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Relevant bibliographies by topics / Estimation theory. Bayesian statistical decision theory. Prediction theory / Journal articles

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Journal articles on the topic 'Estimation theory. Bayesian statistical decision theory. Prediction theory'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Domanov, Aleksey. "The Basics of Bayesian Approach to Quantitative Analysis (at the Example of Euroscepticism)." Political Science (RU), no. 1 (2021): 301–21. http://dx.doi.org/10.31249/poln/2021.01.13.

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This article attempts to identify the main assumptions, prerequisites and techniques of the methods developed by some modern statisticians on the basis of T. Bayes' theorem for the purposes of social variables interactions assessment. The author underlined several advantages of the given approach as compared to more traditional quantitative methods and highlighted key research areas subject to evaluation by Bayesian estimates. First of all, this approach is compatible with game and decision theory, event analysis, hidden Markov chains, prediction using neural networks and other predictive algo

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De Waal, D. J. "Summary on Bayes estimation and hypothesis testing." Suid-Afrikaanse Tydskrif vir Natuurwetenskap en Tegnologie 7, no. 1 (1988): 28–32. http://dx.doi.org/10.4102/satnt.v7i1.896.

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Although Bayes’ theorem was published in 1764, it is only recently that Bayesian procedures were used in practice in statistical analyses. Many developments have taken place and are still taking place in the areas of decision theory and group decision making. Two aspects, namely that of estimation and tests of hypotheses, will be looked into. This is the area of statistical inference mainly concerned with Mathematical Statistics.

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Wijayanti, Rina. "PENAKSIRAN PARAMETER ANALISIS REGRESI COX DAN ANALISIS SURVIVAL BAYESIAN." PRISMATIKA: Jurnal Pendidikan dan Riset Matematika 1, no. 2 (2019): 16–26. http://dx.doi.org/10.33503/prismatika.v1i2.427.

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In the theory of estimation, there are two approaches, namely the classical statistical approach and global statistical approach (Bayesian). Classical statistics are statistics in which the procedure is the decision based only on the data samples taken from the population. While Bayesian statistics in making decisions based on new information from the observed data (sample) and prior knowledge. At this writing Cox Regression Analysis will be taken as an example of parameter estimation by the classical statistical approach Survival Analysis and Bayesian statistical approach as an example of glo

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Bobrowski, Omer, Ron Meir, and Yonina C. Eldar. "Bayesian Filtering in Spiking Neural Networks: Noise, Adaptation, and Multisensory Integration." Neural Computation 21, no. 5 (2009): 1277–320. http://dx.doi.org/10.1162/neco.2008.01-08-692.

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A key requirement facing organisms acting in uncertain dynamic environments is the real-time estimation and prediction of environmental states, based on which effective actions can be selected. While it is becoming evident that organisms employ exact or approximate Bayesian statistical calculations for these purposes, it is far less clear how these putative computations are implemented by neural networks in a strictly dynamic setting. In this work, we make use of rigorous mathematical results from the theory of continuous time point process filtering and show how optimal real-time state estima

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Tseng, Shih-Hsien, and Tien Son Nguyen. "A Method for Visualizing Posterior Probit Model Uncertainty in the Early Prediction of Fraud for Sustainability Development." Axioms 10, no. 3 (2021): 178. http://dx.doi.org/10.3390/axioms10030178.

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Corporate fraud is not only curtailed investors’ rights and privileges but also disrupts the overall market economy. For this reason, the formulation of a model that could help detect any unusual market fluctuations would be essential for investors. Thus, we propose an early warning system for predicting fraud associated with financial statements based on the Bayesian probit model while examining historical data from 1999 to 2017 with 327 businesses in Taiwan to create a visual method to aid in decision making. In this study, we utilize a parametric estimation via the Markov Chain Monte Carlo

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Manea, Daniela-Ioana, Emilia Țiţan, Radu R. Șerban, and Mihaela Mihai. "Statistical applications of optimization methods and mathematical programming." Proceedings of the International Conference on Applied Statistics 1, no. 1 (2019): 312–28. http://dx.doi.org/10.2478/icas-2019-0028.

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Abstract Optimization techniques perform an important role in different domains of statistic. Examples of parameter estimation of different distributions, correlation analysis (parametric and nonparametric), regression analysis, optimal allocation of resources in partial research, exploration of response surfaces, design of experiments, efficiency tests, reliability theory, survival analysis are the most known methods of statistical analysis in which we find optimization techniques. The paper contains a synthetic presentation of the main statistical methods using classical optimization techniq

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Chen, Ruijing. "Simulation Modeling and Application of Travel Mode Choice Based on Bayesian Network." Open Mechanical Engineering Journal 8, no. 1 (2014): 19–25. http://dx.doi.org/10.2174/1874155x01408010019.

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In this paper, we study the travel mode choice of residents to determine the set of factors which can influence travel mode choice of residents and analyze the influence factor characteristics. Using Bayesian theory, we analyze the travel decision-making data of the residents, discrete them, and use them in Bayesian network structure learning and parameter estimation by K2 algorithm. We establish a Bayesian network simulation model to analyze the dependence probability relationship between the parent nodes and child nodes. Validation test was carried out for the building simulation model of Ba

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FELGAER, PABLO, PAOLA BRITOS, and RAMÓN GARCÍA-MARTÍNEZ. "PREDICTION IN HEALTH DOMAIN USING BAYESIAN NETWORKS OPTIMIZATION BASED ON INDUCTION LEARNING TECHNIQUES." International Journal of Modern Physics C 17, no. 03 (2006): 447–55. http://dx.doi.org/10.1142/s0129183106008558.

