Relevant bibliographies by topics / Bayesian estimators

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Bayesian estimators'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 June 2025

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Journal articles on the topic "Bayesian estimators"

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Hu, Xue, and Haiping Ren. "Statistical inference of the stress-strength reliability for inverse Weibull distribution under an adaptive progressive type-Ⅱ censored sample." AIMS Mathematics 8, no. 12 (2023): 28465–87. http://dx.doi.org/10.3934/math.20231457.

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<abstract><p>In this paper, we investigate classical and Bayesian estimation of stress-strength reliability $\delta = P(X > Y)$ under an adaptive progressive type-Ⅱ censored sample. Assume that $X$ and $Y$ are independent random variables that follow inverse Weibull distribution with the same shape but different scale parameters. In classical estimation, the maximum likelihood estimator and asymptotic confidence interval are deduced. An approximate maximum likelihood estimator approach is used to obtain the explicit form. In Bayesian estimation, the Bayesian estimators are derived based on symmetric entropy loss function and LINEX loss function. Due to the complexity of integrals, we proposed Lindley's approximation to get the approximate Bayesian estimates. To compare the different estimators, we performed Monte Carlo simulations. Under gamma prior, the approximate maximum likelihood estimator performs better than Bayesian estimators. Under non-informative prior, the approximate maximum likelihood estimator has the same behavior as Bayesian estimators. In the end, two data sets are used to prove the effectiveness of the proposed methods.</p></abstract>
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Neath, Andrew A., and Natalie Langenfeld. "A Note on the Comparison of the Bayesian and Frequentist Approaches to Estimation." Advances in Decision Sciences 2012 (October 22, 2012): 1–12. http://dx.doi.org/10.1155/2012/764254.

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Samaniego and Reneau presented a landmark study on the comparison of Bayesian and frequentist point estimators. Their findings indicate that Bayesian point estimators work well in more situations than were previously suspected. In particular, their comparison reveals how a Bayesian point estimator can improve upon a frequentist point estimator even in situations where sharp prior knowledge is not necessarily available. In the current paper, we show that similar results hold when comparing Bayesian and frequentist interval estimators. Furthermore, the development of an appropriate interval estimator comparison offers some further insight into the estimation problem.
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Gisler, Alois, and Mario V. Wüthrich. "Credibility for the Chain Ladder Reserving Method." ASTIN Bulletin 38, no. 02 (2008): 565–600. http://dx.doi.org/10.2143/ast.38.2.2033354.

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We consider the chain ladder reserving method in a Bayesian set up, which allows for combining the information from a specific claims development triangle with the information from a collective. That is, for instance, to consider simultaneously own company specific data and industry-wide data to estimate the own company's claims reserves. We derive Bayesian estimators and credibility estimators within this Bayesian framework. We show that the credibility estimators are exact Bayesian in the case of the exponential dispersion family with its natural conjugate priors. Finally, we make the link to the classical chain ladder method and we show that using non-informative priors we arrive at the classical chain ladder forecasts. However, the estimates for the mean square error of prediction differ in our Bayesian set up from the ones found in the literature. Hence, the paper also throws a new light upon the estimator of the mean square error of prediction of the classical chain ladder forecasts and suggests a new estimator in the chain ladder method.
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Gisler, Alois, and Mario V. Wüthrich. "Credibility for the Chain Ladder Reserving Method." ASTIN Bulletin 38, no. 2 (2008): 565–600. http://dx.doi.org/10.1017/s0515036100015294.

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We consider the chain ladder reserving method in a Bayesian set up, which allows for combining the information from a specific claims development triangle with the information from a collective. That is, for instance, to consider simultaneously own company specific data and industry-wide data to estimate the own company's claims reserves. We derive Bayesian estimators and credibility estimators within this Bayesian framework. We show that the credibility estimators are exact Bayesian in the case of the exponential dispersion family with its natural conjugate priors. Finally, we make the link to the classical chain ladder method and we show that using non-informative priors we arrive at the classical chain ladder forecasts. However, the estimates for the mean square error of prediction differ in our Bayesian set up from the ones found in the literature. Hence, the paper also throws a new light upon the estimator of the mean square error of prediction of the classical chain ladder forecasts and suggests a new estimator in the chain ladder method.
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Shojaee, Omid, Hassan Zarei, and Fatemeh Naruei. "E-Bayesian estimation and the corresponding E-MSE under progressive type-II censored data for some characteristics of Weibull distribution." Statistics, Optimization & Information Computing 12, no. 4 (2023): 962–81. http://dx.doi.org/10.19139/soic-2310-5070-1709.

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Estimating the parameters (or characteristics) of a distribution, from the availability of censored samples, is one of the most important topics in statistical inference over the past decades. This study is concerned about the E-Bayesian estimation method to compute the estimates of the parameter, the hazard rate function and the reliability function of the Weibull distribution when the progressive type-2 censored samples are available. The estimations are obtained based on the Squared error loss function (as a symmetric loss) and General Entropy and LINEX loss functions (as asymmetric losses). In addition, the asymptotic behaviour of the derived E-Bayesian estimators is discussed. Moreover, the E-Bayesian estimators under the different loss functions have been compared through Monte Carlo simulation studies by calculating the E-MSE of the resulting estimators, which is a new measure to compare the E-Bayesian estimators. As an application, we analyzed two real data sets that follow from the Weibull distribution.
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Keddali, Meriem, Hamida Talhi, Nawel Khodja, and Assia Chadli. "Bayesian robust analysis of the truncated XLindley distribution." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e8849. http://dx.doi.org/10.54021/seesv5n2-302.

