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List of Publications (Updated: March 26th, 2024)
151. Padilla*, J.L., Azevedo, C.L. and Lachos, V.H. (2024+). Parameter recovery for a skew multidimensional item response model: a comparison of MCMC algorithms and measurement of some effects of interest. Journal of Statistical Computation and Simulation (Under Review).
150. Jorge L. Bazán and V. H. Lachos (2024+). A new class of binary regression model for unbalanced data. Sankhya A (Under Review).
149. Ordonez, J.A, Galarza, C. E. and Lachos, V.H. (2024+). Spatial Censored Regression Models in R: The CensSpatial Package. Preprint arXiv:2110.05570 . SoftwareX (under review)
148. Medina, F., Garay, A.W. and Lachos, V.H. (2024+). Bayesian analysis of censored/missing regression models with autoregressive errors and symmetrical distributions. Statistical Papers (Under Review).
147. Schumacher, F. L., Lachos, V.H. and Matos, L. A. (2024+). Linear Mixed Models for Complex Longitudinal Data with Applications in R. SpringerBriefs in Statistics Series.
146. Zhong*, K. Olivari*, R.C. , Garay, A.M. and Lachos, V.H. (2024+). Mixed-effects models for censored data with autoregressive errors using the multivariate Student’s t-distribution. The New England Journal of Statistics in Data Science (Under Review).
145. Park*, J., Dey, D. and Lachos, V.H. (2024+). Finite mixture of regression models for censored data based on the skew-t distribution . Computational Statistics (In Press ).
144. Schumacher, F. L., Lachos, V.H. Castro, L.M.C. and Matos, L. A. (2024). A censored time series analysis for responses on the unit interval: An application to acid rain modeling . Sankhya A, 86 , 637–660.143. Ordoñez*, A.C., Prates, M.O. Matos, L.A. and Lachos, V.H. (2024). Objective Bayesian analysis for spatial Student-t regression models . Journal of Spatial Science, 69, 61-79 .
142. Ordoñez*, A.C., Prates, M.O. Bazan, J.L. and Lachos V.H. (2024). Penalized complexity priors for the skewness parameter of power links . The Canadian Journal of Statistics, 52(1), 98-117.
141. M.S. Oliveira, D.C.R. Oliveira & V.H. Lachos (2023). Influence diagnostics for skew-t censored linear regression models . Communications for Statistical Applications and Methods, 30(6), 605-629. 140. Benites*, L.S., Bolfarine, H. , Zeller, C.B. and Lachos, V.H. (2023). Regression modeling of censored data based on compound scale mixtures of normal distributions . Brazilian Journal of Probability and Statistics, 37(2), 282-312.
139. Lachos, V.H., Galea-Rojas, M. Prates, M. O. and Zeller, C.B. (2023). Likelihood inference-based for mixed-effects models using the generalized hyperbolic distribution . Stat – An ISI’s journal, 12:e602.
138. Gomes*, J. C., Aoki, R., Lachos, V. H., Paula, G. A. and Russo, C. M. (2023). Fast inference for robust nonlinear mixed-effects models. Journal of Applied Statistics, 50:7, 1568-1591.
137. Valeriano*, K.L., Galarza, C.E., Matos, L.A. and Lachos, V.H. (2023). Likelihood-based inference for multivariate skew-t censored regression models. Journal of Multivariate Analysis, 196, 105174.
136. da Paz*, R. Bazan, J.L., Lachos, V.H. and Dey, D. K. (2023). A finite mixture mixed proportion regression model for classification problems in Longitudinal voting data . Journal of Applied Statistics, 50,871-888.
135. de Alencar*, F.H., Matos, L.A. and Lachos, V.H. (2022). Finite mixture of censored linear mixed models for irregularly observed longitudinal data . Journal of Classification, 39 (3), 463 – 486 .
134. Ferreira* , C.S., Bolfarine, H. and Lachos, V.H. (2022). Linear mixed models based on skew scale mixtures of normal distribution . Communications in Statistics – Simulation and Computation, 51, 7194–7214.
133. B. Mattos* , L.A. Matos and V.H. Lachos (2022). Likelihood-based inference for mixed-effects models with censored response using skew-normal distribution . Springer International Publishing. Edited volume in ‘Innovations in multivariate statistical modeling: navigating theoretical and multidisciplinary domains”. 1st ed. 2022
132. De Alencar* F.H., Galarza, C.E. Matos, L.A. and Lachos V.H. (2022). Finite mixture modeling of censored and missing data using the multivariate skew-normal distribution . Advances and Data Analysis and Classification, 16, 521–557.
131. Mattos*, B. Matos, L.A., Castro, L.M. and Lachos, V.H. (2022). Extending multivariate-t semiparametric mixed models for longitudinal data with censored responses and heavy-tails . Statistics in Medicine, 41, 3696-3719.
