1 resultado para QUANTILE REGRESSION
em QSpace: Queen's University - Canada
Filtro por publicador
- ABACUS. Repositorio de Producción Científica - Universidad Europea (1)
- Aberdeen University (1)
- Academic Research Repository at Institute of Developing Economies (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (2)
- Andina Digital - Repositorio UASB-Digital - Universidade Andina Simón Bolívar (1)
- Applied Math and Science Education Repository - Washington - USA (2)
- Aquatic Commons (4)
- Archimer: Archive de l'Institut francais de recherche pour l'exploitation de la mer (1)
- Archive of European Integration (1)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (2)
- Aston University Research Archive (47)
- Biblioteca de Teses e Dissertações da USP (1)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (29)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (37)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (55)
- Brunel University (1)
- Bulgarian Digital Mathematics Library at IMI-BAS (11)
- Cambridge University Engineering Department Publications Database (66)
- CentAUR: Central Archive University of Reading - UK (58)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (13)
- Cochin University of Science & Technology (CUSAT), India (11)
- Collection Of Biostatistics Research Archive (27)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (2)
- Corvinus Research Archive - The institutional repository for the Corvinus University of Budapest (3)
- Dalarna University College Electronic Archive (3)
- Deakin Research Online - Australia (77)
- Department of Computer Science E-Repository - King's College London, Strand, London (1)
- Digital Commons - Michigan Tech (3)
- Digital Commons - Montana Tech (1)
- Digital Commons @ DU | University of Denver Research (1)
- Digital Commons at Florida International University (4)
- Digital Knowledge Repository of Central Drug Research Institute (1)
- DigitalCommons@The Texas Medical Center (24)
- DigitalCommons@University of Nebraska - Lincoln (2)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (1)
- Duke University (5)
- Glasgow Theses Service (1)
- Greenwich Academic Literature Archive - UK (1)
- Helda - Digital Repository of University of Helsinki (4)
- Indian Institute of Science - Bangalore - Índia (29)
- INSTITUTO DE PESQUISAS ENERGÉTICAS E NUCLEARES (IPEN) - Repositório Digital da Produção Técnico Científica - BibliotecaTerezine Arantes Ferra (1)
- Instituto Politécnico do Porto, Portugal (2)
- Massachusetts Institute of Technology (3)
- Memorial University Research Repository (1)
- Ministerio de Cultura, Spain (4)
- National Center for Biotechnology Information - NCBI (17)
- Nottingham eTheses (1)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (1)
- Publishing Network for Geoscientific & Environmental Data (1)
- QSpace: Queen's University - Canada (1)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (46)
- Queensland University of Technology - ePrints Archive (88)
- RDBU - Repositório Digital da Biblioteca da Unisinos (1)
- Repositorio Academico Digital UANL (2)
- Repositório Alice (Acesso Livre à Informação Científica da Embrapa / Repository Open Access to Scientific Information from Embrapa) (1)
- Repositório Científico da Universidade de Évora - Portugal (2)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (1)
- Repositório digital da Fundação Getúlio Vargas - FGV (17)
- Repositório Digital da UNIVERSIDADE DA MADEIRA - Portugal (1)
- Repositório Institucional da Universidade de Brasília (1)
- Repositório Institucional da Universidade Federal do Rio Grande do Norte (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (74)
- Repositorio Institucional Universidad EAFIT - Medelin - Colombia (1)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (1)
- SAPIENTIA - Universidade do Algarve - Portugal (1)
- School of Medicine, Washington University, United States (1)
- Scientific Open-access Literature Archive and Repository (1)
- The Scholarly Commons | School of Hotel Administration; Cornell University Research (1)
- Universidad de Alicante (4)
- Universidad del Rosario, Colombia (7)
- Universidad Politécnica de Madrid (15)
- Universidade Complutense de Madrid (1)
- Universidade Federal do Rio Grande do Norte (UFRN) (2)
- Universita di Parma (1)
- Universitat de Girona, Spain (4)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (1)
- Université de Lausanne, Switzerland (3)
- Université de Montréal, Canada (10)
- Université Laval Mémoires et thèses électroniques (1)
- University of Canberra Research Repository - Australia (2)
- University of Connecticut - USA (3)
- University of Michigan (9)
- University of Queensland eSpace - Australia (23)
- University of Southampton, United Kingdom (4)
- University of Washington (1)
- Worcester Research and Publications - Worcester Research and Publications - UK (2)
Resumo:
Quantile regression (QR) was first introduced by Roger Koenker and Gilbert Bassett in 1978. It is robust to outliers which affect least squares estimator on a large scale in linear regression. Instead of modeling mean of the response, QR provides an alternative way to model the relationship between quantiles of the response and covariates. Therefore, QR can be widely used to solve problems in econometrics, environmental sciences and health sciences. Sample size is an important factor in the planning stage of experimental design and observational studies. In ordinary linear regression, sample size may be determined based on either precision analysis or power analysis with closed form formulas. There are also methods that calculate sample size based on precision analysis for QR like C.Jennen-Steinmetz and S.Wellek (2005). A method to estimate sample size for QR based on power analysis was proposed by Shao and Wang (2009). In this paper, a new method is proposed to calculate sample size based on power analysis under hypothesis test of covariate effects. Even though error distribution assumption is not necessary for QR analysis itself, researchers have to make assumptions of error distribution and covariate structure in the planning stage of a study to obtain a reasonable estimate of sample size. In this project, both parametric and nonparametric methods are provided to estimate error distribution. Since the method proposed can be implemented in R, user is able to choose either parametric distribution or nonparametric kernel density estimation for error distribution. User also needs to specify the covariate structure and effect size to carry out sample size and power calculation. The performance of the method proposed is further evaluated using numerical simulation. The results suggest that the sample sizes obtained from our method provide empirical powers that are closed to the nominal power level, for example, 80%.