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Journal articles on the topic 'Marginal maximum likelihood'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Schmidt, Klaus D., and Angela Wünsche. "Chain ladder, marginal sum and maximum likelihood estimation." Blätter der DGVFM 23, no. 3 (1998): 267–77. http://dx.doi.org/10.1007/bf02808289.

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2

Ramsay, J. O., and S. Winsberg. "Maximum marginal likelihood estimation for semiparametric item analysis." Psychometrika 56, no. 3 (1991): 365–79. http://dx.doi.org/10.1007/bf02294480.

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3

Wilson, Mark, and Raymond J. Adams. "Marginal Maximum Likelihood Estimation for the Ordered Partition Model." Journal of Educational Statistics 18, no. 1 (1993): 69–90. http://dx.doi.org/10.3102/10769986018001069.

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This article describes a marginal maximum likelihood (MML) estimation algorithm for Wilson’s (1990) ordered partition model (OPM), a measurement model that does not require the set of available responses to assessment tasks to be fully ordered. The model and its estimation algorithm are illustrated through the analysis of an example data set. In the example, we use the ordered partition model to compare a set of alternative scoring schemes for open-ended science items.

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4

Wilson, Mark, and Raymond J. Adams. "Marginal Maximum Likelihood Estimation for the Ordered Partition Model." Journal of Educational Statistics 18, no. 1 (1993): 69. http://dx.doi.org/10.2307/1165183.

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5

Hessen, David J., and Conor V. Dolan. "Heteroscedastic one-factor models and marginal maximum likelihood estimation." British Journal of Mathematical and Statistical Psychology 62, no. 1 (2009): 57–77. http://dx.doi.org/10.1348/000711007x248884.

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6

Tanaka, Kazuyuki. "Maximum Marginal Likelihood Estimation and Constrained Optimization in Image Restoration." Transactions of the Japanese Society for Artificial Intelligence 16 (2001): 246–58. http://dx.doi.org/10.1527/tjsai.16.246.

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7

Hess, Klaus T. "Marginal-sum and maximum-likelihood estimation in a multiplicative tariff." AStA Advances in Statistical Analysis 93, no. 2 (2008): 221–33. http://dx.doi.org/10.1007/s10182-008-0079-9.

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8

Suesse, Thomas. "Marginal maximum likelihood estimation of SAR models with missing data." Computational Statistics & Data Analysis 120 (April 2018): 98–110. http://dx.doi.org/10.1016/j.csda.2017.11.004.

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9

Wood, Simon N. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 73, no. 1 (2010): 3–36. http://dx.doi.org/10.1111/j.1467-9868.2010.00749.x.

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10

De Leeuw, Jan, and Norman Verhelst. "Maximum Likelihood Estimation in Generalized Rasch Models." Journal of Educational Statistics 11, no. 3 (1986): 183–96. http://dx.doi.org/10.3102/10769986011003183.

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We review various models and techniques that have been proposed for item analysis according to the ideas of Rasch. A general model is proposed that unifies them, and maximum likelihood procedures are discussed for this general model. We show that unconditional maximum likelihood estimation in the functional Rasch model, as proposed by Wright and Haberman, is an important special case. Conditional maximum likelihood estimation, as proposed by Rasch and Andersen, is another important special case. Both procedures are related to marginal maximum likelihood estimation in the structural Rasch model

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11

Camilli, Gregory. "Examination of a Simple Errors-in-Variables Model: A Demonstration of Marginal Maximum Likelihood." Journal of Educational and Behavioral Statistics 31, no. 3 (2006): 311–25. http://dx.doi.org/10.3102/10769986031003311.

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A simple errors-in-variables regression model is given in this article for illustrating the method of marginal maximum likelihood (MML). Given suitable estimates of reliability, error variables, as nuisance variables, can be integrated out of likelihood equations. Given the closed form expression of the resulting marginal likelihood, the effects of error can be more clearly demonstrated. Derivations are given in detail to provide a detailed example of the marginalization strategy, and to prepare students for understanding more advanced applications of MML.

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12

Pryadko, Leonid P. "On maximum-likelihood decoding with circuit-level errors." Quantum 4 (August 6, 2020): 304. http://dx.doi.org/10.22331/q-2020-08-06-304.