130. Galarza* , C.E., Matos, L.A. and Lachos, V.H. (2022). An EM algorithm for estimating the parameters of the multivariate skew-normal distribution with censored responses . Metron, 80, 231–253.
129. Ordoñez, J., Galarza, C. E. and Lachos, V. H. (2022). An R package for censored spatial data analysis . Medwave . 22(S1): eCI54. DOI: 10.5867/Medwave.2022.S1.CI54. 128. Lachos, V.H., Bazan, J.L., Castro, L.M. and Park, J. (2022). The skew-t censored regression model: parameter estimation via an EM-type algorithm . Communications for Statistical Applications and Methods, 29, 1-29.
127. C.E. Galarza* , L.A. Matos, D.K. Dey & V.H. Lachos (2022). On moments of folded and truncated multivariate extended skew-normal distributions . Journal of Computational and Graphical Statistics, 31, 455-465.
126. Galarza* , C.E. , Matos, L.A. and Lachos, V.H. (2022). Moments of the doubly truncated selection elliptical distributions with an emphasis on the unified multivariate skew-t distribution . Journal of Multivariate Analysis, 189 . 104944.
125. Mattos* , B., Matos, L.A. and Lachos, V.H. (2021). A Semiparametric Mixed-Effects Model for Censored Longitudinal Data . Statistical Methods in Medical Research, 30, 2582–2603.
124. Ruggeri, F., Bolfarine, H., Bazan, J.L., Arellano-Valle, R.B., Lachos, V.H. and de Castro, M. (2021). 2021 ISI Mahalanobis Award: a Tribute to Heleno Bolfarine . International Statistical Review, 89, 435 – 446.
123. Galarza* , C. Lachos, V.H. and Panpan, Z. (2021). Logistic quantile regression for bounded outcomes using a new family of heavy-tailed distributions . Sankhya B, 83, 325–349.
122. Galarza*, C. E., Lin, T. I. , Wang, W. L. and Lachos, V. H. (2021). On moments of folded and truncated multivariate Student-t distributions based on recurrence relations . Metrika, 84, 825–850.
121. Galarza* , C.E., Bourguignon, M. and Lachos, V.H. (2021). A skew-t quantile regression modeling for censored and missing data, STAT, 10, 1-15 (e379).
120. R.C. Olivari* , A.M. Garay, V.H. Lachos and L.A. (2021). Autoregressive mixed-effects models for censored data . Journal of Biopharmaceutical Statistics , 31, 273-294.
119. Bandyopadhyay, D., Prates, M.O., Zhao*, X. and Lachos,V.H. (2021). Spatial skew-normal independent models for non-randomly missing clustered data . Statistics in Medicine, 40, 3085-3105.
118. Schumacher* , F.L. Dey, D.K. and Lachos, V.H. (2021). Approximate inferences for nonlinear mixed-effects models with scale mixtures of skew-normal distributions . Journal of Statistical Theory and Practice, 15, 60. (Part of a collection: Celebrating the Centenary of Professor C. R. Rao)
117. K.A.L. Valeriano* , L.A. Matos and V.H. Lachos (2021). Likelihood-based inference for spatio-temporal data with censored and missing responses . Environmetrics, 32, e2663.
116. V.H. Lachos, M.O. Prates and D.K. Dey (2021). Heckman selection-t model: parameter estimation via the EM-algorithm . Preprint arXiv:2006.08036. Journal of Multivariate Analysis, 184, 1-19.
115. Schumacher* , F. L., Lachos, V.H. and Matos, L.A. (2021). Scale mixture of skew-normal linear mixed models with within-subject serial dependence . Preprint arXiv :2002.01040. Statistics in Medicine, 40, 1790-1810.
114. Ye* , T. Lachos, V.H. , Wang X. and Dey,D.K. (2021). Comparisons of zero-augmented continuous regression models from a Bayesian perspective . Statistics in Medicine, 40, 1073-1100.
113. Nuñez* , M., Lachos, V.H. and Matos, L. (2021). Estimation and diagnostic for partially censored regression models based on heavy-tailed distributions . Statistics and its Interface, 14, 165 – 182 .
112. Schumacher* , F. L., Ferreira, C.S., Prates, M.O., Lachos, A. and Lachos, V.H (2021). A robust nonlinear mixed-effects model for COVID-19 death data . Statistics and its Interface, 14, 49 – 57. Preprint arXiv :2007.00848
111. Lachos, V.H., Cabral, CRB. and Garay* A.W.M. (2020). Moments of truncated scale mixtures of skew-normal distributions . Brazilian Journal of Probability and Statistics, 34, 478-494.
