11259

10350

كارشناسي ارشد

امار رياضي

اصفهان: دانشگاه صنعتي اصفهان، دانشكده علوم رياضي

1394

دوازده، 87ص.: جدول

سروش عليمرادي

ريحانه ريخته گران

ايرج كاظمي، مريم كلكين نما

1395/03/03

رياضي

ID10350

Linear quantile mixed models Raheleh Mousavi r mousavi@math iut ac ir 2016 Department of Mathematical Sciences Isfahan University of Technology Isfahan 84156 83111 Iran Supervisor Dr Soruosh Alimoradi salimora@cc iut ac ir Advisor Dr Reyhaneh Rikhtehgaran r rikhtehgaran@cc iut ac ir 2010 MSC 62J05 Keywords linear quantile mixed models quantile regression mixed effects models Gaussian quadrature Clarke s derivative Best linear predictor Abstract Quantile regression QR was introduced as an extension of the classical least squaresestimation of conditional mean models to conditional quantile functions Quantile regressionis a statistical analysis able to detect more effects than conventional procedures It does notrestrict attention to the conditional mean and therefore it permits to approximate the wholeconditional distribution of a response variable Conditional quantile regression pertains tothe estimation of unknown quantiles of an outcome as a function of a set of covariates and avector of fixed regression coefficients On the other hand dependent data arise in many studies Frequently adopted samplingdesigns such as cluster and repeated measures may induce this dependence which theanalysis of the data needs to take into due account This sampling designs typically requirethe application of statistical methods that allow for the correlation between observations thatbelong to the same unit or cluster Mixed effects models also known random effects models represent highly popular and flexible models to analyze complex data A longitudinal survey

سروش عليمرادي

ريحانه ريخته گران

ايرج كاظمي، مريم كلكين نما