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Journal articles on the topic 'Maximum likelihood estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 July 2024

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1

Chung, Sai-Ho. "Modified maximum likelihood estimation." Communications in Statistics - Theory and Methods 27, no. 12 (January 1998): 2925–42. http://dx.doi.org/10.1080/03610929808832264.

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2

Jaki, Thomas, and R. Webster West. "Maximum Kernel Likelihood Estimation." Journal of Computational and Graphical Statistics 17, no. 4 (December 2008): 976–93. http://dx.doi.org/10.1198/106186008x387057.

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3

Bertsimas, Dimitris, and Omid Nohadani. "Robust Maximum Likelihood Estimation." INFORMS Journal on Computing 31, no. 3 (July 2019): 445–58. http://dx.doi.org/10.1287/ijoc.2018.0834.

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4

Kwasniok, Frank. "Semiparametric maximum likelihood probability density estimation." PLOS ONE 16, no. 11 (November 9, 2021): e0259111. http://dx.doi.org/10.1371/journal.pone.0259111.

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A comprehensive methodology for semiparametric probability density estimation is introduced and explored. The probability density is modelled by sequences of mostly regular or steep exponential families generated by flexible sets of basis functions, possibly including boundary terms. Parameters are estimated by global maximum likelihood without any roughness penalty. A statistically orthogonal formulation of the inference problem and a numerically stable and fast convex optimization algorithm for its solution are presented. Automatic model selection over the type and number of basis functions is performed with the Bayesian information criterion. The methodology can naturally be applied to densities supported on bounded, infinite or semi-infinite domains without boundary bias. Relationships to the truncated moment problem and the moment-constrained maximum entropy principle are discussed and a new theorem on the existence of solutions is contributed. The new technique compares very favourably to kernel density estimation, the diffusion estimator, finite mixture models and local likelihood density estimation across a diverse range of simulation and observation data sets. The semiparametric estimator combines a very small mean integrated squared error with a high degree of smoothness which allows for a robust and reliable detection of the modality of the probability density in terms of the number of modes and bumps.

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5

Talakua, Mozart W., and Jefri Tipka. "ESTIMASI PARAMETER DISTRIBUSI EKPONENSIAL PADA LOKASI TERBATAS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 1, no. 2 (December 1, 2007): 1–7. http://dx.doi.org/10.30598/barekengvol1iss2pp1-7.

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The common method in Estimating Parameter Distribution Exponential at Finite Location is Maximum Likelihood Estimation (MLE).The best estimator is consistent estimator. Because of The Mean Square Error (MSE) can be used in comparing some detectable estimators that it had looking for with Maximum Likelihood Estimation (MLE) so can find the consistent estimator in Estimating Parameter Distribution Exponential At Finite Location

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6

Currie, Iain D. "Maximum Likelihood Estimation and Mathematica." Applied Statistics 44, no. 3 (1995): 379. http://dx.doi.org/10.2307/2986044.

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7

Milligan, Brook G. "Maximum-Likelihood Estimation of Relatedness." Genetics 163, no. 3 (March 1, 2003): 1153–67. http://dx.doi.org/10.1093/genetics/163.3.1153.

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Abstract Relatedness between individuals is central to many studies in genetics and population biology. A variety of estimators have been developed to enable molecular marker data to quantify relatedness. Despite this, no effort has been given to characterize the traditional maximum-likelihood estimator in relation to the remainder. This article quantifies its statistical performance under a range of biologically relevant sampling conditions. Under the same range of conditions, the statistical performance of five other commonly used estimators of relatedness is quantified. Comparison among these estimators indicates that the traditional maximum-likelihood estimator exhibits a lower standard error under essentially all conditions. Only for very large amounts of genetic information do most of the other estimators approach the likelihood estimator. However, the likelihood estimator is more biased than any of the others, especially when the amount of genetic information is low or the actual relationship being estimated is near the boundary of the parameter space. Even under these conditions, the amount of bias can be greatly reduced, potentially to biologically irrelevant levels, with suitable genetic sampling. Additionally, the likelihood estimator generally exhibits the lowest root mean-square error, an indication that the bias in fact is quite small. Alternative estimators restricted to yield only biologically interpretable estimates exhibit lower standard errors and greater bias than do unrestricted ones, but generally do not improve over the maximum-likelihood estimator and in some cases exhibit even greater bias. Although some nonlikelihood estimators exhibit better performance with respect to specific metrics under some conditions, none approach the high level of performance exhibited by the likelihood estimator across all conditions and all metrics of performance.

