Nonparametric Estimation of Additive Models with Shape Constraints Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/tq57nv074

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  • Monotone additive models are useful in estimating productivity curves or analyzing disease risk where the predictors are known to have monotonic effects on the response. Existing literature mainly focuses on univariate monotone smoothing. Available methods for the estimation of monotone additive models are either difficult to interpret or have no asymptotic guarantees. In the first part of this dissertation, we propose a one-step backfitted constrained polynomial spline method for the estimation of monotone additive models. In our proposed method, we obtain monotone estimators by imposing a set of linear constraints on the spline coefficients for each additive component. In the second part of the dissertation, we extend the constrained polynomial spline method to estimate the production frontier that is used to quantify the maximum production output in econometrics. The estimation of frontier functions is more challenging since it is the boundary of the support rather than the mean output function to be estimated. Here, we develop a two-step shape constrained polynomial spline method for the frontier estimation. The first step is to capture the shape of frontier while the second step is to estimate the location of frontier. Both proposed methods in this dissertation give smooth estimators with the desired shape constraints (monotonicity or/and concavity). They are easily implementable and computationally efficient by taking advantage of linear programming. Most importantly, our methods are applicable for multi-dimensions where some existing methods fail to work. For the assessment of properties of the proposed estimators, asymptotic theory is also developed. In addition, the simulation studies and application of our methods to analyze Norwegian Farm data in both parts suggest that our proposed methods have better numerical performance than the existing methods, especially when the data has outliers.
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  • description.provenance : Submitted by Lu Wang (wangl3@oregonstate.edu) on 2016-05-31T03:43:23Z No. of bitstreams: 1 WangLu2016.pdf: 1548267 bytes, checksum: f0e2a05eb7143e7bde0f3cf4e691addd (MD5)
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  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2016-06-09T17:14:22Z (GMT) No. of bitstreams: 1 WangLu2016.pdf: 1548267 bytes, checksum: f0e2a05eb7143e7bde0f3cf4e691addd (MD5)
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