are analyzed to demonstrate and compare the three nonparametric regression models in R.Item Production Economics Modeling and Analysis of Polluting firms: The Production Frontier Approach(2012-10-19) Mekaroonreung, MaetheeAs concern grows about energy and environment issues, energy and environmental modeling and related policy analysis are critical issues for today's society. Polluting firms such as coal power plants play an important role in providing electricity to drive the U.S. economy as well as producing pollution that damages the environment and human health. This dissertation is intended to model and estimate polluting firms' production using nonparametric methods. First, frontier production function of polluting firms is characterized by weak disposability between outputs and pollutants to reflecting the opportunity cost to reduce pollutants. The StoNED method is extended to estimate a weak disposability frontier production function accounting for random noise in the data. The method is applied to the U.S. coal power plants under the Acid Rain Program to find the average technical inefficiency and shadow price of SO2 and NOx. Second, polluting firms' production processes are modeled characterizing both the output production process and the pollution abatement process. Using the law of conservation of mass applied to the pollution abatement process, this dissertation develops a new frontier pollutant function which then is used to find corresponding marginal abatement cost of pollutants. The StoNEZD method is applied to estimate a frontier pollutant function considering the vintage of capital owned by the polluting firms. The method is applied to estimate the average NOx marginal abatement cost for the U.S. coal power plants under the current Clean Air Interstate Rule NOx program. Last, the effect of a technical change on marginal abatement costs are investigated using an index decomposition technique. The StoNEZD method is extended to estimate sequential frontier pollutant functions reflecting the innovation in pollution reduction. The method is then applied to estimate a technical change effect on a marginal abatement cost of the U.S. coal power plants under the current Clean Air Interstate Rule NOx program.Item Testing for spatial correlation and semiparametric spatial modeling of binary outcomes with application to aberrant crypt foci in colon carcinogenesis experiments(Texas A&M University, 2005-11-01) Apanasovich, Tatiyana VladimirovnaIn an experiment to understand colon carcinogenesis, all animals were exposed to a carcinogen while half the animals were also exposed to radiation. Spatially, we measured the existence of aberrant crypt foci (ACF), namely morphologically changed colonic crypts that are known to be precursors of colon cancer development. The biological question of interest is whether the locations of these ACFs are spatially correlated: if so, this indicates that damage to the colon due to carcinogens and radiation is localized. Statistically, the data take the form of binary outcomes (corresponding to the existence of an ACF) on a regular grid. We develop score??type methods based upon the Matern and conditionally autoregression (CAR) correlation models to test for the spatial correlation in such data, while allowing for nonstationarity. Because of a technical peculiarity of the score??type test, we also develop robust versions of the method. The methods are compared to a generalization of Moran??s test for continuous outcomes, and are shown via simulation to have the potential for increased power. When applied to our data, the methods indicate the existence of spatial correlation, and hence indicate localization of damage. Assuming that there are correlations in the locations of the ACF, the questions are how great are these correlations, and whether the correlation structures di?er when an animal is exposed to radiation. To understand the extent of the correlation, we cast the problem as a spatial binary regression, where binary responses arise from an underlying Gaussian latent process. We model these marginal probabilities of ACF semiparametrically, using ?xed-knot penalized regression splines and single-index models. We ?t the models using pairwise pseudolikelihood methods. Assuming that the underlying latent process is strongly mixing, known to be the case for many Gaussian processes, we prove asymptotic normality of the methods. The penalized regression splines have penalty parameters that must converge to zero asymptotically: we derive rates for these parameters that do and do not lead to an asymptotic bias, and we derive the optimal rate of convergence for them. Finally, we apply the methods to the data from our experiment.