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8

MINAMI, Mihoko. "The Restricted Maximum Likelihood Estimation." Japanese journal of applied statistics 25, no. 2 (1996): 73–78. http://dx.doi.org/10.5023/jappstat.25.73.

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9

Gallant, A. Ronald, and Douglas W. Nychka. "Semi-Nonparametric Maximum Likelihood Estimation." Econometrica 55, no. 2 (March 1987): 363. http://dx.doi.org/10.2307/1913241.

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10

Jaki, Thomas, and R. Webster West. "Symmetric maximum kernel likelihood estimation." Journal of Statistical Computation and Simulation 81, no. 2 (February 2011): 193–206. http://dx.doi.org/10.1080/00949650903232664.

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11

Schön, Thomas B., Adrian Wills, and Brett Ninness. "MAXIMUM LIKELIHOOD NONLINEAR SYSTEM ESTIMATION." IFAC Proceedings Volumes 39, no. 1 (2006): 1003–8. http://dx.doi.org/10.3182/20060329-3-au-2901.00160.

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12

Eggermont, P. P. B., and V. N. Lariccia. "Maximum smoothed likelihood density estimation." Journal of Nonparametric Statistics 4, no. 3 (January 1995): 211–22. http://dx.doi.org/10.1080/10485259508832613.

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13

Myung, In Jae. "Tutorial on maximum likelihood estimation." Journal of Mathematical Psychology 47, no. 1 (February 2003): 90–100. http://dx.doi.org/10.1016/s0022-2496(02)00028-7.

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14

Ljung, L., S. K. Mitter, and J. M. F. Moura. "Optimal Recursive Maximum Likelihood Estimation." IFAC Proceedings Volumes 20, no. 5 (July 1987): 241–42. http://dx.doi.org/10.1016/s1474-6670(17)55040-5.

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15

Stoica, Petre, Peter Händel, and Torsten Söderström. "Approximate maximum likelihood frequency estimation." Automatica 30, no. 1 (January 1994): 131–45. http://dx.doi.org/10.1016/0005-1098(94)90233-x.

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16

Ferrari, Davide, and Yuhong Yang. "Maximum L q -likelihood estimation." Annals of Statistics 38, no. 2 (April 2010): 753–83. http://dx.doi.org/10.1214/09-aos687.

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17

Conniffe, Denis. "Expected Maximum Log Likelihood Estimation." Statistician 36, no. 4 (1987): 317. http://dx.doi.org/10.2307/2348828.

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18

McLeod, A. I., and Y. Zhang. "Faster ARMA maximum likelihood estimation." Computational Statistics & Data Analysis 52, no. 4 (January 2008): 2166–76. http://dx.doi.org/10.1016/j.csda.2007.07.020.

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19

PROGRI, ILIR F., MATTHEW C. BROMBERG, and WILLIAM R. MICHALSON. "Maximum-Likelihood GPS Parameter Estimation." Navigation 52, no. 4 (December 2005): 229–38. http://dx.doi.org/10.1002/j.2161-4296.2005.tb00365.x.

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20

Groeneboom, Piet, Geurt Jongbloed, and Birgit I. Witte. "Maximum smoothed likelihood estimation and smoothed maximum likelihood estimation in the current status model." Annals of Statistics 38, no. 1 (February 2010): 352–87. http://dx.doi.org/10.1214/09-aos721.

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21

Park, Junyong. "Simultaneous estimation based on empirical likelihood and general maximum likelihood estimation." Computational Statistics & Data Analysis 117 (January 2018): 19–31. http://dx.doi.org/10.1016/j.csda.2017.08.003.

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22

Fu, Y. X., and W. H. Li. "Maximum likelihood estimation of population parameters." Genetics 134, no. 4 (August 1, 1993): 1261–70. http://dx.doi.org/10.1093/genetics/134.4.1261.

