Relevant bibliographies by topics / Unconditional Maximum Likelihood

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Academic literature on the topic 'Unconditional Maximum Likelihood'

Author: Grafiati
Published: 4 June 2025
Last updated: 6 July 2025

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Journal articles on the topic "Unconditional Maximum Likelihood"

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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, which has been studied by Sanathanan, Andersen, Tjur, Thissen, and others. Our theoretical results lead to suggestions for alternative computational algorithms.
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Hernandez, Dario Bonilla, David Covarrubias Rosales, and Jose Arceo Olague. "Near Field Source Separation Improvement Through Unconditional Maximum Likelihood Estimator." IEEE Latin America Transactions 4, no. 6 (2006): 403–8. http://dx.doi.org/10.1109/tla.2006.4472144.

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Mestre, Xavier, and Pascal Vallet. "On the Resolution Probability of Conditional and Unconditional Maximum Likelihood DoA Estimation." IEEE Transactions on Signal Processing 68 (August 1, 2020): 4656–71. https://doi.org/10.1109/TSP.2020.3015046.

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After decades of research in Direction of Arrival (DoA) estimation, today Maximum Likelihood (ML) algorithms still provide the best performance in terms of resolution capabilities. At the cost of a multidimensional search, ML algorithms achieve a significant reduction of the outlier production mechanism in the threshold region, where the number of snapshots per antenna and/or the signal to noise ratio (SNR) are low. The objective of this paper is to characterize the resolution capabilities of ML algorithms in the threshold region. Both conditional and unconditional versions of the ML algorithms are investigated in the asymptotic regime where both the number of antennas and the number of snapshots are large but comparable in magnitude. By using random matrix theory techniques, the finite dimensional distributions of both cost functions are shown to be Gaussian distributed in this asymptotic regime, and a closed form expression of the corresponding asymptotic covariance matrices is provided. These results allow to characterize the asymptotic behavior of the resolution probability, which is defined as the probability that the cost function evaluated at the true DoAs is smaller than the values that it takes at the positions of the other asymptotic local minima.
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Katz, Ethan. "Bias in Conditional and Unconditional Fixed Effects Logit Estimation." Political Analysis 9, no. 4 (2001): 379–84. http://dx.doi.org/10.1093/oxfordjournals.pan.a004876.

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Fixed-effects logit models can be useful in panel data analysis, when N units have been observed for T time periods. There are two main estimators for such models: unconditional maximum likelihood and conditional maximum likelihood. Judged on asymptotic properties, the conditional estimator is superior. However, the unconditional estimator holds several practical advantages, and therefore I sought to determine whether its use could be justified on the basis of finite-sample properties. In a series of Monte Carlo experiments for T < 20, I found a negligible amount of bias in both estimators when T ≥ 16, suggesting that a researcher can safely use either estimator under such conditions. When T < 16, the conditional estimator continued to have a very small amount of bias, but the unconditional estimator developed more bias as T decreased.
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Mestre, Xavier, and Pascal Vallet. "On the Resolution Probability of Conditional and Unconditional Maximum Likelihood DoA Estimation." IEEE Transactions on Signal Processing 68 (2020): 4656–71. http://dx.doi.org/10.1109/tsp.2020.3015046.

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Olague, Jose Arceo, David Covarrubias Rosales, and Jose Luna Rivera. "Efficiency Evaluation of the Unconditional Maximum Likelihood Estimator for Near-Field DOA Estimation." ETRI Journal 28, no. 6 (2006): 761–69. http://dx.doi.org/10.4218/etrij.06.0106.0006.

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Vincent, Francois, Olivier Besson, and Eric Chaumette. "Approximate Unconditional Maximum Likelihood Direction of Arrival Estimation for Two Closely Spaced Targets." IEEE Signal Processing Letters 22, no. 1 (2015): 86–89. http://dx.doi.org/10.1109/lsp.2014.2348011.

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He, Zhulin, and Babette A. Brumback. "An Equivalence of Conditional and Unconditional Maximum Likelihood Estimators via Infinite Replication of Observations." Communications in Statistics - Theory and Methods 42, no. 18 (2013): 3267–79. http://dx.doi.org/10.1080/03610926.2011.626547.

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Shin, Dong Wan, and Wayne Fuller. "Unit Root Tests Based on Unconditional Maximum Likelihood Estimation for the Autoregressive Moving Average." Journal of Time Series Analysis 19, no. 5 (1998): 591–99. http://dx.doi.org/10.1111/1467-9892.00110.

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Çekli, Erdinç, and Hakan A. Çırpan. "Unconditional Maximum Likelihood Approach for Localization of Near-Field Sources: Algorithm and Performance Analysis." AEU - International Journal of Electronics and Communications 57, no. 1 (2003): 9–15. http://dx.doi.org/10.1078/1434-8411-54100135.

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Book chapters on the topic "Unconditional Maximum Likelihood"

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Engle, Robert F. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation." In Arch. Oxford University PressOxford, 1995. http://dx.doi.org/10.1093/oso/9780198774310.003.0001.

