Bivariate linear mixed models are useful when analyzing longitudinal data of two associated markers. In this paper, we present a bivariate linear mixed model including random effects or first-order auto-regressive process and independent measurement error for both markers. Codes and tricks to fit these models using SAS Proc MIXED are provided. Limitations of this program are discussed and an ex...

Linear mixed models (LMM) and generalized linear mixed models (GLMM) are widely used in regression analyses. With the variance structure dependent on the random effects with their variance components, the parameter estimation of LMMs is more complicated than linear models (LM). Generally, we use maximum likelihood estimation (MLE) together with some procedure such as derivative free optimizatio...

We present the hglm package for fitting hierarchical generalized linear models. It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the model.

Using forest inventory data and Landsat ETM+ data, linear fixed-effects models and linear mixed-effects models are developed based on the allometric growth model. The surface area of the normalized difference vegetation index (NDVIsa) is developed from the triangulated irregular network (TIN) with the aid of image-processing and the three-dimensional analysis extensions of Environmental Systems...

The GLM procedure has been the workhorse for mixed model applications in the SAS® System. Other procedures, including NESTED and VARCOMP, are used for specific applications. Since its introduction in 1976, GLM has been enhanced with several mixed model facilities such as the RANDOM and REPEATED statements. However, there are aspects of certain models that none of these facilities fully accommod...

this paper proposes a new mathematical model for the integrated procurement-production-distribution problem of a fuzzy mixed integer linear programming (fmilp) type. considering the uncertainty in real problems, a number of parameters such as demand, capacity, and cost where their values are not available or known precisely, have been considered as trapezoidal fuzzy numbers. to solve fuzzy mixe...

this paper uses the weighted l$_1-$norm to propose an algorithm for finding a well-dispersed subset of non-dominated solutions of multiple objective mixed integer linear programming problem. when all variables are integer it finds the whole set of efficient solutions. in each iteration of the proposed method only a mixed integer linear programming problem is solved and its optimal solutions gen...

Differential and linear cryptanalysis are two of the most powerful techniques to analyze symmetric-key primitives. For modern ciphers, resistance against these attacks is therefore a mandatory design criterion. In this paper, we propose a novel technique to prove security bounds against both differential and linear cryptanalysis. We use mixedinteger linear programming (MILP), a method that is f...

Current mixed-integer linear programming solvers are based on linear programming routines that use floating-point arithmetic. Occasionally, this leads to wrong solutions, even for problems where all coefficients and all solution components are small integers. An example is given where many state-of-the-art MILP solvers fail. It is then shown how, using directed rounding and interval arithmetic,...

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