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Error probability distribution associated with a given Clifford measurement circuit is described exactly in terms of the circuit error-equivalence group, or the circuit subsystem code previously introduced by Bacon, Flammia, Harrow, and Shi. This gives a prescription for maximum-likelihood decoding with a given measurement circuit. Marginal distributions for subsets of circuit errors are also analyzed; these generate a family of related asymmetric LDPC codes of varying degeneracy. More generally, such a family is associated with any quantum code. Implications for decoding highly-degenerate qua

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13

Cudeck, Robert, Jeffrey R. Harring, and Stephen H. C. du Toit. "Marginal Maximum Likelihood Estimation of a Latent Variable Model With Interaction." Journal of Educational and Behavioral Statistics 34, no. 1 (2009): 131–44. http://dx.doi.org/10.3102/1076998607313593.

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14

Hong, Maxwell R., and Ying Cheng. "Robust maximum marginal likelihood (RMML) estimation for item response theory models." Behavior Research Methods 51, no. 2 (2018): 573–88. http://dx.doi.org/10.3758/s13428-018-1150-4.

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15

Mislevy, Robert J., and Mark Wilson. "MARGINAL MAXIMUM LIKELIHOOD ESTIMATION FOR A PSYCHOMETRIC MODEL OF DISCONTINUOUS DEVELOPMENT." ETS Research Report Series 1992, no. 2 (1992): i—27. http://dx.doi.org/10.1002/j.2333-8504.1992.tb01505.x.

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16

O'Brien, Liam M., Garrett M. Fitzmaurice, and Nicholas J. Horton. "Maximum Likelihood Estimation of Marginal Pairwise Associations with Multiple Source Predictors." Biometrical Journal 48, no. 5 (2006): 860–75. http://dx.doi.org/10.1002/bimj.200510227.

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17

Feuerstahler, Leah M., and Niels G. Waller. "Abstract: Estimation of the 4-Parameter Model with Marginal Maximum Likelihood." Multivariate Behavioral Research 49, no. 3 (2014): 285. http://dx.doi.org/10.1080/00273171.2014.912889.

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18

Lang, Joseph B., John W. McDonald, and Peter W. F. Smith. "Association-Marginal Modeling of Multivariate Categorical Responses: A Maximum Likelihood Approach." Journal of the American Statistical Association 94, no. 448 (1999): 1161–71. http://dx.doi.org/10.1080/01621459.1999.10473871.

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19

Wang, Wenyu, and Nanpeng Yu. "Maximum Marginal Likelihood Estimation of Phase Connections in Power Distribution Systems." IEEE Transactions on Power Systems 35, no. 5 (2020): 3906–17. http://dx.doi.org/10.1109/tpwrs.2020.2977071.

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20

Hess, Klaus T. "Maximum-likelihood and marginal-sum estimation in some particular collective models." AStA Advances in Statistical Analysis 96, no. 2 (2011): 311–26. http://dx.doi.org/10.1007/s10182-011-0178-x.

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21

Chen, Ying-Xia, and Yu Miao. "Moderate deviations of marginal maximum likelihood estimator for m-dependent processes." Lithuanian Mathematical Journal 56, no. 4 (2016): 449–62. http://dx.doi.org/10.1007/s10986-016-9328-x.

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22

Mislevy, Robert J., and Mark Wilson. "Marginal maximum likelihood estimation for a psychometric model of discontinuous development." Psychometrika 61, no. 1 (1996): 41–71. http://dx.doi.org/10.1007/bf02296958.

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23

Harwell, Michael R., Frank B. Baker, and Michael Zwarts. "Item Parameter Estimation Via Marginal Maximum Likelihood and an EM Algorithm: A Didactic." Journal of Educational Statistics 13, no. 3 (1988): 243–71. http://dx.doi.org/10.3102/10769986013003243.

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The Bock and Aitkin (1981) Marginal Maximum Likelihood/EM approach to item parameter estimation is an alternative to the classical joint maximum likelihood procedure of item response theory. Unfortunately, the complexity of the underlying mathematics and the terse nature of the existing literature has made understanding of the approach difficult. To make the approach accessible to a wider audience, the present didactic paper provides the essential mathematical details of a marginal maximum likelihood/EM solution and shows how it can be used to obtain consistent item parameter estimates. For pe

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24

Petersen, Maya, Joshua Schwab, Susan Gruber, Nello Blaser, Michael Schomaker, and Mark van der Laan. "Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models." Journal of Causal Inference 2, no. 2 (2014): 147–85. http://dx.doi.org/10.1515/jci-2013-0007.

