Histopathological Findings of Intracranial Thrombi in Nonbacterial Thrombotic Endocarditis

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변수계수모형에서의 단조함수추정

학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2013. 2. 박병욱.Function estimation under shape constraints is gaining great popularity among statisticians and particularly, monotone function estimation has been studied extensively. This paper introduces function estimation methods in the varying coefficient model when the coefficient functions are monotone. To estimate the coefficient functions, we have to consider the covariate effect, which enter into the weights in the varying coefficient model. We first review the estimation method and algorithm in the traditional regression model and see how this method can be extended to the additive model. Then, we present formulation and algorithm of monotone function estimation in the varying coefficient model by adapting the minimization problem to the traditional case. We also attempt to extend this model to more generalized varying coefficient model where only some part of the coefficient functions enters into the model. Finally, we carry out numerical studies with simulated data.1 Introduction 1 2 Isotone Regression 4 2.1 Optimization under the monotone constraints 4 2.2 Isotone regression model 8 2.3 Additive isotone regression model 10 3 Estimation in the Varying Coefficient Model 13 3.1 One-dimensional varying coefficient model 13 3.2 Isotonic varying coefficient model 15 3.3 Extended varying coefficient model 18 4 Numerical Studies 23 4.1 Simulated data 23 4.2 Simulation results 24 5 Conclusion 28 References 30 Abstract in Korean 32Maste

역 톤 매핑 방법

The present invention provides an inverse tone mapping method that separates a low-contrast-ratio image into sublayer images, classifies each sublayer image into several categories in accordance withthe characteristics of each sublayer image, and learns a transformation matrix representing a relationship between the low-contrast-ratio image and a high-contrast-ratio image for each category. In addition, the present invention provides a technology that separates an input low-contrast-ratio image into sublayer images, selects a category corresponding to each sublayer image, and applies a learned transformation matrix to generate a high-contrast-ratio image