PROC NLMIXED

The NLMIXED procedure fits nonlinear mixed models—that is, models in which both fixed and random effects enter nonlinearly. These models have a wide variety of applications, two of the most common being pharmacokinetics and overdispersed binomial data. PROC NLMIXED enables you to specify a conditional distribution for your data (given the random effects) having either a standard form (normal, binomial, Poisson) or a general distribution that you code using SAS programming statements. PROC NLMIXED fits nonlinear mixed models by maximizing an approximation to the likelihood integrated over the random effects. Different integral approximations are available, the principal ones being adaptive Gaussian quadrature and a first-order Taylor series approximation. A variety of alternative optimization techniques are available to carry out the maximization; the default is a dual quasi-Newton algorithm. Successful convergence of the optimization problem results in parameter estimates along with their approximate standard errors based on the second derivative matrix of the likelihood function. PROC NLMIXED enables you to use the estimated model to construct predictions of arbitrary functions by using empirical Bayes estimates of the random effects. You can also estimate arbitrary functions of the nonrandom parameters, and PROC NLMIXED computes their approximate standard errors by using the delta method.


References in zbMATH (referenced in 36 articles )

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  1. Dey, Sanku; Mazucheli, Josmar; Anis, M.Z.: Estimation of reliability of multicomponent stress-strength for a Kumaraswamy distribution (2017)
  2. Sellers, Kimberly F.; Morris, Darcy S.: Underdispersion models: models that are “under the radar” (2017)
  3. Su, Xiao; Luo, Sheng: Analysis of censored longitudinal data with skewness and a terminal event (2017)
  4. Commenges, Daniel; Jacqmin-Gadda, Hélène: Dynamical biostatistical models (2016)
  5. Jin, Ying; Kang, Minsoo: Comparing DIF methods for data with dual dependency (2016) MathEduc
  6. Liu, Lei; Huang, Xuelin; Yaroshinsky, Alex; Cormier, Janice N.: Joint frailty models for zero-inflated recurrent events in the presence of a terminal event (2016)
  7. Carroll, Raymond J.: Estimating the distribution of dietary consumption patterns (2014)
  8. Codd, Casey L.; Cudeck, Robert: Nonlinear random-effects mixture models for repeated measures (2014)
  9. Wu, Beilei; de Leon, Alexander R.: Gaussian copula mixed models for clustered mixed outcomes, with application in developmental toxicology (2014)
  10. Anderson, Carolyn J.: Multidimensional item response theory models with collateral information as Poisson regression models (2013)
  11. Silva, Rodrigo B.; Bourguignon, Marcelo; Dias, Cícero R.B.; Cordeiro, Gauss M.: The compound class of extended Weibull power series distributions (2013)
  12. Ye, Fei; Yue, Chen; Yang, Ying: Modeling time-dependent overdispersion in longitudinal count data (2013)
  13. DeCarlo, Lawrence T.: On a signal detection approach to $m$-alternative forced choice with bias, with maximum likelihood and Bayesian approaches to estimation (2012)
  14. Desmond, A.F.; Cíntora González, Carlos L.; Singh, R.S.; Lu, Xuewen: A mixed effects log-linear model based on the Birnbaum-Saunders distribution (2012)
  15. Vock, David M.; Davidian, Marie; Tsiatis, Anastasios A.; Muir, Andrew J.: Mixed model analysis of censored longitudinal data with flexible random-effects density (2012)
  16. Wang, Peng; Tsai, Guei-Feng; Qu, Annie: Conditional inference functions for mixed-effects models with unspecified random-effects distribution (2012)
  17. Gillard, Jonathan: Asymptotic variance-covariance matrices for the linear structural model (2011)
  18. Lawal, Bayo H.: On zero-truncated generalized Poisson count regression model (2011)
  19. Lin, Lanjia; Bandyopadhyay, Dipankar; Lipsitz, Stuart R.; Sinha, Debajyoti: Association models for clustered data with binary and continuous responses (2010)
  20. Duval, Mylène; Robert-Granié, Christèle; Foulley, Jean-Louis: Estimation of heterogeneous variances in nonlinear mixed models via the SAEM-MCMC algorithm with applications to growth curves in poultry (2009)

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