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Smoothing Spline Semiparametric Density Models

Density estimation plays a fundamental role in many areas of statistics and machine learning. Parametric, nonparametric, and semiparametric density estimation methods have been proposed in the literature. Semiparametric density models are flexible in incorporating domain knowledge and uncertainty regarding the shape of the density function. Existing literature on semiparametric density models is scattered and lacks a systematic framework. In this article, we consider a unified framework based on reproducing kernel Hilbert space for modeling, estimation, computation, and theory. We propose general semiparametric density models for both a single sample and multiple samples which include many existing semiparametric density models as special cases. We develop penalized likelihood based estimation methods and computational methods under different situations. We establish joint consistency and derive convergence rates of the proposed estimators for both finite dimensional Euclidean parameters and an infinite-dimensional functional parameter. We validate our estimation methods empirically through simulations and an application. Supplementary materials for this article are available online.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION; Vol.117 No.537 March 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 237-250

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

REPRODUCING KERNEL HILBERT SPACE
NONLINEAR MODELS
BACKFITTING
GAUSS-NEWTON ALGORITHM
JOINT ASYMPTOTICS
MULTI-SAMPLES

Info Detail Spesifik

https://doi.org/10.1080/01621459.2020.1769636

Pernyataan Tanggungjawab

Jiahui Yu, Jian Shi, Anna Liu, Yuedong Wang

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