110. Ferreira, C.S, Lachos, V.H. and Garay, A.M. (2020). Inference and diagnostics for heteroscedastic nonlinear regression models under skew scale mixtures of normal distributions . Journal of Applied Statistics, 47, 1690–1719
109. Galarza*. C.E, Lachos, V.H., Castro, M.C. and Louzada, N.F. (2020). Quantile regression for nonlinear mixed effects models: A likelihood based perspective . Statistical Papers, 61, 1281-1307.
108. F. B. Goncalves, M. O. Prates and V. H. Lachos (2020). Robust Bayesian model selection for heavy-tailed linear regression models using mixtures . Brazilian Journal of Probability and Statistics, 34, 51-70.
107. Ramos*, P. L., Louzada, F., Dey, D. K. and Lachos, V. H. (2020). An Extended Poisson Family of Life Distribution: A Unified Approach in Competitive and Complementary Risks . Journal of Applied Statistics, 47, 306-322. 106. Lachos, V.H., Benites* , L.B. and Maehara* , CRB. (2019). Linear regression models using finite mixtures of skew heavy-tailed distributions . The Chilean Journal of Statistics , 10, 21-41.
105. Matos*, L.A., Castro, LM., V. H. Lachos and Lin, T-I (2019). Heavy-tailed longitudinal regression models for censored data: A robust parametric approach TEST, 28, 844-878.
104. Wang, W. L., Castro, L. M. Lachos, V. H. and Lin, T. I. (2019). Model-based clustering of censored data via mixtures of factor analyzers . Computational Statistics and Data Analysis, 140, 104-121.
103. Luis M. Castro, Wan-Lun Wang, Victor H. Lachos, Cristian L. Bayes and Vanda Inacio (2019). Bayesian semiparametric modeling for HIV longitudinal data with censoring and skewness .Statistical Methods in Medical Research, 28, 1457–1476.
102. Zeller, C.B., Cabral, C.R.B. and Lachos, V.H. (2019). Finite mixture of regression models for censored data based on scale mixtures of normal distributions . Advances in Data Analysis and Classification, 13, 89–116 .
101. Lachos, V. H., Prates, M.O., Cabral, C.R.B. and Dey, D. K. (2019). Robust regression modeling for censored data based on mixtures of student-t distributions . Computational Statistics, 34, 123–152.
100. Lachos, V. H., Matos*, L. A., Castro, L. M. and Chen, M-H. (2019). Flexible longitudinal linear mixed models for multiple censored responses data . Statistics in Medicine, 38, 1074-1102 .
99. Lachos, V.H., Cabral, C.R.B. and Zeller, C.B (2018). “FINITE MIXTURE OF SKEWED DISTRIBUTIONS”. SpringerBriefs in Statistics Series.
98. Matos*, L.A., Lachos, V.H., Cabral, C.R.B. and L. M. Castro (2018). Multivariate measurement error models based on the t-distribution with censored responses . Statistics: A Journal of Theoretical and Applied Statistics, 52, 1395-1416.
97. Wan-Lun Wang, Lachos, V.H. and Tsung-I Lin (2018). Multivariate longitudinal data analysis with censored and intermittent missing responses . Statistics in Medicine , 37, 2822-2835.
96. Matos*, T. B., Lachos, V.H. and Garay, A.W.M. (2018). Likelihood based inference for censored linear regression models with scale mixtures of skew-normal distributions . Journal of Applied Statistics, 45, 2039-2066.
95. Schumacher*, F. L., Lachos, V.H., Vilca-Labra, F.E. and Castro, L.M. (2018). Influence diagnostics for censored regression models with auto-regressive errors . Australian & New Zealand Journal of Statistics, 60, 209-229.
94. Padilla*, J.L., Azevedo, C.L. and Lachos, V.H. (2018). Multidimensional multiple group IRT models with skew normal latent trait distributions . Journal of Multivariate Analysis, 167, 250-268.
93. Wan-Lun Wang, Tsung-I Lin and Lachos, V.H. (2018). Extending multivariate-t linear mixed models for multiple longitudinal data with censored responses and heavy tails . Statistical Methods in Medical Research , 27, 48-64.
92. Ordoñez*, J. A., Lachos, V.H., Cabral, C.R.B, and Bandyopadhyay, D. (2018). Geo-statistical estimation and prediction for censored responses . Spatial Statistics, 23, 109-123.
91. Schumacher*, F. L., Lachos, V.H. and Dey, D.K. (2017). Censored regression models with autoregressive errors: A likelihood-based perspective . Canadian Journal of Statistics , 45, 375–392.
90. Lachos, V.H., Matos*, L.A., Barbosa*. T.S, Dey, DK. and Garay*, A.M. (2017). Influence diagnostics in spatial models with censored response . Environmetrics, 28, 1-21.
