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1
Some Properties of the Pivotal Statistic Based on the Asymptotically Distribution-Free Theory in Structural Equation Modeling
Some Properties of the Pivotal Statistic Based on the Asymptotically Distribution-Free Theory in Structural Equation Modeling
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Some Properties of the Pivotal Statistic Based on the Asymptotically Distribution-Free Theory in Structural Equation Modeling

Ogasawara, Haruhiko

Communications in statistics. Simulation and computation, 2008-10, Vol.37 (10), p.1931-1947 [Periódico revisado por pares]

Colchester: Taylor & Francis Group

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2
Bayesian estimation of multidimensional polytomous item response theory models with Q-matrices using Stan
Bayesian estimation of multidimensional polytomous item response theory models with Q-matrices using Stan
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Bayesian estimation of multidimensional polytomous item response theory models with Q-matrices using Stan

Marcelo Andrade da Silva Ren Liu; Anne Corinne Huggins-Manley; Jorge Luis Bazán Guzmán

Communications in Statistics - Simulation and Computation New York v. 52, n. 11, p. 5178-5194, 2023

New York 2023

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD-3043418 )(Acessar)

3
Using the Standardized Difference to Compare the Prevalence of a Binary Variable Between Two Groups in Observational Research
Using the Standardized Difference to Compare the Prevalence of a Binary Variable Between Two Groups in Observational Research
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Using the Standardized Difference to Compare the Prevalence of a Binary Variable Between Two Groups in Observational Research

Austin, Peter C.

Communications in statistics. Simulation and computation, 2009-05, Vol.38 (6), p.1228-1234 [Periódico revisado por pares]

Colchester: Taylor & Francis Group

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4
Sensitivity analysis and choosing between alternative polytomous IRT models using bayesian model comparison criteria
Sensitivity analysis and choosing between alternative polytomous IRT models using bayesian model comparison criteria
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Sensitivity analysis and choosing between alternative polytomous IRT models using bayesian model comparison criteria

Marcelo A. da Silva (Marcelo Andrade da Silva) Jorge Luis Bazán Guzmán; Anne Corinne Huggins-Manley

Communications in Statistics - Simulation and Computation Philadelphia : Taylor and Francis v. 48, n. 2, p. 601-620, Fev. 2019

Philadelphia 2019

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD 2872836 )(Acessar)

5
Storage Life Prediction for a High-Performance Capacitor Using Multi-Phase Wiener Degradation Model
Storage Life Prediction for a High-Performance Capacitor Using Multi-Phase Wiener Degradation Model
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Storage Life Prediction for a High-Performance Capacitor Using Multi-Phase Wiener Degradation Model

Feng, Jing ; Sun, Quan ; Jin, Tongdan

Communications in statistics. Simulation and computation, 2012-09, Vol.41 (8), p.1317-1335 [Periódico revisado por pares]

Colchester: Taylor & Francis Group

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6
Exact and approximate computation of critical values of the largest root test in high dimension
Exact and approximate computation of critical values of the largest root test in high dimension
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Exact and approximate computation of critical values of the largest root test in high dimension

Ang, Gregory Tai Xiang ; Bai, Zhidong ; Choi, Kwok Pui ; Fujikoshi, Yasunori ; Hu, Jiang

Communications in statistics. Simulation and computation, 2023-05, Vol.52 (5), p.2177-2193 [Periódico revisado por pares]

Philadelphia: Taylor & Francis

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7
Aspects of Dependence in Generalized Farlie-Gumbel-Morgenstern Distributions
Aspects of Dependence in Generalized Farlie-Gumbel-Morgenstern Distributions
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Aspects of Dependence in Generalized Farlie-Gumbel-Morgenstern Distributions

Amini, M. ; Jabbari, H. ; Mohtashami Borzadaran, G. R.

Communications in statistics. Simulation and computation, 2011-09, Vol.40 (8), p.1192-1205 [Periódico revisado por pares]

Colchester: Taylor & Francis Group

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8
Checking Normality and Homoscedasticity in the General Linear Model Using Diagnostic Plots
Checking Normality and Homoscedasticity in the General Linear Model Using Diagnostic Plots
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Checking Normality and Homoscedasticity in the General Linear Model Using Diagnostic Plots

Schützenmeister, A. ; Jensen, U. ; Piepho, H.-P.

Communications in statistics. Simulation and computation, 2012-02, Vol.41 (2), p.141-154 [Periódico revisado por pares]

Colchester: Taylor & Francis Group

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9
An Improved Divergence Information Criterion for the Determination of the Order of an AR Process
An Improved Divergence Information Criterion for the Determination of the Order of an AR Process
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An Improved Divergence Information Criterion for the Determination of the Order of an AR Process

Mantalos, P. ; Mattheou, K. ; Karagrigoriou, A.

Communications in statistics. Simulation and computation, 2010-05, Vol.39 (5), p.865-879 [Periódico revisado por pares]

Colchester: Taylor & Francis Group

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10
Impact of Contamination on Training and Test Error Rates in Statistical Clustering
Impact of Contamination on Training and Test Error Rates in Statistical Clustering
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Impact of Contamination on Training and Test Error Rates in Statistical Clustering

Ruwet, C. ; Haesbroeck, G.

Communications in statistics. Simulation and computation, 2011-02, Vol.40 (3), p.394-411 [Periódico revisado por pares]

Colchester: Taylor & Francis Group

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Deste Autor:

  1. Bazán Guzmán, J
  2. Silva, M
  3. Huggins-Manley, A
  4. Liu, R

Neste Assunto:

  1. Modeling And Simulation
  2. Statistics And Probability
  3. Mathematics
  4. Physical Sciences
  5. Statistics & Probability

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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