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In this study, we offer a robust analysis of the Bayesian estimators using the oscillation of posterior risks (PR) of the two parameters Upper truncated XLindley (UXLE) model, which is a novel variant of the Lindley model. We introduce the model together with its likelihood function in a censored scheme. Nonetheless, a relatively small number of writers addressed the subject of robustness and sensitivity analysis of the Bayesian estimators. For this reason, it can be said that there have only been a small number of applications produced in this area up to this point. The technique is explained using the oscillation of the Bayesian estimator’s posterior risks. Through the application of a Monte Carlo simulation study, we demonstrate that a robust Bayesian estimator of the parameters corresponding the smallest oscillation of the posterior risks maybe obtained under the right generalized loss function; when the parameters are not high, robust estimators can be obtained.
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Hassan, Amal Soliman, Elsayed Ahmed Elsherpieny, and Rokaya Elmorsy Mohamed. "Estimation of Information Measures for Power-Function Distribution in Presence of Outliers and Their Applications." Journal of Information and Communication Technology 21, No.1 (2021): 1–25. http://dx.doi.org/10.32890/jict2022.21.1.1.

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The measure of entropy has an undeniable pivotal role in the field of information theory. This article estimates the Rényi and q-entropies of the power function distribution in the presence of s outliers. The maximum likelihood estimators as well as the Bayesian estimators under uniform and gamma priors are derived. The proposed Bayesian estimators of entropies under symmetric and asymmetric loss functions are obtained. These estimators are computed empirically using Monte Carlo simulation based on Gibbs sampling. Outcomes of the study showed that the precision of the maximum likelihood and Bayesian estimates of both entropies measures improves with sample sizes. The behavior of both entropies estimates increase with number of outliers. Further, Bayesian estimates of the Rényi and q-entropies under squared error loss function are preferable than the other Bayesian estimates under the other loss functions in most of cases. Eventually, real data examples are analyzed to illustrate the theoretical results.
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He, Kexin, and Wenhao Gui. "Reliability Estimation for Burr XII Distribution under the Weighted Q-Symmetric Entropy Loss Function." Applied Sciences 14, no. 8 (2024): 3308. http://dx.doi.org/10.3390/app14083308.

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Considering that the choice of loss function plays a significant role in the derivation of Bayesian estimators, we propose a novel asymmetric loss function named the weighted Q-symmetric entropy loss for computing the estimates of the parameter and reliability function of the Burr XII distribution. This paper covers the classical maximum-likelihood, uniformly minimum-variance unbiased, and Bayesian estimation methods under the squared error loss, general entropy loss, Q-symmetric entropy loss, and new loss functions. Through Monte Carlo simulation, the respective performances of the considered estimators for the reliability function are evaluated, indicating that the Bayesian estimator under the new loss function is more efficient than those under other loss functions. Finally, a real data set is used to demonstrate the practicality of the presented estimators.
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Uma, Srivastava, and Kumar Harish. "Estimation of Cut Point in Burr III Sequence under Linear Exponential Loss." International Journal of Innovative Science and Research Technology 7, no. 7 (2022): 501–8. https://doi.org/10.5281/zenodo.6956269.

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This paper estimates the single cut-point in the mean of a Burr III Sequence and its scale parameters before and after the cut point. We introduce a strong estimator of the parameters with the help of Bayesian inference approach, by persevering these estimatorsin the criteria used to estimate the cut-point under Linear Exponential Loss Function. The simulation technique is used compare the estimators. Open-source R software is used in the simulation section. We have taken real data to estimate the parameters of the sequence and then hypothetical observations of the sequence to prove their robustness of the estimators.
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Alharbi, Yasser S., and Amr R. Kamel. "Fuzzy System Reliability Analysis for Kumaraswamy Distribution: Bayesian and Non-Bayesian Estimation with Simulation and an Application on Cancer Data Set." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 19 (June 7, 2022): 118–39. http://dx.doi.org/10.37394/23208.2022.19.14.

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This paper proposes the fuzzy Bayesian (FB) estimation to get the best estimate of the unknown parameters of a two-parameter Kumaraswamy distribution from a frequentist point of view. These estimations of parameters are employed to estimate the fuzzy reliability function of the Kumaraswamy distribution and to select the best estimate of the parameters and fuzzy reliability function. To achieve this goal we investigate the efficiency of seven classical estimators and compare them with FB proposed estimation. Monte Carlo simulations and cancer data set applications are performed to compare the performances of the estimators for both small and large samples. Tierney and Kadane approximation is used to obtain FB estimates of traditional and fuzzy reliability for the Kumaraswamy distribution. The results showed that the fuzziness is better than the reality for all sample sizes and the fuzzy reliability at the estimates of the FB proposed estimated is better than other estimators, it gives the lowest Bias and root mean squared error.
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Dissertations / Theses on the topic "Bayesian estimators"

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Rastelli, Riccardo, and Nial Friel. "Optimal Bayesian estimators for latent variable cluster models." Springer Nature, 2018. http://dx.doi.org/10.1007/s11222-017-9786-y.

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In cluster analysis interest lies in probabilistically capturing partitions of individuals, items or observations into groups, such that those belonging to the same group share similar attributes or relational profiles. Bayesian posterior samples for the latent allocation variables can be effectively obtained in a wide range of clustering models, including finite mixtures, infinite mixtures, hidden Markov models and block models for networks. However, due to the categorical nature of the clustering variables and the lack of scalable algorithms, summary tools that can interpret such samples are not available. We adopt a Bayesian decision theoretical approach to define an optimality criterion for clusterings and propose a fast and context-independent greedy algorithm to find the best allocations. One important facet of our approach is that the optimal number of groups is automatically selected, thereby solving the clustering and the model-choice problems at the same time. We consider several loss functions to compare partitions and show that our approach can accommodate a wide range of cases. Finally, we illustrate our approach on both artificial and real datasets for three different clustering models: Gaussian mixtures, stochastic block models and latent block models for networks.
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Plourde, Eric. "Bayesian short-time spectral amplitude estimators for single-channel speech enhancement." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66864.

