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Journal articles on the topic 'Pseudo maximum likelihood'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Broze, Laurence, and Christian Gouriéroux. "Pseudo-maximum likelihood method, adjusted pseudo-maximum likelihood method and covariance estimators." Journal of Econometrics 85, no. 1 (July 1998): 75–98. http://dx.doi.org/10.1016/s0304-4076(97)00095-x.

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2

Christian Gouriéroux, Alain Monfort, and Eric Renault. "Consistent Pseudo-Maximum Likelihood Estimators." Annals of Economics and Statistics, no. 125/126 (2017): 187. http://dx.doi.org/10.15609/annaeconstat2009.125-126.0187.

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3

Holly, Alberto, Alain Monfort, and Michael Rockinger. "Fourth order pseudo maximum likelihood methods." Journal of Econometrics 162, no. 2 (June 2011): 278–93. http://dx.doi.org/10.1016/j.jeconom.2011.01.004.

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4

Gang Liang and Bin Yu. "Maximum pseudo likelihood estimation in network tomography." IEEE Transactions on Signal Processing 51, no. 8 (August 2003): 2043–53. http://dx.doi.org/10.1109/tsp.2003.814464.

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5

Robinson, Peter M., and Paolo Zaffaroni. "Pseudo-maximum likelihood estimation of ARCH(∞) models." Annals of Statistics 34, no. 3 (June 2006): 1049–74. http://dx.doi.org/10.1214/009053606000000245.

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6

Parke, William R. "Pseudo Maximum Likelihood Estimation: The Asymptotic Distribution." Annals of Statistics 14, no. 1 (March 1986): 355–57. http://dx.doi.org/10.1214/aos/1176349862.

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7

Fiorentini, Gabriele, and Enrique Sentana. "Consistent non-Gaussian pseudo maximum likelihood estimators." Journal of Econometrics 213, no. 2 (December 2019): 321–58. http://dx.doi.org/10.1016/j.jeconom.2019.05.017.

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8

Pötscher, B. M. "Noninvertibility and Pseudo-Maximum Likelihood Estimation of Misspecified ARMA Models." Econometric Theory 7, no. 4 (December 1991): 435–49. http://dx.doi.org/10.1017/s0266466600004692.

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Recently Tanaka and Satchell [11] investigated the limiting properties of local maximizers of the Gaussian pseudo-likelihood function of a misspecified moving average model of order one in case the spectral density of the data process has a zero at frequency zero. We show that pseudo-maximum likelihood estimators in the narrower sense, that is, global maximizers of the Gaussian pseudo-likelihood function, may exhibit behavior drastically different from that of the local maximizers. Some general results on the limiting behavior of pseudo-maximum likelihood estimators in potentially misspecified ARMA models are also presented.

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9

Abdalmoaty, Mohamed Rasheed, and Håkan Hjalmarsson. "Simulated Pseudo Maximum Likelihood Identification of Nonlinear Models." IFAC-PapersOnLine 50, no. 1 (July 2017): 14058–63. http://dx.doi.org/10.1016/j.ifacol.2017.08.1841.

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10

Beran, Jan, and Martin Schützner. "On approximate pseudo-maximum likelihood estimation for LARCH-processes." Bernoulli 15, no. 4 (November 2009): 1057–81. http://dx.doi.org/10.3150/09-bej189.

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11

Gouriéroux, C., A. Monfort, and J.-M. Zakoïan. "Consistent Pseudo-Maximum Likelihood Estimators and Groups of Transformations." Econometrica 87, no. 1 (2019): 327–45. http://dx.doi.org/10.3982/ecta14727.

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12

Chuang, Christy, and Christopher Cox. "Pseudo maximum likelihood estimation for the dirichlet-multinomial distribution." Communications in Statistics - Theory and Methods 14, no. 10 (January 1985): 2293–311. http://dx.doi.org/10.1080/03610928508829045.

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13

Magnus, Jan R. "THE ASYMPTOTIC VARIANCE OF THE PSEUDO MAXIMUM LIKELIHOOD ESTIMATOR." Econometric Theory 23, no. 05 (May 14, 2007): 1022. http://dx.doi.org/10.1017/s0266466607070417.

