Bayes estimator ≈ Bayes estimator
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Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data Open
The psychometric function describes how an experimental variable, such as stimulus strength, influences the behaviour of an observer. Estimation of psychometric functions from experimental data plays a central role in fields such as psycho…
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HMeta-d: hierarchical Bayesian estimation of metacognitive efficiency from confidence ratings Open
Metacognition refers to the ability to reflect on and monitor one's cognitive processes, such as perception, memory and decision-making. Metacognition is often assessed in the lab by whether an observer's confidence ratings are predictive …
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Bayesian Versus Frequentist Estimation for Structural Equation Models in Small Sample Contexts: A Systematic Review Open
In small sample contexts, Bayesian estimation is often suggested as a viable alternative to frequentist estimation, such as maximum likelihood estimation. Our systematic literature review is the first study aggregating information from num…
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Performance Bounds for Parameter Estimation under Misspecified Models: Fundamental Findings and Applications Open
Inferring information from a set of acquired data is the main objective of any signal processing (SP) method. The common problem of estimating the value of a vector of parameters from a set of noisy measurements is at the core of a plethor…
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Exploring the Bayesian parameter estimation of binary black holes with LISA Open
International audience
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A Comparison of ML, WLSMV, and Bayesian Methods for Multilevel Structural Equation Models in Small Samples: A Simulation Study Open
Multilevel structural equation models are increasingly applied in psychological research. With increasing model complexity, estimation becomes computationally demanding, and small sample sizes pose further challenges on estimation methods …
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Exponentiated Chen distribution: Properties and estimation Open
This article addresses various properties and estimation methods for the Exponentiated Chen distribution. Although, our main focus is on estimation from frequentist point of view, yet, some statistical and reliability characteristics for t…
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Bayesian Prior Choice in IRT Estimation Using MCMC and Variational Bayes Open
This study investigated the impact of three prior distributions: matched, standard vague, and hierarchical in Bayesian estimation parameter recovery in two and one parameter models. Two Bayesian estimation methods were utilized: Markov cha…
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Back to the Basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation Open
Estimating student proficiency is an important task for computer based learning systems. We compare a family of IRT-based proficiency estimation methods to Deep Knowledge Tracing (DKT), a recently proposed recurrent neural network model wi…
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Multilevel modeling of single-case data: A comparison of maximum likelihood and Bayesian estimation. Open
The focus of this article is to describe Bayesian estimation, including construction of prior distributions, and to compare parameter recovery under the Bayesian framework (using weakly informative priors) and the maximum likelihood (ML) f…
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Adaptive posterior contraction rates for the horseshoe Open
We investigate the frequentist properties of Bayesian procedures for estimation based on the horseshoe prior in the sparse multivariate normal means model. Previous theoretical results assumed that the sparsity level, that is, the number o…
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Statistical properties and different methods of estimation of Gompertz distribution with application Open
This article addresses the various properties and different methods of estimation of the unknown parameters of Gompertz distribution. Although, our main focus is on estimation from both frequentist and Bayesian point of view, yet, various …
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Bayesian estimation of direct and correlated responses to selection on linear or ratio expressions of feed efficiency in pigs Open
The Bayesian methodology developed here enables prediction of breeding values for FCR and RFI from a single multi-variate model. In addition, we derived posterior distributions of direct and correlated responses to selection. Genetic param…
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Bayesian Estimation of Species Divergence Times Using Correlated Quantitative Characters Open
Discrete morphological data have been widely used to study species evolution, but the use of quantitative (or continuous) morphological characters is less common. Here, we implement a Bayesian method to estimate species divergence times us…
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Bayesian Estimation of Gumbel Type-II Distribution under Type-II Censoring with Medical Applications Open
The time to event or survival time usually follows certain skewed probability distributions. These distributions encounter vital role using the Bayesian framework to analyze and project the maximum life expectancy in order to inform decisi…