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Single-channel speech enhancement algorithms are used to remove background noise in speech. They are present in many common devices such as cell phones and hearing aids. In the Bayesian short-time spectral amplitude (STSA) approach for speech enhancement, an estimate of the clean speech STSA is derived by minimizing the statistical expectation of a chosen cost function. Examples of such estimators are the minimum mean square error (MMSE) STSA, the β-order MMSE STSA (β-SA), which includes a power law parameter, and the weighted Euclidian (WE), which includes a weighting parameter. This thesis analyzes single-channel Bayesian STSA estimators for speech enhancement with the aim of, firstly, gaining a better understanding of their properties and, secondly, proposing new cost functions and statistical models to improve their performance. In addition to a novel analysis of the β-SA estimator for parameter β ≤ 0, three new families of estimators are developed in this thesis: the Weighted β-SA (Wβ-SA), the Generalized Weighted family of STSA estimators (GWSA) and a family of multi-dimensional Bayesian STSA estimators. The Wβ-SA combines the power law of the β-SA and the weighting factor of the WE. Its parameters are chosen based on the characteristics of the human auditory system which is found to have the advantage of improving the noise reduction at high frequencies while limiting the speech distortions at low frequencies. An analytical generalization of a cost function structure found in many existing Bayesian STSA estimators is proposed through the GWSA family of estimators. This allows a unification of Bayesian STSA estimators and, moreover, provides a better understanding of this general class of estimators. Finally, we propose a multi-dimensional family of estimators that accounts for the correlated frequency components in a digitized speech signal. In fact, the spectral components of the clean<br>Les algorithmes de rehaussement de la parole à voie unique sont utilisés afin de réduire le bruit de fond d'un signal de parole bruité. Ils sont présents dans plusieurs appareils tels que les téléphones sans fil et les prothèses auditives. Dans l'approche bayésienne d'estimation de l'amplitude spectrale locale (Short-Time Spectral Amplitude - STSA) pour le rehaussement de la parole, un estimé de la STSA non bruitée est déterminé en minimisant l'espérance statistique d'une fonction de coût. Ce type d'estimateurs incluent le MMSE STSA, le β-SA, qui intègre un exposant comme paramètre de la fonction de coût, et le WE, qui possède un paramètre de pondération.Cette thèse étudie les estimateurs bayésiens du STSA avec pour objectifs d'approfondir la compréhension de leurs propriétés et de proposer de nouvelles fonctions de coût ainsi que de nouveaux modèles statistiques afin d'améliorer leurs performances. En plus d'une étude approfondie de l'estimateur β-SA pour les valeurs de β ≤ 0, trois nouvelles familles d'estimateur sont dévelopées dans cette thèse: le β-SA pondéré (Weighted β-SA - Wβ-SA), une famille d'estimateur du STSA généralisé et pondéré (Generalized Weighted STSA - GWSA) ainsi qu'une famille d'estimateur du STSA multi-dimensionnel.Le Wβ-SA combine l'exposant présent dans le β-SA et le paramètre de pondération du WE. Ses paramètres sont choisis en considérant certaines caractéristiques du système auditif humain ce qui a pour avantage d'améliorer la réduction du bruit de fond à hautes fréquences tout en limitant les distorsions de la parole à basses fréquences. Une généralisation de la structure commune des fonctions de coût de plusieurs estimateurs bayésiens du STSA est proposée à l'aide de la famille d'estimateur GWSA. Cette dernière permet une unification des estimateurs bayésiens du STSA et apporte une meilleure compréhensio
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He, Qing. "Investigating the performance of process-observation-error-estimator and robust estimators in surplus production model: a simulation study." Thesis, Virginia Tech, 2010. http://hdl.handle.net/10919/76859.

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This study investigated the performance of the three estimators of surplus production model including process-observation-error-estimator with normal distribution (POE_N), observation-error-estimator with normal distribution (OE_N), and process-error-estimator with normal distribution (PE_N). The estimators with fat-tailed distributions including Student's t distribution and Cauchy distribution were also proposed and their performances were compared with the estimators with normal distribution. This study used Bayesian method, revised Metropolis Hastings within Gibbs sampling algorithm (MHGS) that was previously used to solve POE_N (Millar and Meyer, 2000), developed the MHGS for the other estimators, and developed the methodologies which enabled all the estimators to deal with data containing multiple indices based on catch-per-unit-effort (CPUE). Simulation study was conducted based on parameter estimation from two example fisheries: the Atlantic weakfish (Cynoscion regalis) and the black sea bass (Centropristis striata) southern stock. Our results indicated that POE_N is the estimator with best performance among all six estimators with regard to both accuracy and precision for most of the cases. POE_N is also the robust estimator to outliers, atypical values, and autocorrelated errors. OE_N is the second best estimator. PE_N is often imprecise. Estimators with fat-tailed distribution usually result in some estimates more biased than estimators with normal distribution. The performance of POE_N and OE_N can be improved by fitting multiple indices. Our study suggested that POE_N be used for population dynamic models in future stock assessment. Multiple indices from valid surveys should be incorporated into stock assessment models. OE_N can be considered when multiple indices are available.<br>Master of Science
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Astl, Stefan Ludwig. "Suboptimal LULU-estimators in measurements containing outliers." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/85833.