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14

Liu, Lihua, Mounir Ghogho, Des McLernon, and Weidong Hu. "Pseudo-Maximum Likelihood Estimation of ballistic missile precession frequency." Signal Processing 92, no. 9 (September 2012): 2018–28. http://dx.doi.org/10.1016/j.sigpro.2012.01.011.

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15

Velasco, Carlos, and Peter M. Robinson. "Whittle Pseudo-Maximum Likelihood Estimation for Nonstationary Time Series." Journal of the American Statistical Association 95, no. 452 (December 2000): 1229–43. http://dx.doi.org/10.1080/01621459.2000.10474323.

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16

Dryden, Ian, Luigi Ippoliti, and Luca Romagnoli. "Adjusted Maximum Likelihood and Pseudo-Likelihood Estimation for Noisy Gaussian Markov Random Fields." Journal of Computational and Graphical Statistics 11, no. 2 (June 2002): 370–88. http://dx.doi.org/10.1198/106186002760180563.

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17

Chen, Wu, Liu, and Wang. "Application of Iterative Maximum Weighted Likelihood Estimation in 3-D Target Localization." Applied Sciences 9, no. 22 (November 15, 2019): 4921. http://dx.doi.org/10.3390/app9224921.

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Time-of-flight (ToF)-based 3-D target localization is a very challenging topic because of the pseudo-targets introduced by ToF measurement errors in traditional ToF-based methods. Although the influence of errors in ToF measurement can be reduced by the probability-based ToF method, the accuracy of localization is not very high. This paper proposes a new 3-D target localization method, Iterative Maximum Weighted Likelihood Estimation (IMWLE), that takes into account the spatial distribution of pseudo-targets. In our method, each pseudo-target is initially assigned an equal weight. At each iteration, Maximum Weighted Likelihood Estimation (MWLE) is adopted to fit a Gaussian distribution to all pseudo-target positions and assign new weight factors to them. The weight factors of the pseudo-targets, which are far from the target, are reduced to minimize their influence on localization. Therefore, IMWLE can reduce the influence of pseudo-targets that are far from the target and improve the accuracy of localization. The experiments were carried out in a water tank to test the performance of the IMWLE method. Results revealed that the estimated target area can be narrowed down to the target using IMWLE and a point estimate of target location can also be obtained, which shows that IMWLE has a higher degree of accuracy than the probability-based ToF method.

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18

BRESLOW, NORMAN E., and RICHARD HOLUBKOV. "WEIGHTED LIKELIHOOD, PSEUDO-LIKELIHOOD AND MAXIMUM LIKELIHOOD METHODS FOR LOGISTIC REGRESSION ANALYSIS OF TWO-STAGE DATA." Statistics in Medicine 16, no. 1 (January 15, 1997): 103–16. http://dx.doi.org/10.1002/(sici)1097-0258(19970115)16:1<103::aid-sim474>3.0.co;2-p.

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19

Donald, Stephen G., and Harry J. Paarsch. "Piecewise Pseudo-Maximum Likelihood Estimation in Empirical Models of Auctions." International Economic Review 34, no. 1 (February 1993): 121. http://dx.doi.org/10.2307/2526953.

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20

Concordet, Didier, and Olivier G. Nunez. "A simulated pseudo-maximum likelihood estimator for nonlinear mixed models." Computational Statistics & Data Analysis 39, no. 2 (April 2002): 187–201. http://dx.doi.org/10.1016/s0167-9473(01)00052-4.

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21

Berkane, Maia, Yutaka Kano, and Peter M. Bentler. "Pseudo maximum likelihood estimation in elliptical theory: Effects of misspecification." Computational Statistics & Data Analysis 18, no. 2 (September 1994): 255–67. http://dx.doi.org/10.1016/0167-9473(94)90175-9.

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22

Barsky, David J., and Alberto Gandolfi. "A Generalized Maximum Pseudo-Likelihood Estimator for Noisy Markov Fields." Annals of Applied Probability 5, no. 4 (November 1995): 1095–125. http://dx.doi.org/10.1214/aoap/1177004608.

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23

Hualde, Javier, and Peter M. Robinson. "Gaussian pseudo-maximum likelihood estimation of fractional time series models." Annals of Statistics 39, no. 6 (December 2011): 3152–81. http://dx.doi.org/10.1214/11-aos931.

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24

Pfaffermayr, Michael. "Constrained Poisson pseudo maximum likelihood estimation of structural gravity models." International Economics 161 (May 2020): 188–98. http://dx.doi.org/10.1016/j.inteco.2019.11.014.