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Abstract Traditional econometric models assume a constant one-period forecast variance. To generalize this implausible assumption, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced in this paper. These are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances. For such processes, the recent past gives information about the one-period fore cast variance. A regression model is then introduced with disturbances following an ARCH process. Maximum likelihood estimators are described and a simple scoring iteration formulated. Ordinary least squares maintains its optimality properties in this set-up, but maximum likelihood is more efficient. The relative efficiency is calculated and can be infinite. To test whether the disturbances follow an ARCH process, the Lagrange multiplier procedure is employed. The test is based simply on the autocorrelation of the squared OLS residuals.
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Bollerslev, Tim. "Generalized Autoregressive Conditional Heteroskedasticity." In Arch. Oxford University PressOxford, 1995. http://dx.doi.org/10.1093/oso/9780198774310.003.0003.

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Abstract A natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) to allow for past conditional variances in the current conditional variance equation is proposed. Stationarity conditions and autocorrelation structure for this new class of parametric models are derived. Maximum likelihood estimation and testing are also considered. Finally an empirical example relating to the uncertainty of the inflation rate is presented. While conventional time-series and econometric models operate under an assumption of constant variance, the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) allows the conditional variance to change over time as a function of past errors leaving the unconditional variance constant.
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Conference papers on the topic "Unconditional Maximum Likelihood"

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Choqueuse, V., A. Belouchrani, G. Bouleux, and M. E. H. Benbouzid. "Voltage sags estimation in three-phase systems using Unconditional Maximum Likelihood estimation." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178495.

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Gao, Guohua, Hao Lu, Kefei Wang, et al. "A Practical Approach to Select Representative Deterministic Models Using Multi-Objective Optimization from an Integrated Uncertainty Quantification Workflow." In SPE Reservoir Simulation Conference. SPE, 2023. http://dx.doi.org/10.2118/212242-ms.

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Abstract Selecting a set of deterministic (e.g., P10, P50 and P90) models is an important and difficult step in any uncertainty quantification workflow. In this paper, we propose to use multi-objective optimization to find a reasonable balance between often conflicting features that must be captured by these models. We embed this approach into a streamlined uncertainty quantification workflow that seamlessly integrates multi-realization history-matching (MHM), production forecasting with uncertainty ranges and representative, deterministic model selection. Some uncertain parameters strongly impact simulated responses representing historic (production) data and are selected as active parameters for history-matching, whereas others are important only for forecasting. An ensemble of conditional realizations of active history match parameters is generated in the MHM stage using a distributed optimizer, integrated with either randomized-maximum-likelihood (RML) or Gaussian-mixture-model (GMM). This ensemble is extended with unconditional realizations of forecast parameters generated by sampling from their prior distribution. Based on production forecasting results from simulations of this ensemble representing the posterior uncertainty distribution, representative (P10/P50/P90) models are selected using multi-objective optimization. In addition to matching target values of the primary and a few secondary key performance indicators (e.g., cumulative oil/gas/water production, recovery factor, etc.), selected representative models often must satisfy other requirements or constraints, e.g., the value of some key parameters must be within a user specified tight range. It can be quite difficult to find a set of representative models that satisfy all requirements. Even more challenging, some requirements may be conflicting with others such that no single model can satisfy all requirements. To overcome these technical difficulties, this paper proposes formulating different requirements and constraints as objectives and applying a multi-objective optimization strategy to find a set of Pareto optimal solutions based on the concept of dominance. One or more representative models can then be selected from the set of optimal solutions according to case dependent preferences or requirements. The proposed method is tested and validated on a realistic example. Our results confirm that the proposed method is robust and efficient and finds acceptable solutions with no violation or minimal violations of constraints (when conflicting constraints are present). These results suggest that our advanced multi-objective optimization technique can select high-quality representative models by striking a balance between conflicting constraints. Thus, a better decision can be made while running much fewer simulations than would be required with traditional methods.
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Barker, A. T., C. S. Lee, F. Forouzanfar, A. Guion, and X. H. Wu. "Scalable Hierarchical Multilevel Sampling of Lognormal Fields Conditioned on Measured Data." In SPE Reservoir Simulation Conference. SPE, 2021. http://dx.doi.org/10.2118/203907-ms.

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Abstract We explore the problem of drawing posterior samples from a lognormal permeability field conditioned by noisy measurements at discrete locations. The underlying unconditioned samples are based on a scalable PDE-sampling technique that shows better scalability for large problems than the traditional Karhunen-Loeve sampling, while still allowing for consistent samples to be drawn on a hierarchy of spatial scales. Lognormal random fields produced in this scalable and hierarchical way are then conditioned to measured data by a randomized maximum likelihood approach to draw from a Bayesian posterior distribution. The algorithm to draw from the posterior distribution can be shown to be equivalent to a PDE-constrained optimization problem, which allows for some efficient computational solution techniques. Numerical results demonstrate the efficiency of the proposed methods. In particular, we are able to match statistics for a simple flow problem on the fine grid with high accuracy and at much lower cost on a scale of coarser grids.
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