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Thesis (MSc)--Stellenbosch University, 2013.<br>ENGLISH ABSTRACT: Techniques for estimating a signal in the presence of noise which contains outliers are currently not well developed. In this thesis, we consider a constant signal superimposed by a family of noise distributions structured as a tunable mixture f(x) = α g(x) + (1 − α) h(x) between finitesupport components of “well-behaved” noise with small variance g(x) and of “impulsive” noise h(x) with a large amplitude and strongly asymmetric character. When α ≈ 1, h(x) can for example model a cosmic ray striking an experimental detector. In the first part of our work, a method for obtaining the expected values of the positive and negative pulses in the first resolution level of a LULU Discrete Pulse Transform (DPT) is established. Subsequent analysis of sequences smoothed by the operators L1U1 or U1L1 of LULU-theory shows that a robust estimator for the location parameter for g is achieved in the sense that the contribution by h to the expected average of the smoothed sequences is suppressed to order (1 − α)2 or higher. In cases where the specific shape of h can be difficult to guess due to the assumed lack of data, it is thus also shown to be of lesser importance. Furthermore, upon smoothing a sequence with L1U1 or U1L1, estimators for the scale parameters of the model distribution become easily available. In the second part of our work, the same problem and data is approached from a Bayesian inference perspective. The Bayesian estimators are found to be optimal in the sense that they make full use of available information in the data. Heuristic comparison shows, however, that Bayes estimators do not always outperform the LULU estimators. Although the Bayesian perspective provides much insight into the logical connections inherent in the problem, its estimators can be difficult to obtain in analytic form and are slow to compute numerically. Suboptimal LULU-estimators are shown to be reasonable practical compromises in practical problems.<br>AFRIKAANSE OPSOMMING: Tegnieke om ’n sein af te skat in die teenwoordigheid van geraas wat uitskieters bevat is tans nie goed ontwikkel nie. In hierdie tesis aanskou ons ’n konstante sein gesuperponeer met ’n familie van geraasverdelings wat as verstelbare mengsel f(x) = α g(x) + (1 − α) h(x) tussen eindige-uitkomsruimte geraaskomponente g(x) wat “goeie gedrag” en klein variansie toon, plus “impulsiewe” geraas h(x) met groot amplitude en sterk asimmetriese karakter. Wanneer α ≈ 1 kan h(x) byvoorbeeld ’n kosmiese straal wat ’n eksperimentele apparaat tref modelleer. In die eerste gedeelte van ons werk word ’n metode om die verwagtingswaardes van die positiewe en negatiewe pulse in die eerste resolusievlak van ’n LULU Diskrete Pulse Transform (DPT) vasgestel. Die analise van rye verkry deur die inwerking van die gladstrykers L1U1 en U1L1 van die LULU-teorie toon dat hul verwagte gemiddelde waardes as afskatters van die liggingsparameter van g kan dien wat robuus is in die sin dat die bydrae van h tot die gemiddeld van orde grootte (1 − α)2 of hoër is. Die spesifieke vorm van h word dan ook onbelangrik. Daar word verder gewys dat afskatters vir die relevante skaalparameters van die model maklik verkry kan word na gladstryking met die operatore L1U1 of U1L1. In die tweede gedeelte van ons werk word dieselfde probleem en data vanuit ’n Bayesiese inferensie perspektief benader. Die Bayesiese afskatters word as optimaal bevind in die sin dat hulle vol gebruikmaak van die beskikbare inligting in die data. Heuristiese vergelyking wys egter dat Bayesiese afskatters nie altyd beter vaar as die LULU afskatters nie. Alhoewel die Bayesiese sienswyse baie insig in die logiese verbindings van die probleem gee, kan die afskatters moeilik wees om analities af te lei en stadig om numeries te bereken. Suboptimale LULU-beramers word voorgestel as redelike praktiese kompromieë in praktiese probleme.
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Li, Yuan. "Hierarchical Bayesian Model for AK Composite Estimators in the Current Population Survey (CPS)." Thesis, The George Washington University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10748002.

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<p> The Current Population Survey (CPS) is a multistage probability sample survey conducted by the U.S. Census Bureau and the Bureau of Labor Statistics (BLS). The 4-8-4 rotation design is applied to produce overlap in the sample across months. Several weighting steps are used to adjust the ultimate sample in each month to be representative of the population. In order to produce efficient estimates of labor force levels and month-to-month change, the so-called AK composite estimator combines current estimates from eight rotation panels and the previous month&rsquo;s estimates to estimate current values. Values of coefficients A and K are chosen every decade or so for the nation. The Successive Difference Replicate (SDR) method and Balanced Repeated Replication (BRR) method are currently used by the CPS for estimating the variance of the AK Composite Estimates. </p><p> Instead of using constant CPS (<i>A, K</i>) values for AK Composite Estimator over time, one could find the monthly optimal coefficients (<i> A, K</i>) that minimize the variance for measuring the monthly level of unemployment in the target population. The CPS (<i>A, K</i>) values are stable over time but can produce larger variance in some months, while the monthly optimal (<i>A, K</i>) values have lower variance within a month but high variability across months. </p><p> In order to make a compromise between the CPS (<i>A, K</i>) values and monthly optimal (<i>A, K</i>), a Hierarchical Bayesian method is proposed through modeling the obtained monthly optimal (<i> A, K</i>)&rsquo;s using a bivariate normal distribution. The parameters, including the mean vector and the variance-covariance matrix, are unknown in this distribution. In such case, a first step towards a more general model is to assume a conjugate prior distribution for the bivariate normal model. Computing the conditional posterior distribution can be approximated through simulation. In particular, it can be achieved by the Gibbs sampling algorithm with its sequential sampling. As the key to the success of this Hierarchical Bayesian method is that approximated distributions are improved as iteration goes on in the simulation, one needs to check the convergence of the simulated sequences. Then, the sample mean after a number of iterations in the simulation will serve as the Hierarchical Bayesian (HB) (<i>A, K</i>). The HB (<i>A, K</i>) estimates in effect produce a shrinkage between the CPS (<i>A, K</i>) values and the monthly optimal (<i>A, K</i>) values. The shrinkage of the estimates of the coefficients (<i> A, K</i>) occurs by manipulating the certain hyperparameter in the model. </p><p> In this dissertation, detailed comparisons are made among the three estimators. The AK Estimator using the CPS (<i>A, K</i>) values, using the monthly optimal (<i>A, K</i>) values, and using the Hierarchical Bayesian (A,K) values are compared in terms of estimates produced, estimated variance, and estimated coefficients of variation. In each month of the data set, separate estimates using the three methods are produced. </p><p> In order to assess the performance of the proposed methods, a simulation study is implemented and summarized. In the CPS, eight rotating survey panels contribute to the overall estimate in each month. Each panel is measured in a month at one of its month-in- sample. The month-in- sample range from one to eight. In the simulation, month-in- sample values are generated as if replicate panels were available for estimation. These month-in-sample values are used as the original monthly panel estimates of unemployment to produce CPS-style (<i>A, K</i>) estimates, AK-estimates using monthly optimal (<i> A, K</i>) values, and AK-estimates using Hierarchical Bayesian (<i> A, K</i>) values. Performance of each method is evaluated on the simulated data by examining several criteria including bias, variance, and mean squared error.</p><p>
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Chen, Dandan. "Amended Estimators of Several Ratios for Categorical Data." Digital Commons @ East Tennessee State University, 2006. https://dc.etsu.edu/etd/2218.