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25

van Duijn, Marijtje A. J., Krista J. Gile, and Mark S. Handcock. "A framework for the comparison of maximum pseudo-likelihood and maximum likelihood estimation of exponential family random graph models." Social Networks 31, no. 1 (January 2009): 52–62. http://dx.doi.org/10.1016/j.socnet.2008.10.003.

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26

Nguyen, Hien D., and Ian A. Wood. "A Block Successive Lower-Bound Maximization Algorithm for the Maximum Pseudo-Likelihood Estimation of Fully Visible Boltzmann Machines." Neural Computation 28, no. 3 (March 2016): 485–92. http://dx.doi.org/10.1162/neco_a_00813.

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Maximum pseudo-likelihood estimation (MPLE) is an attractive method for training fully visible Boltzmann machines (FVBMs) due to its computational scalability and the desirable statistical properties of the MPLE. No published algorithms for MPLE have been proven to be convergent or monotonic. In this note, we present an algorithm for the MPLE of FVBMs based on the block successive lower-bound maximization (BSLM) principle. We show that the BSLM algorithm monotonically increases the pseudo-likelihood values and that the sequence of BSLM estimates converges to the unique global maximizer of the pseudo-likelihood function. The relationship between the BSLM algorithm and the gradient ascent (GA) algorithm for MPLE of FVBMs is also discussed, and a convergence criterion for the GA algorithm is given.

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27

Kano, Yutaka, Maia Berkane, and Peter M. Bentler. "Statistical Inference Based on Pseudo-Maximum Likelihood Estimators in Elliptical Populations." Journal of the American Statistical Association 88, no. 421 (March 1993): 135. http://dx.doi.org/10.2307/2290706.

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28

Biggeri, A., E. Dreassi, C. Lagazio, and D. Böhning. "A transitional non-parametric maximum pseudo-likelihood estimator for disease mapping." Computational Statistics & Data Analysis 41, no. 3-4 (January 2003): 617–29. http://dx.doi.org/10.1016/s0167-9473(02)00189-5.

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29

Bogaerts, Ph, J. L. Delcoux, and R. Hanus. "Maximum likelihood estimation of pseudo-stoichiometry in macroscopic biological reaction schemes." Chemical Engineering Science 58, no. 8 (April 2003): 1545–63. http://dx.doi.org/10.1016/s0009-2509(02)00680-2.

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30

Aguirregabiria, Victor. "Pseudo maximum likelihood estimation of structural models involving fixed-point problems." Economics Letters 84, no. 3 (September 2004): 335–40. http://dx.doi.org/10.1016/j.econlet.2004.03.002.

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31

Kovačević, Milorad S., and Shesh N. Rai. "A Pseudo Maximum Likelihood Approach to Multilevel Modelling of Survey Data." Communications in Statistics - Theory and Methods 32, no. 1 (January 3, 2003): 103–21. http://dx.doi.org/10.1081/sta-120017802.

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32

Kano, Yutaka, Maia Berkane, and Peter M. Bentler. "Statistical Inference Based on Pseudo-Maximum Likelihood Estimators in Elliptical Populations." Journal of the American Statistical Association 88, no. 421 (March 1993): 135–43. http://dx.doi.org/10.1080/01621459.1993.10594303.

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33

Kristensen, Dennis. "Pseudo-maximum likelihood estimation in two classes of semiparametric diffusion models." Journal of Econometrics 156, no. 2 (June 2010): 239–59. http://dx.doi.org/10.1016/j.jeconom.2009.10.017.

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34

Gupta, Abhimanyu, and Peter M. Robinson. "Pseudo maximum likelihood estimation of spatial autoregressive models with increasing dimension." Journal of Econometrics 202, no. 1 (January 2018): 92–107. http://dx.doi.org/10.1016/j.jeconom.2017.05.019.

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35

Mase, Shigeru. "Marked Gibbs Processes and Asymptotic Normality of Maximum Pseudo-Likelihood Estimators." Mathematische Nachrichten 209, no. 1 (January 2000): 151–69. http://dx.doi.org/10.1002/(sici)1522-2616(200001)209:1<151::aid-mana151>3.0.co;2-j.

