FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3049206795> ?p ?o ?g. }

• W3049206795 endingPage "1502" @default.
• W3049206795 startingPage "1473" @default.
• W3049206795 abstract "Abstract Latent variable models have been playing a central role in psychometrics and related fields. In many modern applications, the inference based on latent variable models involves one or several of the following features: (1) the presence of many latent variables, (2) the observed and latent variables being continuous, discrete, or a combination of both, (3) constraints on parameters, and (4) penalties on parameters to impose model parsimony. The estimation often involves maximizing an objective function based on a marginal likelihood/pseudo-likelihood, possibly with constraints and/or penalties on parameters. Solving this optimization problem is highly non-trivial, due to the complexities brought by the features mentioned above. Although several efficient algorithms have been proposed, there lacks a unified computational framework that takes all these features into account. In this paper, we fill the gap. Specifically, we provide a unified formulation for the optimization problem and then propose a quasi-Newton stochastic proximal algorithm. Theoretical properties of the proposed algorithms are established. The computational efficiency and robustness are shown by simulation studies under various settings for latent variable model estimation." @default.
• W3049206795 created "2020-08-21" @default.
• W3049206795 creator A5042661279 @default.
• W3049206795 creator A5056458931 @default.
• W3049206795 date "2022-05-07" @default.
• W3049206795 modified "2023-10-18" @default.
• W3049206795 title "Computation for Latent Variable Model Estimation: A Unified Stochastic Proximal Framework" @default.
• W3049206795 cites W138052739 @default.
• W3049206795 cites W1568229137 @default.
• W3049206795 cites W1973333717 @default.
• W3049206795 cites W1980782084 @default.
• W3049206795 cites W1982676970 @default.
• W3049206795 cites W1994616650 @default.
• W3049206795 cites W1995884758 @default.
• W3049206795 cites W1998088128 @default.
• W3049206795 cites W2001940873 @default.
• W3049206795 cites W2002352330 @default.
• W3049206795 cites W2004278023 @default.
• W3049206795 cites W2007897277 @default.
• W3049206795 cites W2017966270 @default.
• W3049206795 cites W2022814427 @default.
• W3049206795 cites W2029859090 @default.
• W3049206795 cites W203276351 @default.
• W3049206795 cites W2035779913 @default.
• W3049206795 cites W2041701373 @default.
• W3049206795 cites W2043073960 @default.
• W3049206795 cites W2043661251 @default.
• W3049206795 cites W2047570292 @default.
• W3049206795 cites W2053742104 @default.
• W3049206795 cites W2062528761 @default.
• W3049206795 cites W2064871928 @default.
• W3049206795 cites W2066623346 @default.
• W3049206795 cites W2069398638 @default.
• W3049206795 cites W2069605605 @default.
• W3049206795 cites W2086161653 @default.
• W3049206795 cites W2089773781 @default.
• W3049206795 cites W2100696052 @default.
• W3049206795 cites W2102264754 @default.
• W3049206795 cites W2117640803 @default.
• W3049206795 cites W2122825543 @default.
• W3049206795 cites W2124541940 @default.
• W3049206795 cites W2133555934 @default.
• W3049206795 cites W2136568218 @default.
• W3049206795 cites W2150122281 @default.
• W3049206795 cites W2159307101 @default.
• W3049206795 cites W2168003017 @default.
• W3049206795 cites W2322461341 @default.
• W3049206795 cites W2336766371 @default.
• W3049206795 cites W2529026265 @default.
• W3049206795 cites W2602608495 @default.
• W3049206795 cites W2790638850 @default.
• W3049206795 cites W2903210442 @default.
• W3049206795 cites W2911920720 @default.
• W3049206795 cites W2963173886 @default.
• W3049206795 cites W2963639289 @default.
• W3049206795 cites W2963943233 @default.
• W3049206795 cites W2964080189 @default.
• W3049206795 cites W2967906128 @default.
• W3049206795 cites W2968817420 @default.
• W3049206795 cites W3016820331 @default.
• W3049206795 cites W3031917835 @default.
• W3049206795 cites W3044194783 @default.
• W3049206795 cites W3102317997 @default.
• W3049206795 cites W4232260583 @default.
• W3049206795 cites W4238484605 @default.
• W3049206795 cites W4244393449 @default.
• W3049206795 cites W4246435226 @default.
• W3049206795 cites W4247571494 @default.
• W3049206795 cites W4249513058 @default.
• W3049206795 cites W4250589301 @default.
• W3049206795 cites W4292494783 @default.
• W3049206795 cites W4298091299 @default.
• W3049206795 cites W4302338619 @default.
• W3049206795 doi "https://doi.org/10.1007/s11336-022-09863-9" @default.
• W3049206795 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35524934" @default.
• W3049206795 hasPublicationYear "2022" @default.
• W3049206795 type Work @default.
• W3049206795 sameAs 3049206795 @default.
• W3049206795 citedByCount "6" @default.
• W3049206795 countsByYear W30492067952021 @default.
• W3049206795 countsByYear W30492067952022 @default.
• W3049206795 countsByYear W30492067952023 @default.
• W3049206795 crossrefType "journal-article" @default.
• W3049206795 hasAuthorship W3049206795A5042661279 @default.
• W3049206795 hasAuthorship W3049206795A5056458931 @default.
• W3049206795 hasBestOaLocation W30492067951 @default.
• W3049206795 hasConcept C104317684 @default.
• W3049206795 hasConcept C11413529 @default.
• W3049206795 hasConcept C119857082 @default.
• W3049206795 hasConcept C126255220 @default.
• W3049206795 hasConcept C134306372 @default.
• W3049206795 hasConcept C154945302 @default.
• W3049206795 hasConcept C167928553 @default.
• W3049206795 hasConcept C182365436 @default.
• W3049206795 hasConcept C185592680 @default.
• W3049206795 hasConcept C2776214188 @default.
• W3049206795 hasConcept C33923547 @default.
• W3049206795 hasConcept C41008148 @default.

• next