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Experimental adaptive Bayesian estimation of multiple phases with limited data Open
Achieving ultimate bounds in estimation processes is the main objective of quantum metrology. In this context, several problems require measurement of multiple parameters by employing only a limited amount of resources. To this end, adapti…
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The Unit Teissier Distribution and Its Applications Open
A bounded form of the Teissier distribution, namely the unit Teissier distribution, is introduced. It is subjected to a thorough examination of its important properties, including shape analysis of the main functions, analytical expression…
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Marshall-Olkin Generalized Pareto Distribution: Bayesian and Non Bayesian Estimation Open
A new generalization of generalized Pareto Distribution is obtained using the generator Marshall-Olkin distribution (1997). The new distribution MOGP is more flexible and can be used to model non-monotonic failure rate functions. MOGP incl…
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Maximum Product Spacing and Bayesian Method for Parameter Estimation for Generalized Power Weibull Distribution Under Censoring Scheme Open
This article discusses the estimation of the Generalized Power Weibull parameters using the maximum product spacing (MPS) method, the maximum likelihood (ML) method and Bayesian estimation method under squares error for loss function. The …
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Kumaraswamy Inverted Topp–Leone Distribution with Applications to COVID-19 Data Open
In this paper, an attempt is made to discover the distribution of COVID-19 spread in different countries such as; Saudi Arabia, Italy, Argentina and Angola by specifying an optimal statistical distribution for analyzing the mortality rate …
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The Weibull Generalized Exponential Distribution with Censored Sample: Estimation and Application on Real Data Open
This paper is concerned with the estimation of the Weibull generalized exponential distribution (WGED) parameters based on the adaptive Type‐II progressive (ATIIP) censored sample. Maximum likelihood estimation (MLE), maximum product spaci…
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Bayesian and Maximum Likelihood Estimation for the Weibull Generalized Exponential Distribution Parameters Using Progressive Censoring Schemes Open
In this paper we consider the estimation of the Weibull Generalized Exponential Distribution (WGED) Parameters with Progressive Censoring Schemes. In order to obtain the optimal censoring scheme for WGED, more than one method of estimation…
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A Comparison of Small Area Estimation Methods for Poverty Mapping Open
We review main small area estimation methods for the estimation of general non-linear parameters focusing on FGT family of poverty indicators introduced by Foster, Greer and Thorbecke (1984). In particular, we consider direct estimation, t…
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Bayes, Oracle Bayes and Empirical Bayes Open
This article concerns the Bayes and frequentist aspects of empirical Bayes inference. Some of the ideas explored go back to Robbins in the 1950s, while others are current. Several examples are discussed, real and artificial, illustrating t…
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Likelihood-Free Parameter Estimation with Neural Bayes Estimators Open
Neural Bayes estimators are neural networks that approximate Bayes estimators. They are fast, likelihood-free, and amenable to rapid bootstrap-based uncertainty quantification. In this paper, we aim to increase the awareness of statisticia…
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Estimating Cognitive Diagnosis Models in Small Samples: Bayes Modal Estimation and Monotonic Constraints Open
Despite the increasing popularity, cognitive diagnosis models have been criticized for limited utility for small samples. In this study, the authors proposed to use Bayes modal (BM) estimation and monotonic constraints to stabilize item pa…
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Compressed Sensing with Basis Mismatch: Performance Bounds and Sparse-Based Estimator Open
International audience
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A flexible Bayesian framework for unbiased estimation of timescales Open
Timescales characterize the pace of change for many dynamic processes in nature. They are usually estimated by fitting the exponential decay of data autocorrelation in the time or frequency domain. Here we show that this standard procedure…
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The development of Bayesian integration in sensorimotor estimation Open
Examining development is important in addressing questions about whether Bayesian principles are hard coded in the brain. If the brain is inherently Bayesian, then behavior should show the signatures of Bayesian computation from an early s…
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Power-Modified Kies-Exponential Distribution: Properties, Classical and Bayesian Inference with an Application to Engineering Data Open
We introduce here a new distribution called the power-modified Kies-exponential (PMKE) distribution and derive some of its mathematical properties. Its hazard function can be bathtub-shaped, increasing, or decreasing. Its parameters are es…