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Point estimation of several association parameters in categorical data are presented. Typically, a constant is added to the frequency counts before the association measure is computed. We will study the accuracy of these adjusted point estimators based on frequentist and Bayesian methods respectively. In particular, amended estimators for the ratio of independent Poisson rates, relative risk, odds ratio, and the ratio of marginal binomial proportions will be examined in terms of bias and mean squared error.
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Boedeker, Peter. "Comparison of Heterogeneity and Heterogeneity Interval Estimators in Random-Effects Meta-Analysis." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1157553/.

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Meta-analyses are conducted to synthesize the quantitative results of related studies. The random-effects meta-analysis model is based on the assumption that a distribution of true effects exists in the population. This distribution is often assumed to be normal with a mean and variance. The population variance, also called heterogeneity, can be estimated numerous ways. Accurate estimation of heterogeneity is necessary as a description of the distribution and for determining weights applied in the estimation of the summary effect when using inverse-variance weighting. To evaluate a wide range of estimators, we compared 16 estimators (Bayesian and non-Bayesian) of heterogeneity with regard to bias and mean square error over conditions based on reviews of educational and psychological meta-analyses. Three simulation conditions were varied: (a) sample size per meta-analysis, (b) true heterogeneity, and (c) sample size per effect size within each meta-analysis. Confidence or highest density intervals can be calculated for heterogeneity. The heterogeneity estimators that performed best over the widest range of conditions were paired with heterogeneity interval estimators. Interval estimators were evaluated based on coverage probability, interval width, and coverage of the estimated value. The combination of the Paule Manel estimator and Q-Profile interval method is recommended when synthesizing standardized mean difference effect sizes.
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Azevedo, Camila Ferreira. "Ridge, lasso and bayesian additive-dominance genomic models and new estimators for the experimental accuracy of genome selection." Universidade Federal de Viçosa, 2015. http://www.locus.ufv.br/handle/123456789/7176.

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Submitted by Marco Antônio de Ramos Chagas (mchagas@ufv.br) on 2016-01-13T08:21:37Z No. of bitstreams: 1 texto completo.pdf: 1062061 bytes, checksum: 36720a2028bf76afcba32f3865472cd7 (MD5)<br>Made available in DSpace on 2016-01-13T08:21:37Z (GMT). No. of bitstreams: 1 texto completo.pdf: 1062061 bytes, checksum: 36720a2028bf76afcba32f3865472cd7 (MD5) Previous issue date: 2015-10-26<br>A principal contribuição da genética molecular no melhoramento é a utilização direta das informações de DNA no processo de identificação de indivíduos geneticamente superiores. Sob esse enfoque, idealizou-se a seleção genômica ampla (Genome Wide Selection – GWS), a qual consiste na análise de um grande número de marcadores SNPs (Single Nucleotide Polymorphisms) amplamente distribuídos no genoma. Este trabalho de simulação apresenta uma abordagem completa para a seleção genômica por meio de adequados modelos genéticos incluindo efeitos aditivos e devido à dominância, que são essenciais para a seleção de clones e de cruzamentos, bem como para melhorar a estimativa de efeitos aditivos para a seleção. Até o momento, as abordagens via Ridge Bayesiana e Lasso para modelos aditivo-dominante não foram avaliados e comparados na literatura. Neste trabalho, foram avaliados o desempenho de 10 modelos de predição aditivo-dominante (incluindo os modelos existentes e propostas de modificação). Um novo método Bayesiano/Lasso modificado (chamado BayesA* B* ou t-BLASSO) obteve melhor desempenho na estimação de valores genéticos genômicos dos indivíduos, em todos os quatro cenários (dois níveis de herdabilidades × duas arquiteturas genéticas). Os métodos do tipo BayesA*B* apresentaram melhor capacidade para recuperar a razão entre a variância de dominância e a variância aditiva. Além disso, o papel das três fontes de informação da genética quantitativa (chamadas de desequilíbrio de ligação, co-segregação e relações de parentesco) na seleção genômica foram elucidadas pela decomposição da herdabilidade e da acurácia nos três componentes, mostrando suas relações com a estrutura de populações e o melhoramento genético, a curto e longo prazo. Além disso, neste trabalho de simulação também foi desenvolvido dois novos estimadores para a acurácia preditiva da seleção genômica. O trabalho propõe e avalia o desempenho e a eficiência destes novos estimadores chamados estimador regularizado (RE) e estimador híbrido (HE). O estimador regularizado leva em consideração tanto a herdabilidade genômica quanto a herdabilidade da característica, além da capacidade preditiva. Enquanto, o estimador híbrido (HE), combina as acurácias experimental e esperada. As comparações entre RE e HE com o estimador tradicional (TE) foram feitas sob quatro procedimentos de validação. Em geral, RE apresentou acurácias mais próximas aos valores paramétricos, principalmente quando há seleção de marcadores. RE também foi menos tendencioso e mais preciso, com desvios padrão menores do que o estimador tradicional. Diante dos resultados, o TE pode ser usado apenas com a validação independente, em que tende a ter um melhor desempenho do que RE, embora superestimando a acurácia. O estimador híbrido (HE) provou ser muito eficaz na ausência de validação. Enquanto, que a validação independente mostrou-se superior em relação aos procedimentos de Jacknife, perseguindo melhor a acurácia paramétrica com ou sem seleção de marcador. As seguintes inferências podem ser feitas de acordo com o estimador de acurácia e tipo de validação: (i) a acurácia mais provável: HE sem validação; (ii) a maior acurácia possível (acurácia superestimada): TE com validação independente; (iii) a menor acurácia possível (acurácia subestimada): RE com validação independente.<br>The main contribution of molecular genetics is the direct use of DNA information to identify genetically superior individuals. Under this approach, genome-wide selection (GWS) can be used with this purpose. GWS consists in analyzing of a large number of SNP markers widely distributed in the genome. This simulation work presents a complete approach for genomic selection by using adequate genetic models including dominance effects, which are essential for selecting crosses and clones as well as for improving the estimation of additive effects for parent selection. To date, the approaches via Ridge, Lasso and Bayesian additive-dominance models have not been evaluated and compared in the literature.The performance of 10 additive-dominance prediction models (including current ones and proposed modifications) were evaluated. A new modified Bayesian/Lasso method (called BayesA*B* or t-BLASSO) performed best in the prediction of genomic breeding value of individuals, in all the four scenarios (two heritabilities × two genetic architectures). The BayesA*B*-type methods showed better ability for recovering the dominance variance/additive variance ratio. Also, the role of the three quantitative genetics information sources (called linkage disequilibrium, co- segregation and pedigree relationships) in genomic selection were elucidated by decomposing the heritability and accuracy in the three components and showing their relations with the structure of populations and the genetic improvement in the short and long run. Moreover, this simulation work also, we developed the new estimators for the prediction accuracy of genomic selection. The work proposes and evaluates the performance and efficiency of these new estimators called regularized estimator (RE) and hybrid estimator (HE). The regularized estimator takes in consideration both the genomic and trait heritabilities, in addition to the predictive ability. The hybrid estimator (HE), combines both experimental and expected accuracies. The comparisons of the RE and HE with the traditional (TE) were done under four validation procedures. In general, the new estimator presented accuracies closer to the parametric ones, mainly when selecting markers. It was also less biased and more precise, with smaller standard deviations than the traditional estimator. The TE can be used only with independent validation, where it tends to perform better than RE, although overestimating the accuracy. The hybrid estimator (HE) proved to be very effective in the absence of validation. The independent validation showed to be superior over the Jacknife procedures, chasing better the parametric accuracy with or without marker selection. The following inferences can be made according to the accuracy estimator and kind of validation: (i) most probable accuracy: HE without validation; (ii) highest possible accuracy: TE with independent validation; (iii) lowest possible accuracy: RE with independent validation.<br>Sem Agência de Fomento
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Hernandez, Erika Lyn. "Parameter Estimation in Linear-Linear Segmented Regression." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3551.pdf.