89. Lachos, V. H., Moreno*, E. L. Cabral, C.R. and Kun, C.(2017). Finite mixture modeling of censored data using the multivariate Student-t distribution Journal of Multivariate Analysis , 159, 151-167.
88. V. H. Lachos, Dey, D., Cancho, V.G. and Louzada, N. (2017) . Scale mixtures log-Birnbaum-S8unders regression models with censored data: a Bayesian approach . Journal of Statistical Computation and Simulation , 87, 2002-2022.
87. Galarza*, C.M., V. H. Lachos, Cabral, CRB. and Castro (2017). Robust Quantile Regression using a Generalized Class of Skewed Distributions . STAT , 113-130.
86. Galvis*, D. M., Bandyopadhyay, D and Lachos, V. H. (2017). Augmented mixed models for clustered proportion data . Statistical Methods in Medical Research , 26, 880–897.
85. Garay*, A. W, Castro, L.M., Leskow, L. and Lachos, V. H. (2017). Censored linear regression models for irregularly observed longitudinal data using the multivariate-t distribution Statistical Methods in Medical Research , 26, 542–566.
84. Massuia*, M.B., Garay*, AMG and V. H. Lachos and Cabral, C. R. (2017). Bayesian analysis of censored linear regression models with scale mixtures of skew-normal distributions Statistics and its Interface , 10, 425-439.
83. Galarza*, C.M., Bandyopadhyay, D. and V. H. Lachos (2017). Quantile regression for linear mixed models: A stochastic approximation EM approach . Statistics and its Interface , 10, 471-482.
82. Garay*, A. W, Lachos, V. H., Bolfarine, H and Cabral, C.R. (2017). Linear censored regression models with scale mixtures of normal distributions Statistical Papers , 58, 247–278.
81. Ferreira C.S. and Lachos, V. H.(2016). Nonlinear regression models under skew scale mixtures of normal distributions . Statistical Methodology , 33, 131–146.
80. Matos*, L. A, Lachos, V. H.and Castro, L.M. (2016). Censored mixed-effects models for irregularly observed repeated measures with applications to HIV viral loads TEST , 25, 627–653
79. Gonzalez*, J. A. C., Lachos, V. H., Castro, L.M. and Patriota, A. (2016). A Confidence Set Analysis for Observed Samples: A Fuzzy Set Approach . Entropy , 18, 211-220.
78. Blas*, B.G. Lachos, V. H. and Bolfarine, H. (2016). Heavy tailed calibration model with Berkson measurement errors for replicated data Chemometrics and Intelligent Laboratory Systems , 156, 21-35
77. Ferreira*, C. S., Lachos, V. H. and Bolfarine, H. (2016). Multivariate skew scale mixtures of normal distributions . Advances in Statistical Analysis , 100,421-441.
76. Zeller*, C. B, Lachos, V. H.and Cabral, C.R. (2016). Robust mixture regression modelling based on scale mixtures of skew-normal distributions TEST , 25, 375-396.
75. Garay*, A. W, Lachos, V. H.and Lin, Tsung-I (2016). Nonlinear censored regression models with scale mixtures of normal distributions . Statistics and its Interface ,9, 281 — 293.
74. Galarza*, C.M. and V. H. Lachos (2015). Likelihood based inference for quantile regression nonlinear mixed effects models . Estadistica, Inter-American Statistical Institute, 67, 33-74.
73. Garay*, A. W, Lachos, V. H., Bolfarine, H and Cabral, C.R. (2015). Bayesian analysis of censored linear regression models with scale mixtures of normal distributions . Journal of Applied Statistics , 42, 2694-2714.
72. Matos*, L. A., Bandyopadhyay, D., Castro, L. M. and Lachos, V. H. (2015). Influence diagnostics in mixed-effects models with censored data using the multivariate-t distribution . Journal of Multivariate Analysis , 141, 10-117.
71. Garay*, A. M., Lachos, V. H, and Bolfarine, H. (2015). Bayesian zero-inflated negative binomial regression models: estimation and case influence diagnostics . Journal of Applied Statistics , 42, 1148-1165.
70. Motta, M. R. , Galvis*, D. M., Lachos, V. H, and others (2015). A mixed-effect model for positive responses augmented by zeros . Statistics in Medicine, 34, 1761-1778.
69. Bandyopadhyay, D. Castro, L.M., Lachos, V. H. and Pinheiro, H. P. (2015). Joint nonlinear mixed-effects models and diagnostics for censored HIV viral loads with CD4 measurement error . Journal of Agricultural, Biological, and Environmental Statistics , 20, 121-139.
