Local Influence in Regression Models with Measurement Errors and Censored Data Considering the Student–t Distribution

Springer Science and Business Media LLC - Tập 86 Số 1 - Trang 91-108 - 2024
Alonso Montoya1
1Departamento de Estatística, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, Campus Pampulha, Belo Horizonte, 31.270-901, Minas Gerais, Brazil

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Tài liệu tham khảo

Barnett VD (1969) Simultaneous pairwise linear structural relationships. Biometrics 25:129–142

Bolfarine H, Galea-Rojas M (1996) On structural comparative calibration under a t-model. Computational Statistics 11:63–85

Bolfarine H, Montenegro LC, Lachos VH (2007) Influence diagnostics for skewnormal linear mixed models. Sankhya: The Indian Journal of Statistics 69(4):648–670

Buonaccorsi J (2010) Measurement Error: Models, Methods, and Applications. Chapman and Hall/CRC, Boca Raton

Carroll RJ, Ruppert D, Stefanski LA, et al (2006) Measurement Error in Nonlinear Models. Chapman & Hall/CRC, Boca Raton, second edition.

Cheng CL, Van-Ness JW (1999) Statistical regression with measurement error. Arnold, London

Chipkevitch E, Nishimura RT, Tu DGS, et al (1996) Clinical measurement of testicular volume in adolescents: Comparison of the reliability of 5 methods. The Journal of Urology 156:2050–2053

Cook RD (1977) Detection of influential observation in linear regression. Technometrics 19:15–18

Cook RD (1986) Assessment of local influence. Journal of the Royal Statistical Society, Series B 48:133–169

Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, Series B 39:1–22

Dunn G (1992) Design and Analysis of Reliability: The statistical evaluation of measurement errors. Edward Arnold, New York

Fuller WA (1987) Measurement Error Models. John Wiley and Sons, New York

Kelly G (1984) The influence function in the errors in variables problem. Annals of Statistics 12:87–100

Lachos VH, Angolini T, Abanto-Valle CA (2011) On estimation and local influence analysis for measurement errors models under heavy-tailed distributions. Statistical Papers 52:567–590

Lange KL, Little RJA, Taylor JMG (1989) Robust statistical modeling using the t distribution. Journal of the American Statistical Association 84:881–896

Lee SY, Xu L (2004) Influence analysis of nonlinear mixed-effects models. Computational Statistics and Data Analysis 45:321–341

Lu Y, Ye K, Mathur A, et al (1997) Comparative calibration without a gold standard. Statistics in Medicine 16:1889–1905

Massuia MB, Cabral CRB, Matos LA, et al (2015) Influence diagnostics for student-t censored linear regression models. Statistics 49:1074–1094

Matos LA, Castro LM, Cabral CRB, et al (2018) Multivariate measurement error models based on student-t distribution under censored responses. Statistics 52(6):1395–1416. https://doi.org/10.1080/02331888.2018.1527841

Meng XL, Rubin DB (1993) Maximum likelihood estimation via the ecm algorithm: A general framework. Biometrika 80:267–278

Poon WY, Poon YS (1999) Conformal normal curvature and assessment of local influence. Journal of the Royal Statistical Society, Series B 61:51–61

R Core Team (2023) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/

Zhu H, Lee S (2001) Local influence for incomplete-data models. Journal of the Royal Statistical Society, Series B 63:121–126