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Luo, Shihua. "Bayesian Estimation of Small Proportions Using Binomial Group Test." FIU Digital Commons, 2012. http://digitalcommons.fiu.edu/etd/744.

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Group testing has long been considered as a safe and sensible relative to one-at-a-time testing in applications where the prevalence rate p is small. In this thesis, we applied Bayes approach to estimate p using Beta-type prior distribution. First, we showed two Bayes estimators of p from prior on p derived from two different loss functions. Second, we presented two more Bayes estimators of p from prior on π according to two loss functions. We also displayed credible and HPD interval for p. In addition, we did intensive numerical studies. All results showed that the Bayes estimator was preferred over the usual maximum likelihood estimator (MLE) for small p. We also presented the optimal β for different p, m, and k.
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Books on the topic "Bayesian estimators"

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Kurt, Hoffmann. Improved estimation of distribution parameters: Stein-type estimators. B.G. Teubner, 1992.

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Gruber, Marvin H. J. Regression estimators: A comparative study. Academic Press, 1990.

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Gruber, Marvin H. J. Regression estimators: A comparative study. 2nd ed. Johns Hopkins University Press, 2010.

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Reiser, Benjamin. A comparison of three point estimators for P(Y. University of Toronto, Dept. of Statistics, 1985.

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Tanabe, Kunio. BNDE, FORTRAN subroutines for computing Bayesian nonparametric univariate and bivariate density estimator. Institute of Statistical Mathematics, 1988.

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Houston, Walter M. Empirical Bayes estimates of parameters from the logistic regression model. ACT, Inc., 1997.

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Houston, Walter M. Empirical Bayes estimates of parameters from the logistic regression model. ACT, Inc., 1997.

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1972-, Raymer James, Willekens Frans, and University of Southampton. Division of Social Statistics., eds. International migration in Europe: Data, models and estimates. Wiley, 2008.

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Doppelhofer, Gernot. Determinants of long-term growth: A Bayesian averaging of classical estimates (BACE) approach. National Bureau of Economic Research, 2000.

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Torres, Maura Acevedo. Reduction of Uncertainty in Post-Event Seismic Loss Estimates Using Observation Data and Bayesian Updating. [publisher not identified], 2017.

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Book chapters on the topic "Bayesian estimators"

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Hanson, K. M., and D. R. Wolf. "Estimators for the Cauchy Distribution." In Maximum Entropy and Bayesian Methods. Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8729-7_20.

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Denison, David G. T., and Edward I. George. "Bayesian prediction with adaptive ridge estimators." In Institute of Mathematical Statistics Collections. Institute of Mathematical Statistics, 2012. http://dx.doi.org/10.1214/11-imscoll815.

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Montuelle, L., and E. Le Pennec. "PAC-Bayesian Aggregation of Affine Estimators." In Springer Proceedings in Mathematics & Statistics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96941-1_9.

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Karagiannis, Georgios P. "Introduction to Bayesian Statistical Inference." In Uncertainty in Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83640-5_1.

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AbstractWe present basic concepts of Bayesian statistical inference. We briefly introduce the Bayesian paradigm. We present the conjugate priors; a computational convenient way to quantify prior information for tractable Bayesian statistical analysis. We present tools for parametric and predictive inference, and particularly the design of point estimators, credible sets, and hypothesis tests. These concepts are presented in running examples. Supplementary material is available from GitHub.
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Samaniego, Francisco J. "Improving on Standard Bayesian and Frequentist Estimators." In Springer Series in Statistics. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5941-6_10.

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Wickramasuriya, Dilranjan S., and Rose T. Faghih. "List of Supplementary MATLAB Functions." In Bayesian Filter Design for Computational Medicine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-47104-9_11.

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AbstractAll the MATLAB code examples accompanying this book can be run directly. The examples are self-contained and do not require additional path variables being set up. The following is a partial list of the supplementary MATLAB functions that are called at various stages by the state estimators.
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Samaniego, Francisco J. "Comparing Bayesian and Frequentist Estimators under Asymmetric Loss." In Springer Series in Statistics. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5941-6_8.

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Kalina, Jan, and Barbora Peštová. "On the Bayesian Interpretation of Penalized Statistical Estimators." In Artificial Intelligence and Soft Computing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42508-0_31.

