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References
- Arellano-Valle, R. B., Bolfarine, H., and Lachos, V. H., Skew-normal linear mixed models,
- J. Data Sci., 3 (2005), 415-438.
- Arellano-Valle, R. B., Bolfarine, H., and Lachos, V. H., Bayesian inference for Skew-normal
- linear mixed models, J. Appl. Stat., 3 (2007), 663-682.
- Bandyopadhyay, D., Lachos, V. H., Castro, L. M., and Dey, D., "Skew-normal/independent
- linear mixed models for censored responses with applications to HIV viral loads", Biom. J.
- (2012), 405-425.
- Bandyopadhyay, D., Castro, L. M., Lachos, V. H., and Pinheiro, H. P., "Robust joint non-
- linear mixed-eects models and diagnostics for censored HIV viral loads with CD4 measure-
- ment error", J. Agric. Biol. Environ. Stat. 20 (2015), 121-139.
- Boyer, C.N., Larson, J.A., Roberts, R.K., McClure, A.T., Tyler, D.D., and Zhou, V., Sto-
- chastic corn yield response functions to nitrogen for corn after corn, corn after cotton, and
- corn after soybeans, J. Agric. Appl. Econ., 45 (2013), 669-681.
- Brorsen, B.W., Using Bayesian estimation and decision theory to determine the optimal level
- of nitrogen in cotton, Selected Paper, Southern Agricultural Economics Association, Orlando,
- Florida, (2013).
- de Souza, F. A., Malheiros, E. B., and Carneiro, P. R. O., Positioning and number of nutri-
- tional levels in dose-response trials to estimate the optimal-level and the adjustment of the
- models, Cincia Rural, Santa Maria, 44 (2014), 1204-1209.
- Gelman, A., Carlin J. B., Stern H. S., Dunson D. B., Vehtari A, and Rubin, D.B., Bayesian
- Data Analysis, Chapman Hall/CRC, New York, 2014.
- Hagenbuch, N., A comparison of four methods to analyse a non-linear mixed-eects model
- using simulated pharmacokinetic data, Master Thesis, Department of Mathematics, Swiss
- Federal Institute of Technology Zurich, 2011.
- Harring, J. R. and Liu J., A comparison of estimation methods for nonlinear mixed eects
- models under model misspecication and data sparseness: a simulation study, Journal of
- Modern Applied Statistical Methods, 15 (2016), 539-569.
- Jara, A., Quintana, F., San Martin, E., Linear mixed models with skew-elliptical distribu-
- tions: a Bayesian approach, Comput. Statist. Data Anal., 52 (2008), 5033-5045.
- Kery, M., Introduction to WinBUGS for Ecologists: A Bayesian approach to regression,
- ANOVA, mixed models and related analyses, Academic Press, Amsterdam, The Netherlands,
- Lachos, V. H., Dey, D. K., Cancho, V. G., Robust linear mixed models with skew-normal
- independent distributions from a Bayesian perspective, J. Statist. Plann. Inference, 139
- (2009), 4098-4110.
- Lachos, V. H., Ghosh P., and Arellano-Valle, R. B., Likelihood based inference for skew-
- normal independent linear mixed models, Statist. Sinica., 20 (2010), 303-322.
- Lachos, V. H., Castro L. M., and Dey D. K., Bayesian inference in nonlinear mixed-eects
- models using normal independent distributions, Comput. Statist. Data Anal., 64 (2013),
- -252.
- Lange, K. and Sinsheimer, J., Normal/independent distributions and their applications in
- robust regression, J. Comput. Graph. Stat., 2 (1993), 175-198.
- Lopez-Bellido, R.J., Castillo, J. E., and Lopez-Bellido, L., Comparative response of bread
- and durum wheat cultivars to nitrogen fertilizer in a rainfed Mediterranean environment: soil
- nitrate and N uptake and eciency, Nutr. Cycl. Agroecosyst., 80 (2008), 121-130.
- Makowski, D., Wallach, D., and Meynard, J.M., Statistical methods for predicting the re-
- sponses to applied N and for calculating optimal N rates, Agron. J., 93 (2001), 531-539.
- Makowski, D. and Wallach, D., It pays to base parameter estimation on a realistic description
- of model errors, Agronomie, 22 (2002), 179-189.
- Makowski, D. and Lavielle, M., Using SAEM to estimate parameters of response to applied
- fertilizer, J. Agric. Biol. Environ. Stat., 11 (2006), 45-60.
- Ouedraogo, F. B. and Brorsen, B. W., Bayesian estimation of optimal nitrogen rates with a
- non-normally distributed stochastic plateau function, The Southern Agricultural Economics
- Association (SAEA) Annual Meeting, Dallas, Texas, 2014.
- Park, S.C., Brorsen, B.W., Stoecker, A.L. and Hattey, J.A., Forage response to swine euent:
- a Cox nonnested test of alternative functional forms using a fast double bootstrap, J. Agr.
- Appl. Econ., 44 (2012), 593-606.
- Pinheiro, J., Bornkamp, B., Glimm, E., and Bretz, F., Model-based dose nding under model
- uncertainty using general parametric models, Stat. Med., 33 (2014), 1646-1661.
