Spline local basis methods for nonparametric density estimation-Reference-Cited by-同舟云学术

Spline local basis methods for nonparametric density estimation

Published:2023-01-01 Issue:none Volume:17 Page:
ISSN:1935-7516
Container-title:Statistics Surveys
language:
Short-container-title:Statist. Surv.

Author:

Kirkby J. Lars¹,Leitao Álvaro²,Nguyen Duy³

Affiliation:

1. School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30318, United States

2. Universitat Oberta de Catalunya (UOC), 08018 Barcelona, Spain

3. Department of Mathematics, Marist College, Poughkeepsie, NY 12601, United States

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference129 articles.

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3. BARRON, A., RISSANEN, J. and YU, B. (1998). The minimum description length principle in coding and modeling. IEEE transactions on information theory 44 2743–2760.

4. BERNARD, C., CUI, Z. and MCLEISH, D. (2012). Nearly exact option price simulation using characteristic functions. International Journal of Theoretical and Applied Finance 15 1–29.

5. BLU, T. and UNSER, M. (2004). Quantitative L/sup 2/approximation error of a probability density estimate given by its samples. In 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing 3 iii–952. IEEE.

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