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AbstractThis paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention nodes, intermediate time-dependent covariates, and a possibly time-dependent outcome. The intervention nodes at each time point can include a binary treatment as well as a right-censoring indicator. Given a class of dynamic or static interventions, a marginal structural model is used to model the mean of the intervention-specific count

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25

Pfanzagl, J. "A case of asymptotic equivalence between conditional and marginal maximum likelihood estimators." Journal of Statistical Planning and Inference 35, no. 3 (1993): 301–7. http://dx.doi.org/10.1016/0378-3758(93)90018-2.

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26

Bing, Li, and Baoming Bai. "Design of Simplified Maximum-Likelihood Receivers for Multiuser CPM Systems." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/174294.

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A class of simplified maximum-likelihood receivers designed for continuous phase modulation based multiuser systems is proposed. The presented receiver is built upon a front end employing mismatched filters and a maximum-likelihood detector defined in a low-dimensional signal space. The performance of the proposed receivers is analyzed and compared to some existing receivers. Some schemes are designed to implement the proposed receivers and to reveal the roles of different system parameters. Analysis and numerical results show that the proposed receivers can approach the optimum multiuser rece

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27

Beisemann, Marie, Ortrud Wartlick, and Philipp Doebler. "Comparison of Recent Acceleration Techniques for the EM Algorithm in One- and Two-Parameter Logistic IRT Models." Psych 2, no. 4 (2020): 209–52. http://dx.doi.org/10.3390/psych2040018.

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The expectation–maximization (EM) algorithm is an important numerical method for maximum likelihood estimation in incomplete data problems. However, convergence of the EM algorithm can be slow, and for this reason, many EM acceleration techniques have been proposed. After a review of acceleration techniques in a unified notation with illustrations, three recently proposed EM acceleration techniques are compared in detail: quasi-Newton methods (QN), “squared” iterative methods (SQUAREM), and parabolic EM (PEM). These acceleration techniques are applied to marginal maximum likelihood estimation

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28

NAKAMURA, Tomoyasu, and Shin-ichi MAYEKAWA. "MARGINAL MAXIMUM LIKELIHOOD ESTIMATION OF ITEM PARAMETERS IN A GENERALIZED ITEM RESPONSE MODEL." Japanese Journal of Educational Psychology 41, no. 1 (1993): 22–30. http://dx.doi.org/10.5926/jjep1953.41.1_22.

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29

Dikmen, Onur, and Cédric Fevotte. "Maximum Marginal Likelihood Estimation for Nonnegative Dictionary Learning in the Gamma-Poisson Model." IEEE Transactions on Signal Processing 60, no. 10 (2012): 5163–75. http://dx.doi.org/10.1109/tsp.2012.2207117.

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30

Casabianca, Jodi M., and Charles Lewis. "IRT Item Parameter Recovery With Marginal Maximum Likelihood Estimation Using Loglinear Smoothing Models." Journal of Educational and Behavioral Statistics 40, no. 6 (2015): 547–78. http://dx.doi.org/10.3102/1076998615606112.

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31

Harwell, Michael R., Frank B. Baker, and Michael Zwarts. "Item Parameter Estimation Via Marginal Maximum Likelihood and an EM Algorithm: A Didactic." Journal of Educational Statistics 13, no. 3 (1988): 243. http://dx.doi.org/10.2307/1164654.

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32

Rousseau, Judith, and Botond Szabo. "Asymptotic behaviour of the empirical Bayes posteriors associated to maximum marginal likelihood estimator." Annals of Statistics 45, no. 2 (2017): 833–65. http://dx.doi.org/10.1214/16-aos1469.

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33

Drasgow, Fritz. "An Evaluation of Marginal Maximum Likelihood Estimation for the Two-Parameter Logistic Model." Applied Psychological Measurement 13, no. 1 (1989): 77–90. http://dx.doi.org/10.1177/014662168901300108.

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34

Schnitzer, Mireille E., Erica E. M. Moodie, and Robert W. Platt. "Targeted maximum likelihood estimation for marginal time-dependent treatment effects under density misspecification." Biostatistics 14, no. 1 (2012): 1–14. http://dx.doi.org/10.1093/biostatistics/kxs024.