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Abstract One of the most important parameters in population genetics is theta = 4Ne mu where Ne is the effective population size and mu is the rate of mutation per gene per generation. We study two related problems, using the maximum likelihood method and the theory of coalescence. One problem is the potential improvement of accuracy in estimating the parameter theta over existing methods and the other is the estimation of parameter lambda which is the ratio of two theta's. The minimum variances of estimates of the parameter theta are derived under two idealized situations. These minimum variances serve as the lower bounds of the variances of all possible estimates of theta in practice. We then show that Watterson's estimate of theta based on the number of segregating sites is asymptotically an optimal estimate of theta. However, for a finite sample of sequences, substantial improvement over Watterson's estimate is possible when theta is large. The maximum likelihood estimate of lambda = theta 1/theta 2 is obtained and the properties of the estimate are discussed.

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23

Mardia, K. V., J. T. Kent, G. Hughes, and C. C. Taylor. "Maximum likelihood estimation using composite likelihoods for closed exponential families." Biometrika 96, no. 4 (October 29, 2009): 975–82. http://dx.doi.org/10.1093/biomet/asp056.

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24

Landau, H. J. "Maximum entropy and maximum likelihood in spectral estimation." IEEE Transactions on Information Theory 44, no. 3 (May 1998): 1332–36. http://dx.doi.org/10.1109/18.669428.

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25

Nusrang, Muhammad, Suwardi Annas, Asfar, and Jajang. "Performa Restricted Maximum Likelihood and Maximum Likelihood Estimators on Small Area Estimation." Journal of Physics: Conference Series 1028 (June 2018): 012234. http://dx.doi.org/10.1088/1742-6596/1028/1/012234.

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26

Larsen, Pia Veldt. "Maximum Penalized Likelihood Estimation, Volume I, Density Estimation." Journal of the Royal Statistical Society: Series A (Statistics in Society) 167, no. 2 (May 2004): 378. http://dx.doi.org/10.1111/j.1467-985x.2004.t01-4-.x.

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27

Karr, Alan F. "Maximum Penalized Likelihood Estimation, Vol. I: Density Estimation." Journal of the American Statistical Association 98, no. 462 (June 2003): 493. http://dx.doi.org/10.1198/jasa.2003.s276.

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28

Wesolowski, K. "Adaptive Channel Estimation for Maximum Likelihood Sequence Estimation." IFAC Proceedings Volumes 25, no. 14 (July 1992): 523–28. http://dx.doi.org/10.1016/s1474-6670(17)50786-7.

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29

AA and Scott R. Eliason. "Maximum Likelihood Estimation: Logic and Practice." Journal of the American Statistical Association 89, no. 427 (September 1994): 1150. http://dx.doi.org/10.2307/2290971.

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30

Pal, Nabendu, and J. Calvin Berry. "On Invariance and Maximum Likelihood Estimation." American Statistician 46, no. 3 (August 1992): 209. http://dx.doi.org/10.2307/2685216.

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31

Hengartner, Nicolas W. "A Note on Maximum Likelihood Estimation." American Statistician 53, no. 2 (May 1999): 123. http://dx.doi.org/10.2307/2685730.

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32

Mak, T. K. "Maximum likelihood estimation of familial correlations." Communications in Statistics - Theory and Methods 23, no. 8 (January 1994): 2351–65. http://dx.doi.org/10.1080/03610929408831391.

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33

Douglas, J. B. "User friendly computerized maximum likelihood estimation." Communications in Statistics - Theory and Methods 15, no. 3 (January 1986): 727–45. http://dx.doi.org/10.1080/03610928608829148.

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34

Chambers, R. L., L. Woyzbun, and R. Pillig. "MAXIMUM LIKELIHOOD ESTIMATION OF GROSS FLOWS." Australian Journal of Statistics 30, no. 2 (June 1988): 149–62. http://dx.doi.org/10.1111/j.1467-842x.1988.tb00845.x.

