A New Regression Model on the Unit Interval: Properties, Estimation, and Application

被引:0
|
作者
Benites, Yury R. [1 ]
Cancho, Vicente G. [1 ]
Ortega, Edwin M. M. [2 ]
Vila, Roberto [3 ]
Cordeiro, Gauss M. [4 ]
机构
[1] Univ Sao Paulo, Dept Appl Math & Stat, BR-13566590 Sao Carlos, Brazil
[2] Univ Sao Paulo, Dept Exact Sci, BR-13418900 Piracicaba, Brazil
[3] Univ Brasilia, Dept Stat, BR-70910900 Brasilia, Brazil
[4] Univ Fed Pernambuco, Dept Stat, BR-50670901 Recife, Brazil
关键词
Bayesian inference; generalized extreme value distribution; Johnson S-B distribution; regression model; BAYESIAN-ANALYSIS; BETA REGRESSION; DISTRIBUTIONS; VARIABLES; RATES;
D O I
10.3390/math10173198
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
A new and flexible distribution is introduced for modeling proportional data based on the quantile of the generalized extreme value distribution. We obtain explicit expressions for the moments, quantiles, and other structural properties. An extended regression model is constructed as an alternative to compete with the beta regression. Some simulations from the Bayesian perspectives are developed, and an illustrative application to real data involving the comparison of models and influence diagnostics is also addressed.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] A new unit distribution: properties, estimation, and regression analysis
    Karakaya, Kadir
    Rajitha, C. S.
    Saglam, Sule
    Tashkandy, Yusra A.
    Bakr, M. E.
    Muse, Abdisalam Hassan
    Kumar, Anoop
    Hussam, Eslam
    Gemeay, Ahmed M.
    SCIENTIFIC REPORTS, 2024, 14 (01)
  • [2] A New Probability Model with Support on Unit Interval: Structural Properties, Regression of Bounded Response and Applications
    Chakraborty, Subrata
    Ong, Seng Huat
    Ng, Choung Min
    JOURNAL OF STATISTICAL THEORY AND PRACTICE, 2023, 17 (04)
  • [3] A New Probability Model with Support on Unit Interval: Structural Properties, Regression of Bounded Response and Applications
    Subrata Chakraborty
    Seng Huat Ong
    Choung Min Ng
    Journal of Statistical Theory and Practice, 2023, 17
  • [4] Bayesian Interval Estimation of Tobit Regression Model
    Lee, Seung-Chun
    Choi, Byung Su
    KOREAN JOURNAL OF APPLIED STATISTICS, 2013, 26 (05) : 737 - 746
  • [5] Interval estimation for a fuzzy linear regression model
    Yoon, Jin Hee
    Kim, Hae Kyung
    Jung, Hye-Young
    Lee, Woo-Joo
    Choi, Seung Hoe
    PROCEEDINGS OF THE 20TH CZECH-JAPAN SEMINAR ON DATA ANALYSIS AND DECISION MAKING UNDER UNCERTAINTY, 2017, : 227 - 233
  • [6] New Lifetime Distribution for Modeling Data on the Unit Interval: Properties, Applications and Quantile Regression
    Nasiru, Suleman
    Abubakari, Abdul Ghaniyyu
    Chesneau, Christophe
    MATHEMATICAL AND COMPUTATIONAL APPLICATIONS, 2022, 27 (06)
  • [7] Improved point and interval estimation for a beta regression model
    Ospina, Raydonal
    Cribari Neto, Francisco
    Vasconcellos, Klaus L. P.
    COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2006, 51 (02) : 960 - 981
  • [8] A Constrained Interval-Valued Linear Regression Model:A New Heteroscedasticity Estimation Method
    ZHONG Yu
    ZHANG Zhongzhan
    LI Shoumei
    JournalofSystemsScience&Complexity, 2020, 33 (06) : 2048 - 2066
  • [9] A Constrained Interval-Valued Linear Regression Model: A New Heteroscedasticity Estimation Method
    Yu Zhong
    Zhongzhan Zhang
    Shoumei Li
    Journal of Systems Science and Complexity, 2020, 33 : 2048 - 2066
  • [10] A Constrained Interval-Valued Linear Regression Model: A New Heteroscedasticity Estimation Method
    Zhong, Yu
    Zhang, Zhongzhan
    Li, Shoumei
    JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY, 2020, 33 (06) : 2048 - 2066