Density estimation using bootstrap quantile variance and quantile-mean covariance
We propose two novel bootstrap density estimators based on the quantile variance and the quantile-mean covariance. We review previous developments on quantile-density estimation and asymptotic results in the literature that can be applied to this case. We conduct Monte Carlo simulations for dierent...
Autores principales: | , |
---|---|
Formato: | Artículo publishedVersion |
Lenguaje: | Inglés |
Publicado: |
Instituto Interdisciplinario de Economía Política (IIEP UBA-CONICET)
2022
|
Materias: | |
Acceso en línea: | https://ojs.economicas.uba.ar/DT-IIEP/article/view/2449 https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=dociiep&d=2449_oai |
Aporte de: |
id |
I28-R145-2449_oai |
---|---|
record_format |
dspace |
spelling |
I28-R145-2449_oai2025-02-11 Montes Rojas, Gabriel Mena, Andrés Sebastián 2022-11-28 We propose two novel bootstrap density estimators based on the quantile variance and the quantile-mean covariance. We review previous developments on quantile-density estimation and asymptotic results in the literature that can be applied to this case. We conduct Monte Carlo simulations for dierent data generating processes, sample sizes, and parameters. The estimators perform well in comparison to benchmark nonparametric kernel density estimator. Some of the explored smoothing techniques present lower bias and mean integrated squared errors, which indicates that the proposed estimator is a promising strategy. Evaluamos dos estimadores de densidades basados en la varianza y la covarianza entre media y varianza estimados por bootstrap. Revisamos otros desarrollos de estimadores de densidad relacionados con cuantiles. Las simulaciones de Monte Carlo para distintos procesos generadores de datos, tamaños de muestra, y otros parámetros muestran que los estimadores tienen buena performance en comparación con el estimador no paramétrico de kernel. Algunas de las técnicas de suavizamiento tienen menor error cuadrático medio integrado y sesgo, lo que indica que los estimadores propuestos son una estrategia promisoria. application/pdf https://ojs.economicas.uba.ar/DT-IIEP/article/view/2449 eng Instituto Interdisciplinario de Economía Política (IIEP UBA-CONICET) https://ojs.economicas.uba.ar/DT-IIEP/article/view/2449/3196 Documentos de trabajo del Instituto Interdisciplinario de Economía Política; Núm. 50 (2020): Estimación de la densidad utilizando el cuantil bootstrap varianza y covarianza media cuantil; 28 Working Papers series at Instituto Interdisciplinario de Economía Política; No. 50 (2020): Density estimation using bootstrap quantile variance and quantile-mean covariance; 28 2451-5728 estimación de densidades varianza de cuantiles covarianza entre media y cuantiles bootstrap density estimation quantile variance quantile-mean covariance bootstrap Density estimation using bootstrap quantile variance and quantile-mean covariance Estimación de la densidad utilizando el cuantil bootstrap varianza y covarianza media cuantil info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=dociiep&d=2449_oai |
institution |
Universidad de Buenos Aires |
institution_str |
I-28 |
repository_str |
R-145 |
collection |
Repositorio Digital de la Universidad de Buenos Aires (UBA) |
language |
Inglés |
orig_language_str_mv |
eng |
topic |
estimación de densidades varianza de cuantiles covarianza entre media y cuantiles bootstrap density estimation quantile variance quantile-mean covariance bootstrap |
spellingShingle |
estimación de densidades varianza de cuantiles covarianza entre media y cuantiles bootstrap density estimation quantile variance quantile-mean covariance bootstrap Montes Rojas, Gabriel Mena, Andrés Sebastián Density estimation using bootstrap quantile variance and quantile-mean covariance |
topic_facet |
estimación de densidades varianza de cuantiles covarianza entre media y cuantiles bootstrap density estimation quantile variance quantile-mean covariance bootstrap |
description |
We propose two novel bootstrap density estimators based on the quantile variance and the quantile-mean covariance. We review previous developments on quantile-density estimation and asymptotic results in the literature that can be applied to this case. We conduct Monte Carlo simulations for dierent data generating processes, sample sizes, and parameters. The estimators perform well in comparison to benchmark nonparametric kernel density estimator. Some of the explored smoothing techniques present lower bias and mean integrated squared errors, which indicates that the proposed estimator is a promising strategy. |
format |
Artículo publishedVersion |
author |
Montes Rojas, Gabriel Mena, Andrés Sebastián |
author_facet |
Montes Rojas, Gabriel Mena, Andrés Sebastián |
author_sort |
Montes Rojas, Gabriel |
title |
Density estimation using bootstrap quantile variance and quantile-mean covariance |
title_short |
Density estimation using bootstrap quantile variance and quantile-mean covariance |
title_full |
Density estimation using bootstrap quantile variance and quantile-mean covariance |
title_fullStr |
Density estimation using bootstrap quantile variance and quantile-mean covariance |
title_full_unstemmed |
Density estimation using bootstrap quantile variance and quantile-mean covariance |
title_sort |
density estimation using bootstrap quantile variance and quantile-mean covariance |
publisher |
Instituto Interdisciplinario de Economía Política (IIEP UBA-CONICET) |
publishDate |
2022 |
url |
https://ojs.economicas.uba.ar/DT-IIEP/article/view/2449 https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=dociiep&d=2449_oai |
work_keys_str_mv |
AT montesrojasgabriel densityestimationusingbootstrapquantilevarianceandquantilemeancovariance AT menaandressebastian densityestimationusingbootstrapquantilevarianceandquantilemeancovariance AT montesrojasgabriel estimaciondeladensidadutilizandoelcuantilbootstrapvarianzaycovarianzamediacuantil AT menaandressebastian estimaciondeladensidadutilizandoelcuantilbootstrapvarianzaycovarianzamediacuantil |
_version_ |
1825551329779515392 |