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35

Fan, J., M. Farmen, and I. Gijbels. "Local maximum likelihood estimation and inference." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 60, no. 3 (August 1998): 591–608. http://dx.doi.org/10.1111/1467-9868.00142.

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36

Elliott, R. J., J. B. Moore, and S. Dey. "Risk-sensitive maximum likelihood sequence estimation." IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 43, no. 9 (1996): 805–10. http://dx.doi.org/10.1109/81.536754.

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37

Jayaraj, Akshay, Sanjeev Tannirkulam Chandrasekaran, Archana Ganesh, Imon Banerjee, and Arindam Sanyal. "Maximum Likelihood Estimation-Based SAR ADC." IEEE Transactions on Circuits and Systems II: Express Briefs 66, no. 8 (August 2019): 1311–15. http://dx.doi.org/10.1109/tcsii.2018.2886260.

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38

Pal, Nabendu, and J. Calvin Berry. "On Invariance and Maximum Likelihood Estimation." American Statistician 46, no. 3 (August 1992): 209–12. http://dx.doi.org/10.1080/00031305.1992.10475886.

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39

Hengartner, Nicolas W. "A Note on Maximum Likelihood Estimation." American Statistician 53, no. 2 (May 1999): 123–25. http://dx.doi.org/10.1080/00031305.1999.10474444.

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40

Okafor, R. O. "Maximum likelihood estimation from incomplete data." Journal of Applied Statistics 14, no. 1 (January 1987): 23–33. http://dx.doi.org/10.1080/02664768700000003.

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41

Rose, Colin, and M. D. Smith. "Symbolic Maximum Likelihood Estimation with Mathematica." Journal of the Royal Statistical Society: Series D (The Statistician) 49, no. 2 (July 2000): 229–40. http://dx.doi.org/10.1111/1467-9884.00233.

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42

Alfonsi, Aurélien, Ahmed Kebaier, and Clément Rey. "Maximum likelihood estimation for Wishart processes." Stochastic Processes and their Applications 126, no. 11 (November 2016): 3243–82. http://dx.doi.org/10.1016/j.spa.2016.04.026.

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43

Elliott, Robert J., John B. Moore, and Subhrakanti Dey. "Risk-Sensitive Maximum Likelihood Sequence Estimation." IFAC Proceedings Volumes 29, no. 1 (June 1996): 4616–21. http://dx.doi.org/10.1016/s1474-6670(17)58410-4.

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44

Todros, Koby, and Alfred O. Hero. "Measure-Transformed Quasi-Maximum Likelihood Estimation." IEEE Transactions on Signal Processing 65, no. 3 (February 1, 2017): 748–63. http://dx.doi.org/10.1109/tsp.2016.2621732.

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45

Lawrence, Charles E., and Andrew A. Reilly. "Maximum likelihood estimation of subsequence conservation." Journal of Theoretical Biology 113, no. 3 (April 1985): 425–39. http://dx.doi.org/10.1016/s0022-5193(85)80031-x.

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46

Lee, Sik-Yum, and Wai-Yin Poon. "Maximum likelihood estimation of polyserial correlations." Psychometrika 51, no. 1 (March 1986): 113–21. http://dx.doi.org/10.1007/bf02294004.

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47

Sah, N. K., P. R. Sheorey, and L. N. Upadhyaya. "Maximum likelihood estimation of slope stability." International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts 31, no. 1 (February 1994): 47–53. http://dx.doi.org/10.1016/0148-9062(94)92314-0.

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48

Shi, Dawei, Tongwen Chen, and Ling Shi. "Event-triggered maximum likelihood state estimation." Automatica 50, no. 1 (January 2014): 247–54. http://dx.doi.org/10.1016/j.automatica.2013.10.005.

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49

Moraga-González, José Luis, and Matthijs R. Wildenbeest. "Maximum likelihood estimation of search costs." European Economic Review 52, no. 5 (July 2008): 820–48. http://dx.doi.org/10.1016/j.euroecorev.2007.06.025.

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50

Ronning, G. "Maximum likelihood estimation of dirichlet distributions." Journal of Statistical Computation and Simulation 32, no. 4 (July 1989): 215–21. http://dx.doi.org/10.1080/00949658908811178.

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