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36

Islamov, Bakhtiyor, and Munisa Turdibaeva. "ECONOMETRIC ESTIMATION OF THE GRAVITY MODEL OF EXPORTS: A CASE OF THE REPUBLIC OF UZBEKISTAN." Economics and Education 24, no. 3 (June 30, 2023): 510–17. http://dx.doi.org/10.55439/eced/vol24_iss3/a81.

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In this article, the authors critically analyze the existing econometric approaches to estimating gravity models of exports from the point of view of solving such problems as taking into account zero observations, bias, inconsistency and inefficiency of estimates. As a result, the authors come to the conclusion that the method of Poisson pseudo-maximum-likelihood estimation (Poisson pseudo-maximum-likelihood, PPML) is the most preferable method for estimating the gravity export model of the Republic of Uzbekistan.

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37

Brummel, Bradley J., and Fritz Drasgow. "The Effects Of Estimator Choice And Weighting Strategies On Confirmatory Factor Analysis With Stratified Samples." Applied Multivariate Research 13, no. 2 (August 9, 2010): 113. http://dx.doi.org/10.22329/amr.v13i2.3019.

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Survey researchers often design stratified sampling strategies to target specific subpopulations within the larger population. This stratification can influence the population parameter estimates from these samples because they are not simple random samples of the population. There are three typical estimation options that account for the effects of this stratification in latent variable models: unweighted maximum likelihood, weighted maximum likelihood, and pseudo-maximum likelihood estimation. This paper examines the effects of these procedures on parameter estimates, standard errors, and fit statistics in Lisrel 8.7 (Jöreskog & Sörbom, 2004) and Mplus 3.0 (Muthén & Muthén, 2004). Options using several estimation methods will be compared to pseudo-maximum likelihood estimation. Results indicated the choice of estimation technique does not have a substantial effect on confirmatory factor analysis parameter estimates in large samples. However, standard errors of those parameter estimates and RMSEA values for assessing of model fit can be substantially affected by estimation technique.

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38

Crepon, Bruno, and Emmanuel Duguet. "Research and development, competition and innovation pseudo-maximum likelihood and simulated maximum likelihood methods applied to count data models with heterogeneity." Journal of Econometrics 79, no. 2 (August 1997): 355–78. http://dx.doi.org/10.1016/s0304-4076(97)00027-4.

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39

Huang, Fuchun, and Yosihiko Ogata. "Improvements of the Maximum Pseudo-Likelihood Estimators in Various Spatial Statistical Models." Journal of Computational and Graphical Statistics 8, no. 3 (September 1999): 510. http://dx.doi.org/10.2307/1390872.

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40

Arminger, Gerhard, and Michael E. Sobel. "Pseudo-Maximum Likelihood Estimation of Mean and Covariance Structures with Missing Data." Journal of the American Statistical Association 85, no. 409 (March 1990): 195–203. http://dx.doi.org/10.1080/01621459.1990.10475326.

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41

Prehn, S., B. Brümmer, and T. Glauben. "Gravity model estimation: fixed effects vs. random intercept Poisson pseudo-maximum likelihood." Applied Economics Letters 23, no. 11 (November 2, 2015): 761–64. http://dx.doi.org/10.1080/13504851.2015.1105916.

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42

Berghaus, Betina, and Axel Bücher. "Weak convergence of a pseudo maximum likelihood estimator for the extremal index." Annals of Statistics 46, no. 5 (October 2018): 2307–35. http://dx.doi.org/10.1214/17-aos1621.

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43

Fascista, Alessio, Angelo Coluccia, and Giuseppe Ricci. "A Pseudo Maximum likelihood approach to position estimation in dynamic multipath environments." Signal Processing 181 (April 2021): 107907. http://dx.doi.org/10.1016/j.sigpro.2020.107907.

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44

Huang, Fuchun, and Yosihiko Ogata. "Improvements of the Maximum Pseudo-Likelihood Estimators in Various Spatial Statistical Models." Journal of Computational and Graphical Statistics 8, no. 3 (September 1999): 510–30. http://dx.doi.org/10.1080/10618600.1999.10474829.

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45

Kouassi, Eugene, Patrice Takam Soh, Jean Marcelin Bosson Brou, and Emile Herve Ndoumbe. "Pseudo maximum-likelihood estimation of the univariate GARCH (1,1) and asymptotic properties." Communications in Statistics - Theory and Methods 46, no. 20 (October 10, 2016): 10253–71. http://dx.doi.org/10.1080/03610926.2016.1231824.

