MARC Record from marc_columbia

LEADER: 01862cam a22003613i 4500
001 13678431
005 20190216175007.0
006 m o d
007 cr |n||||a||||
008 190107s2019 nyu|||| om 00| ||eng d
035 $a(OCoLC)1083235712
035 $a(OCoLC)on1083235712
035 $a(NNC)ACfeed:legacy_id:ac:0cfxpnvx2g
035 $a(NNC)ACfeed:doi:10.7916/D8Q25H61
035 $a(NNC)13678431
040 $aNNC$beng$erda$cNNC
100 1 $aLiu, Xiang.
245 10 $aThree Contributions to Latent Variable Modeling /$cXiang Liu.
264 1 $a[New York, N.Y.?] :$b[publisher not identified],$c2019.
300 $a1 online resource.
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
502 $aThesis (Ph.D.)--Columbia University, 2019.
500 $aDepartment: Measurement and Evaluation.
500 $aThesis advisor: Lawrence DeCarlo.
520 $aThe dissertation includes three papers that address some theoretical and technical issues of latent variable models. The first paper extends the uniformly most powerful test approach for testing person parameter in IRT to the two-parameter logistic models. In addition, an efficient branch-and-bound algorithm for computing the exact p-value is proposed. The second paper proposes a reparameterization of the log-linear CDM model. A Gibbs sampler is developed for posterior computation. The third paper proposes an ordered latent class model with infinite classes using a stochastic process prior. Furthermore, a nonparametric IRT application is also discussed.
653 0 $aEducational tests and measurements
653 0 $aStatistics
653 0 $aLatent structure analysis
653 0 $aLatent variables
856 40 $uhttps://doi.org/10.7916/D8Q25H61$zClick for full text
852 8 $blweb$hDISSERTATIONS