68. Costa*, D. R., Castro, L. M., Prates, M. and Lachos, V. H. (2015). Likelihood-based inference for Tobit confirmatory factor analysis using the multivariate t-distribution . Statistics and Computing , 25, 1163-1183.
67. Lachos*, V. H., Azevedo, C. L. N, Abanto-Valle, C. A. and Chen, M-H (2015). Quantile regression for censored mixed-effects models with applications to HIV studies . Statistics and its Interface , 8, 203-215.
66 . Massuia*, M. B., Cabral, M. O, Matos, L.A. and Lachos, V.H.(2015). Influence diagnostics for Student-t censored linear regression models . Statistics- A Journal of Theoretical and Applied Statistics , 49, 1074-1094.
65. Castro, L. M., Galvis*, D. M. and Lachos, V. H. (2015). Bayesian semiparametric linear mixed-effects models with normal/independent distributions . Chapman & Hall/CRC Press. Edited volume in -Current Trends in Bayesian Methodology with Applications-. Textbook – 516 Pages.
64. Abanto-Valle, C. A., Lachos, V. H, and Dey, D.(2015). Bayesian estimation of a skew-t stochastic volatility model . Methodology & Computing in Applied Probability , 17, 721-738.
63. Lachos*, V. H, and Labra, F. V. (2014). Multivariate skew-normal/independent distributions: properties and inference . Pro Mathematica , 28, 11–53.
62. Costa*, D. R, Lachos, V. H. Bazan, J. L. and Azevedo, C. L. N (2014). E stimation methods for multivariate Tobit confirmatory factor analysis . Computational Statistics and Data Analysis, 79, 248-260.
61. Galvis*, D. M, Bandyopadhyay, D. and Lachos, V. H. (2014). Augmented mixed beta regression models for periodontal proportion data . Statistics in Medicine , 33, 3759-3771.
60. Garay*, A. M., Lachos, V. H., Vilca, L. F. and Ortega, E. M. (2014). Statistical diagnostics for nonlinear regression models based on scale mixtures of skew-normal distributions . Journal of Statistical Computation and Simulation , 84, 1761-1778.
59. Costa*, D. R, Lachos, V. H. and Prates, M.O. (2014). Generalized linear mixed models for correlated binary data with T-link Statistics and Computing , 24,1111-1123.
58. Zeller*, C. B., Lachos, V. H., Vilca, L. F. (2014). On estimation and influence diagnostics for the Grubbs’ model with asymmetric heavy-tailed distributions Statistical Papers, 55, 671-690.
57. Ferreira*, C. S, Lachos, V. H., and Bolfarine, H. (2014). Inference and diagnostics in skew scale mixtures of normal regression models . Journal of Statistical Computation and Simulation , 85, 517-537.
56. Cabral, C. R., Lachos, V. H, and Zeller*, C.B.(2014). Multivariate measurement error models using finite mixtures of skew-student t distributions . Journal of Multivariate Analysis , 124, 179-198.
55. Castro, L. M., Lachos, V. H, Ferreira, G. and Arellano-Valle, R. (2014). Partially linear censored regression models using heavy-tailed distributions: A Bayesian approach Statistical Methodology , 18, 14-31.
54. Ferreira, G., Castro, L. M., Lachos, V. H, and Dias, R. (2013). Bayesian modeling of autoregressive partial linear models with scale mixture of normal errors . Journal of Applied Statistics , 40, 1796-1816.
53. Prates*, M. O., Cabral, M. O, and Lachos, V.H.(2013). Fitting finite mixture of scale mixture of skew-normal distributions Journal of Statistical Software , 54, 1-20.
52. Lachos, V. H. Castro, L. M. and Dey, D.K. (2013). Bayesian inference in nonlinear mixed-effects models using normal independent distributions Computational Statistics and Data Analysis , 64, 237-252.
51. Matos*, L. A., Lachos, V. H,, Balakrishnan, N. and Vilca-Labra, F.(2013). Influence diagnostics in linear and nonlinear mixed-effects models with censored data Computational Statistics and Data Analysis , 57, 450-464.
50. Matos*, L. A., Prates*, M. O., Chen, M-H. and Lachos, V. H. (2013). Likelihood based inference for linear and nonlinear mixed-effects models with censored response using the multivariate-t distribution Statistica Sinica , 23, 1299-1322.
49. Blás*, B.G. Lachos, V. H. and Bolfarine, H. (2013). Statistical analysis of controlled calibration model with replicates Journal of Statistical Computation and Simulation , 83, 941-961.
48. Abanto-Valle, C. A., Lachos, V.H. and Ghosh, P. (2012). A Bayesian term structure modeling using heavy-tailed distributions . Applied Stochastic Models in Business and Industry , 28, 430-447.
