Mixture of Networks for Clustering Categorical Data: A Penalized Composite Likelihood Approach.
- Published in:
- American Statistician, 2023, v. 77, n. 3, p. 259, doi. 10.1080/00031305.2022.2141856
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- Publication type:
- Article
Works matching DE "FINITE mixture models (Statistics)"
Results: 354
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Greedy approximation and regression analysis for the discrete TM system.
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- International Journal of Wavelets, Multiresolution & Information Processing, 2024, v. 22, n. 4, p. 1, doi. 10.1142/S0219691324500085
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- Publication type:
- Article
3
Robust variable selection for finite mixture regression models.
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- Annals of the Institute of Statistical Mathematics, 2018, v. 70, n. 3, p. 489, doi. 10.1007/s10463-017-0602-4
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- Article
4
An approximation to the information matrix of exponential family finite mixtures.
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- Annals of the Institute of Statistical Mathematics, 2017, v. 69, n. 2, p. 333, doi. 10.1007/s10463-015-0542-9
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- Article
5
Empirical identifiability in finite mixture models.
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- Annals of the Institute of Statistical Mathematics, 2015, v. 67, n. 4, p. 745, doi. 10.1007/s10463-014-0474-9
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- Article
6
Quantification of variability of valid travel times with FMMs for buses, passenger cars, and taxis.
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- IET Intelligent Transport Systems (Wiley-Blackwell), 2017, v. 11, n. 1, p. 1, doi. 10.1049/iet-its.2016.0017
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- Publication type:
- Article
7
How to use statistics to claim antagonism and synergism from binary mixture experiments.
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- Pest Management Science, 2021, v. 77, n. 9, p. 3890, doi. 10.1002/ps.6348
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- Article
8
Finite mixture modeling of change point processes to discover opioid prescribing patterns: A case study of automated reports and consolidated ordering system data.
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- Communications in Statistics: Case Studies & Data Analysis, 2022, v. 8, n. 1, p. 199, doi. 10.1080/23737484.2021.2010621
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- Article
9
Balanced longitudinal data clustering with a copula kernel mixture model.
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- Canadian Journal of Statistics, 2025, v. 53, n. 1, p. 1, doi. 10.1002/cjs.11838
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10
Homogeneity testing under finite location‐scale mixtures.
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- Canadian Journal of Statistics, 2020, v. 48, n. 4, p. 670, doi. 10.1002/cjs.11557
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11
Hypothesis testing in finite mixture of regressions: Sparsity and model selection uncertainty.
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- Canadian Journal of Statistics, 2018, v. 46, n. 3, p. 429, doi. 10.1002/cjs.11467
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- Article
12
Issue Information.
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- Canadian Journal of Statistics, 2018, v. 46, n. 3, p. 377, doi. 10.1002/cjs.11336
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- Article
13
The joy of proofs in statistical research.
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- Canadian Journal of Statistics, 2015, v. 43, n. 4, p. 481, doi. 10.1002/cjs.11259
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- Article
14
Coefficient Alpha and Beyond: Issues and Alternatives for Educational Research.
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- Asia-Pacific Education Researcher (Springer Science & Business Media B.V.), 2013, v. 22, n. 2, p. 209, doi. 10.1007/s40299-013-0075-z
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- Publication type:
- Article
15
Characterization of a robust probabilistic framework for brain magnetic resonance image data distributions.
- Published in:
- Stat, 2023, v. 12, n. 1, p. 1, doi. 10.1002/sta4.541
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- Publication type:
- Article
16
Model-based clustering of semiparametric temporal exponential-family random graph models.
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- Stat, 2022, v. 11, n. 1, p. 1, doi. 10.1002/sta4.459
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- Publication type:
- Article
17
Parametric identification of the joint distribution of the potential outcomes.
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- Stat, 2020, v. 9, n. 1, p. 1, doi. 10.1002/sta4.254
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- Article
18
Bayesian sparse graphical models and their mixtures.
