A STATISTICAL APPROACH TO EPISTEMIC DEMOCRACY

Episteme ◽  
2012 ◽  
Vol 9 (2) ◽  
pp. 115-137 ◽  
Author(s):  
Marcus Pivato
Keyword(s):  
Maximum Likelihood ◽  
Social Choice ◽  
Expected Utility ◽  
General Framework ◽  
Statistical Approach ◽  
Maximum Likelihood Estimators ◽  
Preference Aggregation ◽  
A Posteriori ◽  
Epistemic Democracy ◽  
Social Choice Rules

AbstractWe briefly review Condorcet's and Young's epistemic interpretations of preference aggregation rules as maximum likelihood estimators. We then develop a general framework for interpreting epistemic social choice rules as maximum likelihood estimators, maximum a posteriori estimators, or expected utility maximizers. We illustrate this framework with several examples. Finally, we critique this program.

scholarly journals Robust Significance Analysis of Microarrays by Minimum β-Divergence Method

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Md. Shahjaman ◽  
Nishith Kumar ◽  
Md. Manir Hossain Mollah ◽  
Md. Shakil Ahmed ◽  
Anjuman Ara Begum ◽  
...  
Keyword(s):  
Gene Expression ◽  
Maximum Likelihood ◽  
Statistical Approach ◽  
Maximum Likelihood Estimators ◽  
Small Sample ◽  
Gene Expressions ◽  
Large Sample ◽  
Significance Analysis ◽  
Real Gene ◽  
Better Than

Identification of differentially expressed (DE) genes with two or more conditions is an important task for discovery of few biomarker genes. Significance Analysis of Microarrays (SAM) is a popular statistical approach for identification of DE genes for both small- and large-sample cases. However, it is sensitive to outlying gene expressions and produces low power in presence of outliers. Therefore, in this paper, an attempt is made to robustify the SAM approach using the minimum β-divergence estimators instead of the maximum likelihood estimators of the parameters. We demonstrated the performance of the proposed method in a comparison of some other popular statistical methods such as ANOVA, SAM, LIMMA, KW, EBarrays, GaGa, and BRIDGE using both simulated and real gene expression datasets. We observe that all methods show good and almost equal performance in absence of outliers for the large-sample cases, while in the small-sample cases only three methods (SAM, LIMMA, and proposed) show almost equal and better performance than others with two or more conditions. However, in the presence of outliers, on an average, only the proposed method performs better than others for both small- and large-sample cases with each condition.

Using Approximation Non-Bayesian Computation with Fuzzy Data to Estimation Inverse Weibull Parameters and Reliability Function

2018 ◽  
pp. 397
Author(s):  
Nadia Hashim Al-Noor ◽  
Shurooq A.K. Al-Sultany
Keyword(s):  
Maximum Likelihood ◽  
Expectation Maximization ◽  
Mean Squared Error ◽  
Maximum Likelihood Estimators ◽  
Reliability Function ◽  
Fuzzy Data ◽  
Monte Carlo Simulation Study ◽  
Weibull Parameters ◽  
Squared Error ◽  
Newton Raphson

        In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson” and the “Expectation-Maximization” techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function in terms of their mean squared error values and integrated mean squared error values respectively.

Inference on P(Y

2020 ◽  
Vol 72 (2) ◽  
pp. 89-110
Author(s):  
Manoj Chacko ◽  
Shiny Mathew
Keyword(s):  
Maximum Likelihood ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Maximum Likelihood Estimators ◽  
Record Values ◽  
Asymptotic Distributions ◽  
Bayes Estimators ◽  
Generalized Pareto ◽  
Pareto Distributions ◽  
Generalized Pareto Distributions

In this article, the estimation of [Formula: see text] is considered when [Formula: see text] and [Formula: see text] are two independent generalized Pareto distributions. The maximum likelihood estimators and Bayes estimators of [Formula: see text] are obtained based on record values. The Asymptotic distributions are also obtained together with the corresponding confidence interval of [Formula: see text]. AMS 2000 subject classification: 90B25

scholarly journals Social choice rules driven by propositional logic

2013 ◽  
Vol 70 (3) ◽  
pp. 279-312
Author(s):  
Rosa Camps ◽  
Xavier Mora ◽  
Laia Saumell
Keyword(s):  
Social Choice ◽  
Propositional Logic ◽  
Social Choice Rules
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