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THE RESIDUALS RQR: A DIAGNOSTIC MEASURE IN R FOR CUB MODELS ; LOS RESIDUALES RQR: UNA MEDIDA DIAGNÓSTICA EN R PARA LOS MODELOS CUB

Mazo Vélez, Deisy Alejandra ; Hernández-Barajas, Freddy
In: Revista de la Facultad de Ciencias; Vol. 9 No. 1 (2020); 92-111 ; Revista de la Facultad de Ciencias; Vol. 9 Núm. 1 (2020); 92-111 ; 2357-5549 ; 0121-747X, 2020
academicJournal

Titel:
THE RESIDUALS RQR: A DIAGNOSTIC MEASURE IN R FOR CUB MODELS ; LOS RESIDUALES RQR: UNA MEDIDA DIAGNÓSTICA EN R PARA LOS MODELOS CUB
Autor/in / Beteiligte Person: Mazo Vélez, Deisy Alejandra ; Hernández-Barajas, Freddy
Link:
Zeitschrift: Revista de la Facultad de Ciencias; Vol. 9 No. 1 (2020); 92-111 ; Revista de la Facultad de Ciencias; Vol. 9 Núm. 1 (2020); 92-111 ; 2357-5549 ; 0121-747X, 2020
Veröffentlichung: Universidad Nacional de Colombia - Sede Medellín - Facultad de Ciencias, 2020
Medientyp: academicJournal
Schlagwort:
  • CUB models
  • Ordinal data
  • Fitting measure
  • RQR residuals
  • statistics
  • Modelos CUB
  • Datos ordinales
  • Medida de ajuste
  • Residuales RQR
  • estadística
Sonstiges:
  • Nachgewiesen in: BASE
  • Sprachen: Spanish; Castilian
  • Collection: Universidad Nacional de Colombia: Portal de Revistas UN
  • Document Type: article in journal/newspaper
  • File Description: application/pdf
  • Language: Spanish; Castilian
  • Relation: https://revistas.unal.edu.co/index.php/rfc/article/view/79512/73873; Agresti, Alan. (2002), Categorical Data Analysis Second Edition. John Wiley & Sons. New York, U.S.A. 721 p.; Balirano, Giuseppe & Corduas, Marcella. (2008). Detecting semiotically-expressed humor in diasporic TV productions. Humor-International Journal of Humor Research. 21(3), 227-251.; Bentler, Peter & Weeks, David. (1980). Linear structural equations with latent variables. Psychometrika, 45(3), 289-308.; Bishop, Christopher. (1998). Learning in graphical models: Latent variable models. Springer. 371-403.; Cerchiello, Paola; Iannario, Maria; Piccolo, Domenico. (2010). Assessing risk perception by means of ordinal models. Mathematical and Statistical Methods for Actuarial Sciences and Finance. 75-83.; Corduas, Marcella; Iannario, Maria; Piccolo, Domenico. (2009). A class of statistical models for evaluating services and performances. Statistical methods for the evaluation of educational services and quality of products. 99-117.; Deldossi, Laura and Zappa, Diego.(2011). Measurement errors and uncertainty: a statistical perspective. In: New Perspectives in Statistical Modeling and Data Analysis. Berlin, Heidelberg. 145-153; D'Elia, Angela. (2008). A statistical modelling approach for the analysis of TMD chronic pain data. Statistical Methods in Medical Research. 17(4), 389-403.; D'Elia, Angela & Piccolo, Domenico. (2005). A mixture model for preferences data analysis. Computational Statistics \& Data Analysis. 49(3), 917-934.; Dunn, P. & Smyth, G. (1996), Randomized quantile residuals. Journal of Computational and Graphical Statistics. 5(3), 236-244.; Gambacorta, Romina & Iannario, Maria. (2013). Measuring job satisfaction with \mbox{CUB} models. Labour. 27(2), 198-224.; Harrell, Frank. (2001). Regression modeling strategies: Ordinal logistic regression. Springer, New York. 331-343.; Iannario, Maria. (2008). A class of models for ordinal variables with covariates effects. Quaderni di Statistica. 10, 53-72.; Iannario, Maria & Piccolo, Domenico. (2009). A program in R for CUB models inference. Version 2.; Iannario, Maria & Piccolo, Domenico (2010). A New Statistical Model for the Analysis of Customer Satisfaction. Quality Technology & Quantitative Management. 7(2), 149-168.; Iannario, Maria; Manisera, Marica; Piccolo, Domenico & Zuccolotto, Paola. (2012). Sensory analysis in the food industry as a tool for marketing decisions. Advances in Data Analysis and classification. 6(4), 303-321.; McCullagh, Peter (1980). Regression models for ordinal data. Journal of the royal statistical society. Series B (Methodological). 42(2), 109-142.; McFadden, Daniel & et al. (1973). Conditional logit analysis of qualitative choice behavior. Institute of Urban and Regional Development, University of California Berkeley, CA; Piccolo, Domenico. (2006). Observed information matrix for MUB models. Quaderni di Statistica. 8, 33-78.; R Core Team. (2019). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/; So, Ying & Kuhfeld, F. (1995). Multinomial logit models in SUGI 20 Conference Proceedings. 1227-1234.; Stasinopoulos, Mikis; Rigby, Robert; Heller, Gillian; Voudouris, Vlasios; De Bastiani, Fernanda. (2017) Flexible Regression and Smoothing: Using GAMLSS in R. New York, U.S.A. 571 p.; https://revistas.unal.edu.co/index.php/rfc/article/view/79512
  • Rights: Derechos de autor 2020 Revista de la Facultad de Ciencias ; https://creativecommons.org/licenses/by-nc-nd/4.0

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