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- Stat, 2014, v. 3, n. 1, p. 109, doi. 10.1002/sta4.49
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- Article
19
Prediction of Lifespan of Tropical Cyclones Landing at China in Spring: A Case Study of Northward-moving-cluster Tracks.
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- Meteorological & Environmental Research, 2020, v. 11, n. 2, p. 56, doi. 10.19574/j.issn2152-3940.2020.02.011
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20
Editorial.
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- BMS: Bulletin de Methodologie Sociologique (Sage Publications Ltd.), 2016, n. 132, p. 3, doi. 10.1177/0759106316662938
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- Article
21
Focused analysis of RNFL decay in glaucomatous eyes using circular statistics on high-resolution OCT data.
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- PLoS ONE, 2023, v. 18, n. 10, p. 1, doi. 10.1371/journal.pone.0292915
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- Article
22
The benefits of incorporating utility dependencies in finite mixture probit models.
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- OR Spectrum, 2017, v. 39, n. 3, p. 793, doi. 10.1007/s00291-017-0478-y
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- Article
23
Are all Homeowners Willing to Pay for Better Schools? ─ Evidence from a Finite Mixture Model Approach.
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- Journal of Real Estate Finance & Economics, 2019, v. 58, n. 4, p. 638, doi. 10.1007/s11146-018-9658-z
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- Article
24
Profile Analysis: A Closer Look at the PISA 2000 Reading Data.
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- Scandinavian Journal of Educational Research, 2012, v. 56, n. 3, p. 315, doi. 10.1080/00313831.2011.583937
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- Article
25
“GANZ RASCH”: A Free Software for Categorical Data Analysis.
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- Social Science Computer Review, 2012, v. 30, n. 3, p. 369, doi. 10.1177/0894439311417222
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- Article
26
Model-Based Clustering and Prediction With Mixed Measurements Involving Surrogate Classifiers.
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- Statistics in Biopharmaceutical Research, 2022, v. 14, n. 3, p. 368, doi. 10.1080/19466315.2020.1863257
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- Article
27
Commensurate Priors on a Finite Mixture Model for Incorporating Repository Data in Clinical Trials.
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- Statistics in Biopharmaceutical Research, 2016, v. 8, n. 2, p. 151, doi. 10.1080/19466315.2015.1133453
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- Article
28
Assessing variation in life-history tactics within a population using mixture regression models: a practical guide for evolutionary ecologists.
- Published in:
- Biological Reviews, 2017, v. 92, n. 2, p. 754, doi. 10.1111/brv.12254
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- Article
29
A New Semiparametric Finite Mixture Model-Based Adaptive Arithmetic Coding for Lossless Image Compression.
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- Circuits, Systems & Signal Processing, 2016, v. 35, n. 4, p. 1163, doi. 10.1007/s00034-015-0103-8
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- Article
30
Bayesian model-based clustering for longitudinal ordinal data.
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- Computational Statistics, 2019, v. 34, n. 3, p. 1015, doi. 10.1007/s00180-019-00872-4
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- Publication type:
- Article
31
Comparative evaluation of various frequentist and Bayesian non-homogeneous Poisson counting models.
- Published in:
- Computational Statistics, 2017, v. 32, n. 1, p. 1, doi. 10.1007/s00180-016-0686-y
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- Article
32
Penalized marginal likelihood estimation of finite mixtures of Archimedean copulas.
- Published in:
- Computational Statistics, 2014, v. 29, n. 1/2, p. 283, doi. 10.1007/s00180-013-0454-1
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- Article
33
Finite mixtures of unimodal beta and gamma densities and the $$k$$-bumps algorithm.
- Published in:
- Computational Statistics, 2013, v. 28, n. 4, p. 1571, doi. 10.1007/s00180-012-0367-4
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- Article
34
Linear latent variable models: the lava-package.