- Plan, E. L., Maloney, A., Mentr, F., Karlsson, M. O., and Bertrand J., Performance compar-
- ison of various maximum likelihood nonlinear mixed-eects estimation methods for dosere-
- sponse models, The AAPS J., 14 (2012), 420-432.
- Plummer, M., JAGS: A program for analysis of Bayesian graphical models using Gibbs sam-
- pling, Proceedings of the 3rd International Workshop on Distributed Statistical Computing,
- Vienna, Austria, 2003.
- Rizzo, M. L., Statistical Computing with R, Chapman Hall/CRC, New York, 2008.
- Rosa, G. J. M., Padovani, C. R., and Gianola, D., Robust linear mixed models with nor-
- mal/independent distributions and Bayesian MCMC implementation, Biom. J., 45 (2003),
- -590.
- Sain, G. E., and Jauregui, M.A., Deriving fertilizer recommendations with a
- exible functional
- form, Agron. J., 85 (1993), 934-937.
- Spiegelhalter, D. J., Best, N. G., Carlin, B. P., and van der Linde, A., Bayesian measures of
- model complexity and t, J. R. Stat. Soc. Ser. B. Stat. Methodol., 64 (2002), 583-639.
- Spiegelhalter, D. J., Best, N. G., Carlin, B. P., and van der Linde, A., The deviance infor-
- mation criterion: 12 years on, J. R. Stat. Soc. Ser. B. Stat. Methodol., 76 (2014), 485-493.
- Su, Y.S., and Yajima, M., R2jags: A package for running jags from R, R package version
- 5-7, 2015.
- Tanner, M. A., Tools for Statistical Inference: Methods for the Exploration of Posterior
- Distributions and Likelihood Functions, Springer-Verlag New York, 1993.
- Tembo, G., Brorsen, B. W., Epplin, F. M., and Tostao, E., Crop input response functions
- with stochastic plateaus, Amer. J. Agr. Econ., 90 (2008), 424-434.
- Tumusiime, E., Brorsen, B.W., Mosali, J., Johnson, J., Locke, J. and Biermacher, J.T.,
- Determining optimal levels of nitrogen fertilizer using random parameter models, J. Agr.
- Appl. Econ., 43 (2011), 541-552.
- Wallach, D., Regional optimization of fertilization using a hierarchical linear model, Biomet-
- rics, 51 (1995), 338-346.
- Ward, E. J., A review and comparison of four commonly used Bayesian and maximum like-
- lihood model selection tools, Ecological Modelling, 211 (2008), 110.
- WHO, Principles for Modelling Dose-Response For The Risk Assessment of Chemicals,
- WHO Press, World Health Organization, Geneva, Switzerland, 2009.
References
Arellano-Valle, R. B., Bolfarine, H., and Lachos, V. H., Skew-normal linear mixed models,
J. Data Sci., 3 (2005), 415-438.
Arellano-Valle, R. B., Bolfarine, H., and Lachos, V. H., Bayesian inference for Skew-normal
linear mixed models, J. Appl. Stat., 3 (2007), 663-682.
Bandyopadhyay, D., Lachos, V. H., Castro, L. M., and Dey, D., "Skew-normal/independent
linear mixed models for censored responses with applications to HIV viral loads", Biom. J.
(2012), 405-425.
Bandyopadhyay, D., Castro, L. M., Lachos, V. H., and Pinheiro, H. P., "Robust joint non-
linear mixed-eects models and diagnostics for censored HIV viral loads with CD4 measure-
ment error", J. Agric. Biol. Environ. Stat. 20 (2015), 121-139.
Boyer, C.N., Larson, J.A., Roberts, R.K., McClure, A.T., Tyler, D.D., and Zhou, V., Sto-
chastic corn yield response functions to nitrogen for corn after corn, corn after cotton, and
corn after soybeans, J. Agric. Appl. Econ., 45 (2013), 669-681.
Brorsen, B.W., Using Bayesian estimation and decision theory to determine the optimal level
of nitrogen in cotton, Selected Paper, Southern Agricultural Economics Association, Orlando,
Florida, (2013).
de Souza, F. A., Malheiros, E. B., and Carneiro, P. R. O., Positioning and number of nutri-
tional levels in dose-response trials to estimate the optimal-level and the adjustment of the
models, Cincia Rural, Santa Maria, 44 (2014), 1204-1209.
Gelman, A., Carlin J. B., Stern H. S., Dunson D. B., Vehtari A, and Rubin, D.B., Bayesian
Data Analysis, Chapman Hall/CRC, New York, 2014.
Hagenbuch, N., A comparison of four methods to analyse a non-linear mixed-eects model
using simulated pharmacokinetic data, Master Thesis, Department of Mathematics, Swiss
Federal Institute of Technology Zurich, 2011.
Harring, J. R. and Liu J., A comparison of estimation methods for nonlinear mixed eects
models under model misspecication and data sparseness: a simulation study, Journal of
Modern Applied Statistical Methods, 15 (2016), 539-569.