47. Prates*, M. O., Dey, D. K, and Lachos, V.H. (2012). A dengue fever study in the state of Rio de Janeiro with the use of generalized skew-normal/independent spatial fields. The Chilean Journal of Statistics , 3, 33-45.
46. Zeller*, C. B., Carvalho*, R. R, and Lachos, V. H. (2012). On diagnostics for multivariate measurement error model with asymmetric heavy-tailed distributions . Statistical Papers , 53, 665-683.
45. Abanto-Valle, C. A., Migon, H. S. and Lachos, V. H.(2012). Stochastic volatility in mean models with heavy-tailed distributions . Brazilian Journal of Probability and Statistics , 26, 402-422.
44. Lachos, V.H. , Bandyopadhyay, D., Castro, L.M.C and Dey, D. K.(2012). Skew-normal/independent linear mixed models for censored responses with applications to HIV viral loads . Biometrical Journal , 405-425, 2012.
43. Lachos, V. H., Garay*, A. M. , Ortega, E. M. and Vilca, L. F. (2012). Estimation and diagnostics for heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions . Journal of Statistical Planning and Inference , 142, 2149-2165.
42. Barbosa-Cabral, C. R., Lachos, V. H. and Madruga, R. M. (2012). Bayesian skew-normal independent linear mixed models with heterogeneity in the random-effects population . Journal of Statistical Planning and Inference , 212, 181-200.
41. Cabral, C. R., Lachos, V. H, and Prates*, M.O (2012). Robust multivariate mixture modelling using scale mixtures of skew-normal distributions . Computational Statistics and Data Analysis , 56, 226-246.
40. Lachos, V. H., Cabral, C. R. and Abanto-Valle, C. A. (2012). A noniterative sampling Bayesian method for linear mixed models with normal independent distributions . Journal of Applied Statistics , 39, 531-549.
39. Lachos, V.H., Bandyopadhyay D. and Dey D. K. (2011) Linear and non-linear mixed-effects models for censored HIV viral loads using normal/independent distributions . Biometrics , 55, 1304-1318.
38. Lachos, V. H., Bandyopadhyay, D. and A. M. Garay* (2011). Heteroscedastic nonlinear regression models based on scale mixtures of skew normal distributions . Statistics and Probability Letter , 81, 1208-1217.
37. Garay*, A. M., Ortega, E. M., and Lachos, V. H. (2011). On estimation and influence diagnostics for zero-inflated negative binomial regression models . Computational Statistics and Data Analysis , 55, 1304-1318
36. Zeller*, C. B., Lachos, V. H, and Labra, F. (2011). Local influence analysis for regression models with skew-normal independent distributions . Journal of Applied Statistics , 38, 343 – 368.
35. Abanto-Valle, C. A., Migon, H. and Lachos, V.H. (2011). Bayesian analysis of heavy-tailed stochastic volatility in mean model using scale mixtures of normal distributions Journal of Statistical Planning and Inference , 141, 1875-1887.
34. Ferreira*, C. S., Lachos, V. H. and Bolfarine, H. (2011). Skew scale mixtures of normal distributions: properties and estimation . Statistical Methodology , 8, 154-171.
33. Lachos, V. H., Abanto-Valle, C. A. and Angolini*, T (2011). On estimation and local influence analysis for measurement errors models under heavy-tailed distributions Statistical Papers , 52, 567-590.
32. Garay*, A. M., Lachos, V. H, and Abanto-Valle, C.A. (2011). Nonlinear regression models based on scale mixtures of skew-normal distributions Journal of the Korean Statistical Society , 40, 115-124.
31. Lachos, V.H., Filidor, V.L., Garibay, V.C. and Aoky R. (2011). Skew-normal distribution in multivariate null intercept measurement error models . Brazilian Journal of Probability and Statistics , 25, 145-170.
30. Lachos, V. H., Ghosh, P. and Arellano-Valle R. B. (2010). Likelihood based inference for skew-normal/independent linear mixed model . Statistica Sinica , 20, 303-322.
29. Lachos, V.H., Bolfarine H., Vilca, L.F. and Ghosh, P. (2010). Robust multivariate measurement error models with scale mixtures of skew-normal distributions . Statistics (A Journal of Theoretical and Applied Statistics) , 44, 541-556.
28. Abanto-Valle, C. A., Bandyopadhyay, D, Lachos, V. H. (2010). Robust Bayesian analysis of heavy-tailed stochastic volatility models using scale mixtures of normal distributions Computational Statistics and Data Analysis , 54, 2883-2898.
27. Bandyopadhyay, D, Lachos, V. H. Abanto-Valle, C. A. (2010). Bayesian inference for bivariate skew-normal/independent linear mixed models with application to periodontal disease . Statistics in Medicine , 29, 2643-2655.