- Published in:
- Computational Statistics, 2013, v. 28, n. 4, p. 1385, doi. 10.1007/s00180-012-0344-y
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- Article
35
Generating cotton fiber length probability density function from fiber length parameters by finite mixture model.
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- Journal of the Textile Institute, 2015, v. 106, n. 6, p. 655, doi. 10.1080/00405000.2014.933516
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- Article
36
Application of finite mixture model in cotton fiber length probability distribution.
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- Journal of the Textile Institute, 2015, v. 106, n. 2, p. 146, doi. 10.1080/00405000.2014.906102
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- Article
37
Analysis of HIV/AIDS DRG in Portugal: a hierarchical finite mixture model.
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- European Journal of Health Economics, 2013, v. 14, n. 5, p. 715, doi. 10.1007/s10198-012-0416-5
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- Article
38
Effective Segmentation of University Alumni: Mining Contribution Data with Finite-Mixture Models.
- Published in:
- Research in Higher Education, 2015, v. 56, n. 1, p. 78, doi. 10.1007/s11162-014-9339-6
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- Article
39
Latent Class Analysis in Higher Education: An Illustrative Example of Pluralistic Orientation.
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- Research in Higher Education, 2014, v. 55, n. 5, p. 508, doi. 10.1007/s11162-013-9324-5
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- Publication type:
- Article
40
Long-run Relations between Childhood Shocks and Health in Late Adulthood—Evidence from the Survey of Health, Ageing, and Retirement in Europe.
- Published in:
- CESifo Economic Studies, 2014, v. 60, n. 2, p. 402, doi. 10.1093/cesifo/ifu015
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- Article
41
Fast progressive training of mixture models for model selection.
- Published in:
- Journal of Intelligent Information Systems, 2015, v. 44, n. 2, p. 223, doi. 10.1007/s10844-013-0282-3
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- Article
42
Hybrid confidence intervals for informative uniform asymptotic inference after model selection.
- Published in:
- Biometrika, 2024, v. 111, n. 1, p. 109, doi. 10.1093/biomet/asad023
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- Article
43
Order selection in finite mixture models: complete or observed likelihood information criteria?
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- Biometrika, 2015, v. 102, n. 3, p. 724, doi. 10.1093/biomet/asv027
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- Article
44
Latent factor models for density estimation.
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- Biometrika, 2014, v. 101, n. 3, p. 641, doi. 10.1093/biomet/asu019
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- Publication type:
- Article
45
带有相依稀疏算子的一阶 随机系数二项自回归模型.
- Published in:
- Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban), 2023, v. 61, n. 5, p. 1083, doi. 10.13413/j.cnki.jdxblxb.2022493
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46
MODELE DAYALI KÜMELEME ANALİZİNDE OPTİMUM KÜMELEME İÇİN YENİ BİR YAKLAŞIM.
- Published in:
- SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi, 2020, v. 8, n. 5, p. 218, doi. 10.21923/jesd.828051
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47
MODELE DAYALI KÜMELEME ANALİZİNDE OPTİMUM KÜMELEME İÇİN YENİ BİR YAKLAŞIM.
- Published in:
- SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi, 2020, v. 8, p. 218, doi. 10.21923/jesd.828051
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- Publication type:
- Article
48
Regional International Organizations and Individual Immigration Attitudes: Results from Finite Mixture Models.
- Published in:
- International Interactions, 2014, v. 40, n. 3, p. 350, doi. 10.1080/03050629.2014.899226
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- Article
49
Regime-dependent Characteristics of KOSPI Return.
- Published in:
- Communications for Statistical Applications & Methods, 2014, v. 21, n. 6, p. 501, doi. 10.5351/CSAM.2014.21.6.501
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- Article
50
Finite Mixture Model: Prediction of Time Series Data Using Bayesian Method.
- Published in:
- Malaysian Journal of Mathematical Sciences, 2022, v. 16, n. 2, p. 175, doi. 10.47836/mjms.16.2.01
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- Article