Jara, A., Quintana, F., San Martin, E., Linear mixed models with skew-elliptical distribu-
tions: a Bayesian approach, Comput. Statist. Data Anal., 52 (2008), 5033-5045.
Kery, M., Introduction to WinBUGS for Ecologists: A Bayesian approach to regression,
ANOVA, mixed models and related analyses, Academic Press, Amsterdam, The Netherlands,
Lachos, V. H., Dey, D. K., Cancho, V. G., Robust linear mixed models with skew-normal
independent distributions from a Bayesian perspective, J. Statist. Plann. Inference, 139
(2009), 4098-4110.
Lachos, V. H., Ghosh P., and Arellano-Valle, R. B., Likelihood based inference for skew-
normal independent linear mixed models, Statist. Sinica., 20 (2010), 303-322.
Lachos, V. H., Castro L. M., and Dey D. K., Bayesian inference in nonlinear mixed-eects
models using normal independent distributions, Comput. Statist. Data Anal., 64 (2013),
-252.
Lange, K. and Sinsheimer, J., Normal/independent distributions and their applications in
robust regression, J. Comput. Graph. Stat., 2 (1993), 175-198.
Lopez-Bellido, R.J., Castillo, J. E., and Lopez-Bellido, L., Comparative response of bread
and durum wheat cultivars to nitrogen fertilizer in a rainfed Mediterranean environment: soil
nitrate and N uptake and eciency, Nutr. Cycl. Agroecosyst., 80 (2008), 121-130.
Makowski, D., Wallach, D., and Meynard, J.M., Statistical methods for predicting the re-
sponses to applied N and for calculating optimal N rates, Agron. J., 93 (2001), 531-539.
Makowski, D. and Wallach, D., It pays to base parameter estimation on a realistic description
of model errors, Agronomie, 22 (2002), 179-189.
Makowski, D. and Lavielle, M., Using SAEM to estimate parameters of response to applied
fertilizer, J. Agric. Biol. Environ. Stat., 11 (2006), 45-60.
Ouedraogo, F. B. and Brorsen, B. W., Bayesian estimation of optimal nitrogen rates with a
non-normally distributed stochastic plateau function, The Southern Agricultural Economics
Association (SAEA) Annual Meeting, Dallas, Texas, 2014.
Park, S.C., Brorsen, B.W., Stoecker, A.L. and Hattey, J.A., Forage response to swine euent:
a Cox nonnested test of alternative functional forms using a fast double bootstrap, J. Agr.
Appl. Econ., 44 (2012), 593-606.
Pinheiro, J., Bornkamp, B., Glimm, E., and Bretz, F., Model-based dose nding under model
uncertainty using general parametric models, Stat. Med., 33 (2014), 1646-1661.
Plan, E. L., Maloney, A., Mentr, F., Karlsson, M. O., and Bertrand J., Performance compar-
ison of various maximum likelihood nonlinear mixed-eects estimation methods for dosere-
sponse models, The AAPS J., 14 (2012), 420-432.
Plummer, M., JAGS: A program for analysis of Bayesian graphical models using Gibbs sam-
pling, Proceedings of the 3rd International Workshop on Distributed Statistical Computing,
Vienna, Austria, 2003.
Rizzo, M. L., Statistical Computing with R, Chapman Hall/CRC, New York, 2008.
Rosa, G. J. M., Padovani, C. R., and Gianola, D., Robust linear mixed models with nor-
mal/independent distributions and Bayesian MCMC implementation, Biom. J., 45 (2003),
-590.
Sain, G. E., and Jauregui, M.A., Deriving fertilizer recommendations with a
exible functional
form, Agron. J., 85 (1993), 934-937.
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., and van der Linde, A., Bayesian measures of
model complexity and t, J. R. Stat. Soc. Ser. B. Stat. Methodol., 64 (2002), 583-639.
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., and van der Linde, A., The deviance infor-
mation criterion: 12 years on, J. R. Stat. Soc. Ser. B. Stat. Methodol., 76 (2014), 485-493.
Su, Y.S., and Yajima, M., R2jags: A package for running jags from R, R package version
5-7, 2015.
Tanner, M. A., Tools for Statistical Inference: Methods for the Exploration of Posterior
Distributions and Likelihood Functions, Springer-Verlag New York, 1993.
Tembo, G., Brorsen, B. W., Epplin, F. M., and Tostao, E., Crop input response functions
with stochastic plateaus, Amer. J. Agr. Econ., 90 (2008), 424-434.
Tumusiime, E., Brorsen, B.W., Mosali, J., Johnson, J., Locke, J. and Biermacher, J.T.,
Determining optimal levels of nitrogen fertilizer using random parameter models, J. Agr.
Appl. Econ., 43 (2011), 541-552.
Wallach, D., Regional optimization of fertilization using a hierarchical linear model, Biomet-
rics, 51 (1995), 338-346.
Ward, E. J., A review and comparison of four commonly used Bayesian and maximum like-
lihood model selection tools, Ecological Modelling, 211 (2008), 110.
WHO, Principles for Modelling Dose-Response For The Risk Assessment of Chemicals,
WHO Press, World Health Organization, Geneva, Switzerland, 2009.