Logspline density estimation for binned data

Ja Yong Koo; Charles Kooperberg

doi:10.1016/S0167-7152(99)00097-8

Logspline density estimation for binned data

Ja Yong Koo^*
, Charles Kooperberg

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

Abstract

In this paper we consider logspline density estimation for binned data. Rates of convergence are established when the log-density function is assumed to be in a Besov space. An algorithm involving a procedure similar to maximum likelihood, stepwise knot addition, and stepwise knot deletion is proposed for the estimation of the density function based upon binned data. Numerical examples are used to show the finite-sample performance of inference based on the logspline density estimation.

Original language	English
Pages (from-to)	133-147
Number of pages	15
Journal	Statistics and Probability Letters
Volume	46
Issue number	2
DOIs	https://doi.org/10.1016/S0167-7152(99)00097-8
Publication status	Published - 2000 Jan 15
Externally published	Yes

Keywords

Besov space
Binning
Knot selection
MILE
Optimal rate of convergence

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1016/S0167-7152(99)00097-8

Cite this

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abstract = "In this paper we consider logspline density estimation for binned data. Rates of convergence are established when the log-density function is assumed to be in a Besov space. An algorithm involving a procedure similar to maximum likelihood, stepwise knot addition, and stepwise knot deletion is proposed for the estimation of the density function based upon binned data. Numerical examples are used to show the finite-sample performance of inference based on the logspline density estimation.",

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AB - In this paper we consider logspline density estimation for binned data. Rates of convergence are established when the log-density function is assumed to be in a Besov space. An algorithm involving a procedure similar to maximum likelihood, stepwise knot addition, and stepwise knot deletion is proposed for the estimation of the density function based upon binned data. Numerical examples are used to show the finite-sample performance of inference based on the logspline density estimation.

KW - Besov space

KW - Binning

KW - Knot selection

KW - MILE

KW - Optimal rate of convergence

UR - https://www.scopus.com/pages/publications/0042785054

U2 - 10.1016/S0167-7152(99)00097-8

DO - 10.1016/S0167-7152(99)00097-8

M3 - Article

AN - SCOPUS:0042785054

SN - 0167-7152

VL - 46

SP - 133

EP - 147

JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

IS - 2

ER -

Logspline density estimation for binned data

Abstract

Keywords

ASJC Scopus subject areas

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