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35

Saboor, Abdus, Hassan S. Bakouch, Fernando A. Moala, and Sheraz Hussain. "Properties and methods of estimation for a bivariate exponentiated Fréchet distribution." Mathematica Slovaca 70, no. 5 (2020): 1211–30. http://dx.doi.org/10.1515/ms-2017-0426.

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AbstractIn this paper, a bivariate extension of exponentiated Fréchet distribution is introduced, namely a bivariate exponentiated Fréchet (BvEF) distribution whose marginals are univariate exponentiated Fréchet distribution. Several properties of the proposed distribution are discussed, such as the joint survival function, joint probability density function, marginal probability density function, conditional probability density function, moments, marginal and bivariate moment generating functions. Moreover, the proposed distribution is obtained by the Marshall-Olkin survival copula. Estimatio

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36

Yiu, Sean, and Brian DM Tom. "Two-part models with stochastic processes for modelling longitudinal semicontinuous data: Computationally efficient inference and modelling the overall marginal mean." Statistical Methods in Medical Research 27, no. 12 (2017): 3679–95. http://dx.doi.org/10.1177/0962280217710573.

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Several researchers have described two-part models with patient-specific stochastic processes for analysing longitudinal semicontinuous data. In theory, such models can offer greater flexibility than the standard two-part model with patient-specific random effects. However, in practice, the high dimensional integrations involved in the marginal likelihood (i.e. integrated over the stochastic processes) significantly complicates model fitting. Thus, non-standard computationally intensive procedures based on simulating the marginal likelihood have so far only been proposed. In this paper, we des

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37

Kripfganz, Sebastian. "Quasi–maximum Likelihood Estimation of Linear Dynamic Short-T panel-data Models." Stata Journal: Promoting communications on statistics and Stata 16, no. 4 (2016): 1013–38. http://dx.doi.org/10.1177/1536867x1601600411.

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In this article, I describe the xtdpdqml command for the quasi–maximum likelihood estimation of linear dynamic panel-data models when the time horizon is short and the number of cross-sectional units is large. Based on the theoretical groundwork by Bhargava and Sargan (1983, Econometrica 51: 1635–1659) and Hsiao, Pesaran, and Tahmiscioglu (2002, Journal of Econometrics 109: 107–150), the marginal distribution of the initial observations is modeled as a function of the observed variables to circumvent a short- T dynamic panel-data bias. Both random-effects and fixed-effects versions are availab

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38

Tempelman, RJ, and D. Gianola. "Marginal maximum likelihood estimation of variance components in Poisson mixed models using Laplacian integration." Genetics Selection Evolution 25, no. 4 (1993): 305. http://dx.doi.org/10.1186/1297-9686-25-4-305.

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39

Jin, Shaobo, Johan Vegelius, and Fan Yang-Wallentin. "A Marginal Maximum Likelihood Approach for Extended Quadratic Structural Equation Modeling with Ordinal Data." Structural Equation Modeling: A Multidisciplinary Journal 27, no. 6 (2020): 864–73. http://dx.doi.org/10.1080/10705511.2020.1712552.

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40

Gierl, Mark J., and Terry Ackerman. "Software Review : XCALIBRE™ Marginal Maximum-Likelihood Estimation Program, Windows™ Version 1. 10." Applied Psychological Measurement 20, no. 3 (1996): 303–7. http://dx.doi.org/10.1177/014662169602000312.

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41

Glas, C. A. W. "The Rasch Model and Multistage Testing." Journal of Educational Statistics 13, no. 1 (1988): 45–52. http://dx.doi.org/10.3102/10769986013001045.

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This paper concerns the problem of estimating the item parameters of latent trait models in a multistage testing design. It is shown that using the Rasch model and conditional maximum likelihood estimates does not lead to solvable estimation equations. It is also shown that marginal maximum likelihood estimation, which assumes a sample of subjects from a population with a specified distribution of ability, will lead to solvable estimation equations, both in the Rasch model and in the Birnbaum model.

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42

Aravkin, A., J. V. Burke, A. Chiuso, and G. Pillonetto. "On the estimation of hyperparameters for Empirical Bayes estimators: Maximum Marginal Likelihood vs Minimum MSE." IFAC Proceedings Volumes 45, no. 16 (2012): 125–30. http://dx.doi.org/10.3182/20120711-3-be-2027.00353.