26. Zeller*, C. B., Lachos, V. H., Vilca, L. F. and Balakrishnan, N. (2010). Influence analyses of skew-normal/independent linear mixed models . Computational Statistics and Data Analysis , 54, 1266-1280.
25. Basso*, R., Lachos, V. H, and Cabral, C.R.B (2010). Robust mixture modelling using scale mixtures of skew-normal distributions . Computational Statistics and Data Analysis , 54, 2926-2941.
24. Cancho, V. G., Dey, K. D., Lachos, V. H. and Andrade, M. (2010). Bayesian nonlinear regression models with scale mixtures of skew normal distributions: Estimation and case influence diagnostics . Computational Statistics and Data Analysis , 55, 588-602.
23. Garibay, V. Aoki, R. and Lachos, V.H. (2010). Bayesian analysis for skew-t multivariate null intercept measurement error models . Statistical Papers , 51,531-545.
22. Garibay, V. Ortega, E. and Lachos, V.H.(2010). Skew-normal comparative calibration models . Journal of Statistical Theory and Applications , 9, 143-168.
21. Lachos, V. H., Garibay, V. and Ortega, E. (2010). A nonlinear model with skew-normal errors . Statistical Papers , 51, 547-558.
20. Lachos, V.H., Bolfarine H. and Montenegro*, L.C. (2010). Inference for a Skew Extension of the Grubbs Model . Statistical Papers, 51, 701-715.
19. Montenegro*, L.C, Lachos, V.H. and Bolfarine H. (2009). Local influence analysis of skew-normal linear mixed models . Communication in Statistics- Theory and Methods , 38, 484-496.
18. Montenegro*, L.C., Bolfarine, H. and Lachos, V.H. (2009). Influence diagnostics for a skew extension of the Grubbs model . Communication in Statistics- Simulation and Computation , 38, 667-681.
17. Lachos, V. H., Dey, K. D. and Cancho, V. G. (2009). Robust linear mixed models with skew-normal independent distributions from a Bayesian perspective. Journal of Statistical Planning and Inference, 139, 4098-4110.
16. Ghosh, P., Bayes*, C. R. and Lachos, V. H. (2009). A robust Bayesian approach to null inter cept measurement error model with application to dental data . Computational Statistics and Data Analysis , 53, 1066-1079.
15. Lachos, V. H., Vilca, F. L,, Garibay, V. and Aoki, R. (2009). Robust multivariate measurement error Model with skew-normal/independent distributions and Bayesian MCMC implementation . Statistical Methodology , 6, 527-541
14. Ortega, E. M., Garibay, V., and Lachos, V. H. (2009). A generalized log-gamma mixture model with long-term survivors: sensitivity analysis . Sankhya – Indian Statistical Institute , Series B, 71, 1-29.
13. Lachos, V.H., Bolfarine H. e Montenegro*, L.C (2008). Inference and assessment of local influence in skew-normal null intercept measurement error models . Journal of Statistical Computation and Simulation , 78, 395-419.
12. Ortega, E., Garibay, V. and Lachos, V.H. (2008). Influence diagnostics in the Weibull mixture model with covariates. Statistics and Operations Research Transactions (SORT), 115-140, 2008.
11. Aoki, R. Garibay, V. and Lachos, V.H. (2008). Bayesian analysis for a skew extension of the multivariate null intercept measurement error model . Journal of Applied Statistics , 35, 1239-1251.
10. Lachos, V. H. (2008). Scale mixtures of skew-normal distribution with applications in regression models . Estadistica (Inter-American Statistical Institute), 60, 1-28.
9. Lachos, V.H., Bolfarine H. and Montenegro*, L.C. (2007). Influence diagnostics for skew-normal linear mixed models Sankhya – Indian Statistical Institute , 69, 648-670.
8. Lachos, V.H., Vilca, L.F. and Galea, M. (2007). Influence diagnostics for Grubbs’s model . Statistical Papers , 48, 419-436.
7. Lachos, V.H. and Bolfarine H. (2007). Skew-probit measurement error models . Statistical Methodology , 4, 1-12.
6. Lachos, V.H., Bolfarine H., Arellano-Valle, R.B. and Montenegro*, L.C. (2007). Likelihood based inference for multivariate skew-normal regression models . Communication in Statistics: Theory and methods (Special Issue in skew distributions), 36, 1769-1786.
5. Arellano-Valle, R.B., Bolfarine, H. and Lachos, V.H. (2007). Bayesian inference for skew-normal linear mixed models . Journal of Applied Statistics , 34, 663-682.
4. Lachos, V.H. and Bolfarine H. (2006). Skew binary regression with measurement errors . Statistics (A Journal of Theoretical and Applied Statistics), 40, 485-494.
