Titlebook: Asymmetric Kernel Smoothing; Theory and Applicati Masayuki Hirukawa Book 2018 The Author(s) 2018 Asymmetric Kernel.Kernel Smoothing.Boundar

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Asymmetric Kernels: An Introduction,ry of asymmetric kernels, we provide an informal definition and a list of the kernels. Obviously it is difficult and even uneconomical to investigate each of them fully within the limited space of this book. Instead, we concentrate on a few kernels throughout, explain why they are chosen, and illustrate their functional forms and shapes.

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Specification Testing, Each test has an application-driven flavor, and it is shown to be consistent, i.e., the power of the test approaches one as the sample size diverges. In particular, the test of discontinuity in densities in Sect.?. extends an existing method and contains some new results.

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https://doi.org/10.1007/978-981-10-5466-2Asymmetric Kernel; Kernel Smoothing; Boundary Correction; Nonparametric Estimation; Kernel Density Estim

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Our Emotions as Seen through a WebcamResearchers and policy-makers are often interested in the distributions of economic and financial variables. Specifying their density functions provides natural descriptions of the distributions.

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Assessing Neural Synchrony in Tutoring DyadsAs mentioned in Chap.?., research in asymmetric kernels appears to begin with regression estimation. This chapter investigates nonparametric regression estimation smoothed by asymmetric kernels.

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Peter B. Walker,Ian N. DavidsonThe final chapter presents two applications of asymmetric kernel smoothing to real data. One is on density estimation and the other on a testing problem. Each empirical illustration is closely related to the author’s latest work.

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Univariate Density Estimation,Researchers and policy-makers are often interested in the distributions of economic and financial variables. Specifying their density functions provides natural descriptions of the distributions.

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Bias Correction in Density Estimation,We are motivated to estimate the quantity of interest in a less biased manner, and density estimation is not an exception. The bias correction methods discussed in this chapter are natural extensions of what are originally proposed for nonnegative (or second-order) symmetric kernels.