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43

Hojo, Hiroshi. "A marginal maximum likelihood method for the vector threshold model to analyze dichotomous choice data." Japanese Psychological Research 45, no. 3 (2003): 188–201. http://dx.doi.org/10.1111/1468-5884.t01-1-00044.

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44

Cook, Scott J., Jude C. Hays, and Robert J. Franzese. "Fixed effects in rare events data: a penalized maximum likelihood solution." Political Science Research and Methods 8, no. 1 (2018): 92–105. http://dx.doi.org/10.1017/psrm.2018.40.

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AbstractMost agree that models of binary time-series-cross-sectional data in political science often possess unobserved unit-level heterogeneity. Despite this, there is no clear consensus on how best to account for these potential unit effects, with many of the issues confronted seemingly misunderstood. For example, one oft-discussed concern with rare events data is the elimination of no-event units from the sample when estimating fixed effects models. Many argue that this is a reason to eschew fixed effects in favor of pooled or random effects models. We revisit this issue and clarify that th

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45

Bhat, Chandra R. "The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models." Transportation Research Part B: Methodological 45, no. 7 (2011): 923–39. http://dx.doi.org/10.1016/j.trb.2011.04.005.

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46

Ogasawara, Haruhiko. "Marginal maximum likelihood estimation of item response theory (IRT) equating coefficients for the common-examinee design." Japanese Psychological Research 43, no. 2 (2001): 72–82. http://dx.doi.org/10.1111/1468-5884.00162.

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47

Molenaar, Dylan, Sophie van der Sluis, Dorret I. Boomsma, and Conor V. Dolan. "Detecting Specific Genotype by Environment Interactions Using Marginal Maximum Likelihood Estimation in the Classical Twin Design." Behavior Genetics 42, no. 3 (2011): 483–99. http://dx.doi.org/10.1007/s10519-011-9522-x.

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48

Maunder, Mark N., and Richard B. Deriso. "Estimation of recruitment in catch-at-age models." Canadian Journal of Fisheries and Aquatic Sciences 60, no. 10 (2003): 1204–16. http://dx.doi.org/10.1139/f03-104.

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Management strategies must be designed to take into account the uncertainty inherent in fish populations and their assessments. Annual recruitment variation is an important component of uncertainty. Several methods that allow the estimation of annual recruitment in statistical catch-at-age models are described: (a) maximum likelihood estimation with no penalty on the annual recruitment residuals, (b) maximum likelihood estimation with a lognormal penalty on the annual recruitment residuals, (c) importance sampling to numerically approximate the marginal likelihood with a lognormal penalty on t

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49

Mazzeo, S. E., K. S. Mitchell, C. M. Bulik, T. Reichborn-Kjennerud, K. S. Kendler, and M. C. Neale. "Assessing the heritability of anorexia nervosa symptoms using a marginal maximal likelihood approach." Psychological Medicine 39, no. 3 (2008): 463–73. http://dx.doi.org/10.1017/s0033291708003310.

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BackgroundAssessment of eating disorders at the symptom level can facilitate the refinement of phenotypes. We examined genetic and environmental contributions to liability to anorexia nervosa (AN) symptoms in a population-based twin sample using a genetic common pathway model.MethodParticipants were from the Norwegian Institute of Public Health Twin Panel (NIPHTP) and included all female monozygotic (MZ; 448 complete pairs and four singletons) and dizygotic (DZ; 263 complete pairs and four singletons) twins who completed the Composite International Diagnostic Interview (CIDI) assessing DSM-IV

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50

Bakouch, Hassan S., Fernando A. Moala, Abdus Saboor, and Haniya Samad. "A bivariate Kumaraswamy-exponential distribution with application." Mathematica Slovaca 69, no. 5 (2019): 1185–212. http://dx.doi.org/10.1515/ms-2017-0300.

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Abstract In this paper, we introduce a new bivariate Kumaraswamy exponential distribution, whose marginals are univariate Kumaraswamy exponential. Some probabilistic properties of this bivariate distribution are derived, such as joint density function, marginal density functions, conditional density functions, moments and stress-strength reliability. Also, we provide the expected information matrix with its elements in a closed form. Estimation of the parameters is investigated by the maximum likelihood, Bayesian and least squares estimation methods. A simulation study is carried out to compar

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