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Wickramasuriya, Dilranjan S., and Rose T. Faghih. "Additional Models and Derivations." In Bayesian Filter Design for Computational Medicine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-47104-9_9.

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AbstractMuch of what we have described in the preceding chapters provides the basic tools necessary to build physiological state-space estimators. In this chapter, we will briefly review some additional concepts in state-space estimation, a non-traditional method of estimation, and some supplementary models. These may help serve as pointers if extensions are to be built to the models already described.
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Samaniego, Francisco J. "Comparing Bayesian and Frequentist Estimators of a Scalar Parameter." In Springer Series in Statistics. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5941-6_5.

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Conference papers on the topic "Bayesian estimators"

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Barnes, Leighton P., Alex Dytso, Jingbo Liu, and H. Vincent Poor. "Multivariate Priors and the Linearity of Optimal Bayesian Estimators under Gaussian Noise." In 2024 IEEE International Symposium on Information Theory (ISIT). IEEE, 2024. http://dx.doi.org/10.1109/isit57864.2024.10619358.

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De S. Soares, Elton F., Emilio Vital Brazil, and Carlos Alberto V. Campos. "Leveraging Bayesian Optimization to Enhance Self-Supervised Federated Learning of Monocular Depth Estimators." In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024. https://doi.org/10.1109/itsc58415.2024.10920195.

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Garg, Tulika, Anupama Rajoriya, and Rohit Budhiraja. "Hardware Impairments Aware Bayesian Learning Channel Estimator for mmWave Wireless Systems." In 2025 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2025. https://doi.org/10.1109/wcnc61545.2025.10978184.

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Ribeiro, Rafael Oliveira, Alex Miyamoto Mussi, and Taufik Abrao. "Bayesian estimators by particle filtering." In 2011 SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC). IEEE, 2011. http://dx.doi.org/10.1109/imoc.2011.6169393.

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Luengo, David, Luca Martino, Victor Elvira, and Monica Bugallo. "Bias correction for distributed Bayesian estimators." In 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2015. http://dx.doi.org/10.1109/camsap.2015.7383784.

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Taylor, Clark N., and Shane Lubold. "Verifying the predicted uncertainty of Bayesian estimators." In Geospatial Informatics, and Motion Imagery Analytics VIII, edited by Kannappan Palaniappan, Gunasekaran Seetharaman, and Peter J. Doucette. SPIE, 2018. http://dx.doi.org/10.1117/12.2304954.

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Dyson, Matthew, and Kianoush Nazarpour. "Abstract Decoding using Bayesian Muscle Activation Estimators." In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018. http://dx.doi.org/10.1109/embc.2018.8512663.

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Fienup, J. R., and B. J. Thelen. "Bayesian estimation of the magnitude of an optical field from its intensity." In OSA Annual Meeting. Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.fnn8.

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For several problems in optics it is necessary to estimate the magnitude of an optical field from a photon-limited measurement of its intensity (squared magnitude). An example is wavefront sensing from one or more measured intensities using an iterative transform algorithm such as the Gerchberg–Saxton algorithm. The maximum-likelihood estimate of the intensity and the minimum mean-squared error estimate of the intensity is simply the measured photon-limited intensity. The square root of the measured intensity is the maximum likelihood estimate of the magnitude, but it has a negative bias in that the square root of the intensity systematically underestimates the magnitude. We show that there exists no unbiased estimator for the magnitude. Other estimators, such as the square root of (1 + intensity), where intensity is in terms of number of detected photons, perform better than the square root estimator, as do some Bayesian estimators that use an assumed prior probability distribution for the intensity. The Cramer–Rao lower bound on the mean-squared error in estimating the magnitude is 1/4, and all of the above estimators have a mean-squared error close to 1/4 when the intensity is large but significantly differ from 1/4 when the intensity is small. The analysis is also given for intensity measurements with a background bias.
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Mossel, Elchanan, Noah Olsman, and Omer Tamuz. "Efficient Bayesian Learning in Social Networks with Gaussian Estimators." In 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2016. http://dx.doi.org/10.1109/allerton.2016.7852262.

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Wu, Chi-hsin, and Peter C. Doerschuk. "Markov random fields as a priori information for image restoration." In Signal Recovery and Synthesis. Optica Publishing Group, 1995. http://dx.doi.org/10.1364/srs.1995.rwc2.

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Markov random fields (MRFs) [1, 2, 3, 4] provide attractive statistical models for multidimensional signals. However, unfortunately, optimal Bayesian estimators tend to require large amounts of computation. We present an approximation to a particular Bayesian estimator which requires much reduced computation and an example illustrating low-light unknown-blur imaging. See [7] for an alternative approximation based on approximating the MRF lattice by a system of trees and for an alternative cost function.
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Reports on the topic "Bayesian estimators"

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Baltagi, Badi H., Georges Bresson, Anoop Chaturvedi та Guy Lacroix. Robust dynamic space-time panel data models using ε-contamination: An application to crop yields and climate change. CIRANO, 2023. http://dx.doi.org/10.54932/ufyn4045.

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This paper extends the Baltagi et al. (2018, 2021) static and dynamic ε-contamination papers to dynamic space-time models. We investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, we consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner (1986)’s g-priors for the variance-covariance matrices. We propose a general “toolbox” for a wide range of specifications which includes the dynamic space-time panel model with random effects, with cross-correlated effects `a la Chamberlain, for the Hausman-Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using an extensive Monte Carlo simulation study, we compare the finite sample properties of our proposed estimator to those of standard classical estimators. We illustrate our robust Bayesian estimator using the same data as in Keane and Neal (2020). We obtain short run as well as long run effects of climate change on corn producers in the United States.
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Cheng, Benny N., and Lap S. Tam. Bayesian Missile System Reliability from Point Estimates. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada611099.

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Tamburini, Andrea, Arkadiusz Wiśniowski, and Dilek Yildiz. BAYESIAN MULTI-DIMENSIONAL MORTALITY RECONSTRUCTION. Verlag der Österreichischen Akademie der Wissenschaften, 2024. http://dx.doi.org/10.1553/0x003eb05e.

