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 49 articles )

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  1. Preisser, John S.; Inan, Gul; Powers, James M.; Chu, Haitao: A population-averaged approach to diagnostic test meta-analysis (2019)
  2. Yiyun Shou and Michael Smithson: cdfquantreg: An R Package for CDF-Quantile Regression (2019) not zbMATH
  3. Alberto Garcia-Hernandez; Dimitris Rizopoulos: %JM: A SAS Macro to Fit Jointly Generalized Mixed Models for Longitudinal Data and Time-to-Event Responses (2018) not zbMATH
  4. Delia Voronca; Mulugeta Gebregziabher; Valerie Durkalski-Mauldin; Lei Liu; Leonard Egede: MTPmle: A SAS Macro and Stata Programs for Marginalized Inference in Semi-Continuous Data (2018) not zbMATH
  5. Shoukri, Mohamed M.: Analysis of correlated data with SAS and R (2018)
  6. Dey, Sanku; Mazucheli, Josmar; Anis, M. Z.: Estimation of reliability of multicomponent stress-strength for a Kumaraswamy distribution (2017)
  7. Grimm, Kevin J.; Ram, Nilam; Estabrook, Ryne: Growth modeling. Structural equation and multilevel modeling approaches (2017)
  8. Sellers, Kimberly F.; Morris, Darcy S.: Underdispersion models: models that are “under the radar” (2017)
  9. Su, Xiao; Luo, Sheng: Analysis of censored longitudinal data with skewness and a terminal event (2017)
  10. Commenges, Daniel; Jacqmin-Gadda, Hélène: Dynamical biostatistical models (2016)
  11. Jin, Ying; Kang, Minsoo: Comparing DIF methods for data with dual dependency (2016) MathEduc
  12. 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)
  13. Diaz, Mireya: Performance measures of the bivariate random effects model for meta-analyses of diagnostic accuracy (2015)
  14. Carroll, Raymond J.: Estimating the distribution of dietary consumption patterns (2014)
  15. Codd, Casey L.; Cudeck, Robert: Nonlinear random-effects mixture models for repeated measures (2014)
  16. Gumedze, Freedom N.; Chatora, Tinashe D.: Detection of outliers in longitudinal count data via overdispersion (2014)
  17. Wu, Beilei; de Leon, Alexander R.: Gaussian copula mixed models for clustered mixed outcomes, with application in developmental toxicology (2014)
  18. Anderson, Carolyn J.: Multidimensional item response theory models with collateral information as Poisson regression models (2013)
  19. Donald Hedeker; Rachel Nordgren: MIXREGLS: A Program for Mixed-Effects Location Scale Analysis (2013) not zbMATH
  20. Silva, Rodrigo B.; Bourguignon, Marcelo; Dias, Cícero R. B.; Cordeiro, Gauss M.: The compound class of extended Weibull power series distributions (2013)

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