3. Arellano-Valle, R.B., Ozan, S., Bolfarine, H. and Lachos, V.H. (2005). Skew-normal measurement error models . Journal of Multivariate Analysis , 96, 93-116.
2. Arellano-Valle, R.B., Bolfarine, H. and Lachos, V.H. (2005). Skew-normal linear mixed models . Journal of Data Science , 4, 415-438.
1. Lachos, V.H. (2004). Modelos Lineares Mistos Assimétricos. Ph.D. Thesis, Inst. of Mathematics and Statistics, University of São Paulo, Brazil, 160 p. (Wednesday, 10/27/2004).
Packages for R
mixsmsn : Fitting finite mixture of scale mixture of skew-normal distributions (2010)tlmec : Linear Student-t Mixed-Effects Models with Censored Data (2011)nlsmsn : Fitting univariate non-linear scale mixture of skew-normal regression models. (2012)CensRegMod : Fitting Normal and Student-t censored regression models. (2012)SMNCensReg : Fitting univariate censored regression model under the scale mixture of normal distributions. (2013)ALDqr : Quantile Regression Using Asymmetric Laplace Distribution. (2013)BayesCR : Bayesian analysis of censored linear regression models with scale mixtures of normal (SMN) distributions (2013)qrLMM : Quantile Regression for Linear Mixed-Effects Models (2015)ald : The Asymmetric Laplace Distribution (2015)CensMixReg : Censored Linear Mixture Regression Models (2015)lqr : Robust Linear Quantile Regression (2016)FMsmsnReg : Regression Models with Finite Mixtures of Skew Heavy-Tailed Errors (2016)ARCensReg : Fitting Univariate Censored Linear Regression Model with Autoregressive Errors (2016)CensSpatial : Censored Spatial Models (2016)MomTrunc : Moments of Folded and Doubly Truncated Multivariate Distributions (2018)PartCensReg : Partially Censored Regression Models Based on Heavy-Tailed Distributions (2018)StempCens : Spatio-Temporal Estimation and Prediction for Censored/Missing Responses (2019)CensMFM : Finite Mixture of Multivariate Censored/Missing Data (2019)skewlmm : Scale Mixture of Skew-Normal Linear Mixed Models (2020)OBASpatial : Objective Bayesian Analysis for Spatial Regression Models (2020)
Submitted/in Progress
Zhong, K., Zhang P., Castro, L.M., and Lachos, V.H. (2024). Bayesian analysis of autoregressive linear mixed models for censored responses using the multivariate t distribution. (In Progress)
R. Retnam, S. Srivastava, D. Bandyopadhyay, and V.H. Lachos (2024). A divide-and-conquer EM algorithm for large non-Gaussian longitudinal data with irregular follow-ups. (In Progress)
K.S. Conceição, M.G. Andrade, V.H. Lachos & N. Ravishanker (2024). k-Modified Distributions for Count Data. (In Progress) Fusheng Yang and V.H. Lachos (2024). Comparison of Zero-Inflated and Hurdle INAR(1) Processes for Modeling Count Data (In progress)
Galarza, C. and Lachos, V.H. (2024). Finite mixture modeling of censored and missing data using the multivariate skew-t distribution. (In progress)
D.C.R. Oliveira, D. Liu & V.H. Lachos (2024). The use of the EM algorithm for regularization problems in high-dimensional censored linear mixed-effects models. (In progress). F.L. Schumacher, L.A. Matos & V.H. Lachos (2023). skewlmm: An R Package for Fitting Skewed and Heavy-Tailed Linear Mixed Models. (submitted) M.S. Oliveira, C. Galarza, M.O. Prates & V.H. Lachos (2023). Influence Diagnostics for Heckman selection-t Models. (Submitted). D. Liu, D.C.R. Oliveira, V.H. Lachos and L.M. Castro (2023). Lasso regularization for censored regression and high dimensional predictors. (Submitted). V.H. Lachos, S.D. Tomarchio, A. Punzo & S. Ingrassia (2024). On Matrix-Variate Normal Distribution for Interval-Censored and Missing Data (submitted) Brisilda Nbreka, D. Dey and VH Lachos (2024). Bayesian Estimation of Contagion Effect: An Application of Friendship Networks and Alcohol Behavior (Submitted).
N. Jerez-Lillo, A. Tapia, V.H. Lachos & P.L. Ramos (2024). A New Semi-Parametric Power-Law Regression Model with Long-Term Survival, Change Point Detection and Regularization (submitted) D.C.R. Oliveira, F. Schumacher & V.H. Lachos (2024). The use of the EM algorithm for regularization problems in high-dimensional linear mixed-effects models. (Submitted). Victor H. Lachos, José Alejandro Ordoñez, Heeju Lim (2024). Heckman selection – contaminated normal model. (In progress)