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Even though mortality differentials by socio-economic status and educational attainment level have been widely examined, this research is often limited to developed countries and recent years. This is primarily due to the absence of consistently good-quality inherent data. Systematic studies with a broad geographical and temporal spectrum that engage with the link between educational attainment and mortality are lacking. In this paper, we propose a mortality rates reconstruction model based on multiple patchy data sources, and provide mortality rates by level of education. The proposed model is a hierarchical Bayesian model that combines the strengths of multiple sources in order to disaggregate mortality rates by time periods, age groups, sex and educational attainment. We apply the model in a case study that includes 13 countries across South-East Europe, Western Asia and North Africa, and calculate education-specific mortality rates for five-year age groups starting at age 15 for the 1980-2015 time period. Furthermore, we evaluate the model’s performance relying on standard convergence indicators and trace plots, and validate our estimates via posterior predictive checks. This study contributes to the literature by proposing a novel methodology to enhance the research on the relationship between education and adult mortality. It addresses the lack of educationspecific mortality differentials by providing a flexible method for their estimation.
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Baluga, Anthony, and Masato Nakane. Maldives Macroeconomic Forecasting:. Asian Development Bank, 2020. http://dx.doi.org/10.22617/wps200431-2.

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This study aims to build an efficient small-scale macroeconomic forecasting tool for Maldives. Due to significant limitations in data availability, empirical economic modeling for the country can be problematic. To address data constraints and circumvent the “curse of dimensionality,” Bayesian vector autoregression estimations are utilized comprising of component-disaggregated domestic sectoral production, price, and tourism variables. Results demonstrate how this methodology is appropriate for economic modeling in Maldives. With the appropriate level of shrinkage, Bayesian vector autoregressions can exploit the information content of the macroeconomic and tourism variables. Augmenting for qualitative assessments, the directional inclination of the forecasts is improved.
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Calafat, Francisco Mir, Thomas Frederikse, and Kevin Horsburgh. Mediterranean trend and acceleration sea-level estimates. EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d5.2_v2.

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Sea-level change is geographically non-uniform, with regional departures that can reach several times the global average rate of change. Characterizing this spatial variability and understanding its causes is crucial to the design of adaptation strategies for sea-level rise. This, as it turns out, is no easy feat, primarily due to the sparseness of the observational sea-level record in time and space. Long tide gauge records are restricted to a few locations along the coast. Satellite altimetry offers a better spatial coverage but only since 1992. In the Mediterranean Sea, the tide gauge network is heavily biased towards the European shorelines, with only one record with at least 35 years of data on the African coasts. Past studies have attempted to address the difficulties related to this data sparseness in the Mediterranean Sea by combining the available tide gauge records with satellite altimetry observations. The vast majority of such studies represent sea level through a combination of altimetry-derived empirical orthogonal functions whose temporal amplitudes are then inferred from the tide gauge data. Such methods, however, have tremendous difficulty in separating trends and variability, make no distinction between relative and geocentric sea level, and tell us nothing about the causes of sea level changes. Here, we combine observational data from tide gauges and altimetry with sea-level fingerprints of land-mass changes using a Bayesian hierarchical model (BHM) to quantify the sources of sea-level changes since 1960 in the Mediterranean Sea. The Bayesian estimates are provided on 1/4o x 1/4o regular grid. We find that Mediterranean Sea level rose at a relatively low rate from 1960 to 1990, at which point it started rising significantly faster with comparable contributions from sterodynamic sea level (ocean dynamics and thermal expansion) and land-mass changes. (EuroSea Deliverable, D5.2_v2)
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Albis, Manuel Leonard, Mara Claire Tayag, and Jong Woo Kang. Estimating Regional Integration Using the Bayesian State-Space Approach. Asian Development Bank, 2024. http://dx.doi.org/10.22617/wps230622-2.

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Estimating regional integration faces challenges due to incomplete data. This paper addresses this through the dynamic factor model estimated using the Bayesian state-space approach. Bilateral economic integration (BEI) indexes are estimated across four dimensions: trade, foreign direct investments, finance, and migration. The regional integration index (RII) of Asia and the Pacific is calculated by applying network density to the BEI estimates. The RII declined slightly in recent years, with the network centering more around the People’s Republic of China.
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Calafat, Franciso Mir, Thomas Frederikse, Kevin Horsburgh, and Nadim Dayoub. Mediterranean sea-level reconstruction spanning 1960-2018. EuroSea, 2021. http://dx.doi.org/10.3289/eurosea_d5.2.

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We have used spatiotemporal Bayesian methods to produce statistically rigorous estimates of sea-level trends in the Mediterranean Sea since 1960 by combining tide gauge and satellite altimetry data. Furthermore, we have also quantified the contributions from sterodynamic sea-level change, land-mass changes and glacial isostatic adjustment to the trends.
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Doppelhofer, Gernot, Ronald Miller, and Xavier Sala-i-Martin. Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach. National Bureau of Economic Research, 2000. http://dx.doi.org/10.3386/w7750.

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Melo-Velandia, Luis Fernando, Rubén Albeiro Loaiza-Maya, and Mauricio Villamizar-Villegas. Bayesian combination for inflation forecasts : the effects of a prior based on central banks' estimates. Banco de la República, 2014. http://dx.doi.org/10.32468/be.853.

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Kurozumi, Takushi, Ryohei Oishi, and Willem Van Zandweghe. Sticky Information Versus Sticky Prices Revisited: A Bayesian VAR-GMM Approach. Federal Reserve Bank of Cleveland, 2022. http://dx.doi.org/10.26509/frbc-wp-202234.

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Several Phillips curves based on sticky information and sticky prices are estimated and compared using Bayesian VAR-GMM. This method derives expectations in each Phillips curve from a VAR and estimates the Phillips curve parameters and the VAR coefficients simultaneously. Quasi-marginal likelihood-based model comparison selects a dual stickiness Phillips curve in which, each period, some prices remain unchanged, consistent with micro evidence. Moreover, sticky information is a more plausible source of inflation inertia in the Phillips curve than other sources proposed in previous studies. Sticky information, sticky prices, and unchanged prices in each period are all needed to better describe inflation dynamics.
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