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46

Shen, Pao-sheng, and Yi Liu. "Pseudo maximum likelihood estimation for the Cox model with doubly truncated data." Statistical Papers 60, no. 4 (January 2, 2017): 1207–24. http://dx.doi.org/10.1007/s00362-016-0870-8.

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47

Dias, Alexandra. "Maximum Pseudo-Likelihood Estimation of Copula Models and Moments of Order Statistics." Risks 12, no. 1 (January 18, 2024): 15. http://dx.doi.org/10.3390/risks12010015.

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It has been shown that, despite being consistent and in some cases efficient, maximum pseudo-likelihood (MPL) estimation for copula models overestimates the level of dependence, especially for small samples with a low level of dependence. This is especially relevant in finance and insurance applications when data are scarce. We show that the canonical MPL method uses the mean of order statistics, and we propose to use the median or the mode instead. We show that the MPL estimators proposed are consistent and asymptotically normal. In a simulation study, we compare the finite sample performance of the proposed estimators with that of the original MPL and the inversion method estimators based on Kendall’s tau and Spearman’s rho. In our results, the modified MPL estimators, especially the one based on the mode of the order statistics, have a better finite sample performance both in terms of bias and mean square error. An application to general insurance data shows that the level of dependence estimated between different products can vary substantially with the estimation method used.

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48

WANG, JINLIANG. "A pseudo-likelihood method for estimating effective population size from temporally spaced samples." Genetical Research 78, no. 3 (December 2001): 243–57. http://dx.doi.org/10.1017/s0016672301005286.

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A pseudo maximum likelihood method is proposed to estimate effective population size (Ne) using temporal changes in allele frequencies at multi-allelic loci. The computation is simplified dramatically by (1) approximating the multi-dimensional joint probabilities of all the data by the product of marginal probabilities (hence the name pseudo-likelihood), (2) exploiting the special properties of transition matrix and (3) using a hidden Markov chain algorithm. Simulations show that the pseudo-likelihood method has a similar performance but needs much less computing time and storage compared with the full likelihood method in the case of 3 alleles per locus. Due to computational developments, I was able to assess the performance of the pseudo-likelihood method against the F-statistic method over a wide range of parameters by extensive simulations. It is shown that the pseudo-likelihood method gives more accurate and precise estimates of Ne than the F-statistic method, and the performance difference is mainly due to the presence of rare alleles in the samples. The pseudo-likelihood method is also flexible and can use three or more temporal samples simultaneously to estimate satisfactorily the Nes of each period, or the growth parameters of the population. The accuracy and precision of both methods depend on the ratio of the product of sample size and the number of generations involved to Ne, and the number of independent alleles used. In an application of the pseudo-likelihood method to a large data set of an olive fly population, more precise estimates of Ne are obtained than those from the F-statistic method.

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49

Gourieroux, C., and A. Monfort. "A General Framework for Testing a Null Hypothesis in a “Mixed” Form." Econometric Theory 5, no. 1 (April 1989): 63–82. http://dx.doi.org/10.1017/s0266466600012263.

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A general framework for asymptotic tests is proposed. The framework contains as particular cases tests based on various estimation techniques: maximum likelihood methods, pseudo-maximum likelihood (PML) methods and quasi-generalized PML methods,m-estimation methods, moments or generalized moments method, and asymptotic least squares. Moreover the null hypothesis has a general mixed form, including the usual implicit and explicit form.

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50

AGUIRREGABIRIA, VICTOR, and PEDRO MIRA. "A HYBRID GENETIC ALGORITHM FOR THE MAXIMUM LIKELIHOOD ESTIMATION OF MODELS WITH MULTIPLE EQUILIBRIA: A FIRST REPORT." New Mathematics and Natural Computation 01, no. 02 (July 2005): 295–303. http://dx.doi.org/10.1142/s1793005705000160.

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This paper presents a hybrid genetic algorithm to obtain maximum likelihood estimates of parameters in structural econometric models with multiple equilibria. The algorithm combines a pseudo maximum likelihood (PML) procedure with a genetic algorithm (GA). The GA searches globally over the large space of possible combinations of multiple equilibria in the data. The PML procedure avoids the computation of all the equilibria associated with every trial value of the structural parameters.

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