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A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and flexible methods of reasoning. Obtaining it from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper we define an automatic learning method that optimizes the Bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees (TDIDT-C4.5) with

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Liu, Xingye, Jingye Li, Xiaohong Chen, Lin Zhou, and Kangkang Guo. "Bayesian discriminant analysis of lithofacies integrate the Fisher transformation and the kernel function estimation." Interpretation 5, no. 2 (2017): SE1—SE10. http://dx.doi.org/10.1190/int-2016-0025.1.

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The accurate identification of lithofacies is indispensable for reservoir parameter prediction. In recent years, the application of multivariate statistical methods has gained more and more attention in petroleum geology. In terms of the identification for lithofacies, the commonly used multivariate statistical methods include discriminant analysis and cluster analysis. Fisher and Bayesian discriminant analyses are two different discriminant analysis methods, which include intrinsic advantages and disadvantages. Given the discriminant efficiency of different methods, calculation cost, difficul

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Daniel, Lucky O., Caston Sigauke, Colin Chibaya, and Rendani Mbuvha. "Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods." Algorithms 13, no. 6 (2020): 132. http://dx.doi.org/10.3390/a13060132.

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Wind offers an environmentally sustainable energy resource that has seen increasing global adoption in recent years. However, its intermittent, unstable and stochastic nature hampers its representation among other renewable energy sources. This work addresses the forecasting of wind speed, a primary input needed for wind energy generation, using data obtained from the South African Wind Atlas Project. Forecasting is carried out on a two days ahead time horizon. We investigate the predictive performance of artificial neural networks (ANN) trained with Bayesian regularisation, decision trees bas

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Zhang, Xin, Shuaiwen Tang, Taoyuan Liu, and Bangcheng Zhang. "A New Residual Life Prediction Method for Complex Systems Based on Wiener Process and Evidential Reasoning." Journal of Control Science and Engineering 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/9473526.

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A new residual life prediction method for complex systems based on Wiener process and evidential reasoning is proposed to predict the residual life of complex systems effectively. Moreover, the better maintenance strategies and decision supports are provided. For the residual life prediction of complex systems, the maximum likelihood method is adopted to estimate the drift coefficient, and the Bayesian method is adopted to update the parameters of Wiener process. The process of parameters estimation and the probability density function (PDF) of the residual life are deduced. To improve the acc

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Woolliams, J. A., and T. H. E. Meuwissen. "Decision rules and variance of response in breeding schemes." Animal Science 56, no. 2 (1993): 179–86. http://dx.doi.org/10.1017/s0003356100021231.

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AbstractSelection decisions in breeding schemes can involve choices between candidates evaluated to different accuracies. A Bayesian framework is put forward for choosing among candidates, and it is shown that attaching loss functions for estimation errors makes this process different from selecting upon best linear unbiased predictions alone. Examples are given using both linear and quadratic loss to show that when estimation errors are penalized, the selection process tends to select more unrelated and more accurately evaluated individuals. In a dairy cattle breeding scheme response was only

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Zhang, Zhihao, Saksham Chandra, Andrew Kayser, Ming Hsu, and Joshua L. Warren. "A Hierarchical Bayesian Implementation of the Experience-Weighted Attraction Model." Computational Psychiatry 4 (August 2020): 40–60. http://dx.doi.org/10.1162/cpsy_a_00028.

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Social and decision-making deficits are often the first symptoms of neuropsychiatric disorders. In recent years, economic games, together with computational models of strategic learning, have been increasingly applied to the characterization of individual differences in social behavior, as well as their changes across time due to disease progression, treatment, or other factors. At the same time, the high dimensionality of these data poses an important challenge to statistical estimation of these models, potentially limiting the adoption of such approaches in patients and special populations.

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Zhu, Shuang, Xiangang Luo, Zhanya Xu, and Lei Ye. "Seasonal streamflow forecasts using mixture-kernel GPR and advanced methods of input variable selection." Hydrology Research 50, no. 1 (2018): 200–214. http://dx.doi.org/10.2166/nh.2018.023.

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Abstract Gaussian Process Regression (GPR) is a new machine-learning method based on Bayesian theory and statistical learning theory. It provides a flexible framework for probabilistic regression and uncertainty estimation. The main effort in GPR modelling is determining the structure of the kernel function. As streamflow is composed of trend, period and random components. In this study, we constructed a mixture-kernel composed of squared exponential kernel, periodic kernel and a rational quadratic term to reflect different properties of streamflow time series to make streamflow forecasts. A r

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Prateepasen, Asa, Pakorn Kaewtrakulpong, and Chalermkiat Jirarungsatean. "Semi-Parametric Learning for Classification of Pitting Corrosion Detected by Acoustic Emission." Key Engineering Materials 321-323 (October 2006): 549–52. http://dx.doi.org/10.4028/www.scientific.net/kem.321-323.549.

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This paper presents a Non-Destructive Testing (NDT) technique, Acoustic Emission (AE) to classify pitting corrosion severity in austenitic stainless steel 304 (SS304). The corrosion severity is graded roughly into five levels based on the depth of corrosion. A number of timedomain AE parameters were extracted and used as features in our classification methods. In this work, we present practical classification techniques based on Bayesian Statistical Decision Theory, namely Maximum A Posteriori (MAP) and Maximum Likelihood (ML) classifiers. Mixture of Gaussian distributions is used as the class

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Solodov, A. A. "Mathematical Formalization and Algorithmization of the Main Modules of Organizational and Technical Systems." Statistics and Economics 17, no. 4 (2020): 96–104. http://dx.doi.org/10.21686/2500-3925-2020-4-96-104.

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The purpose of the research is to develop a generalized structural scheme of organizational and technical systems based on the general theory of management, which contains the necessary and sufficient number of modules and formalize on this basis the main management tasks that act as goals of the behavior of the management object. The main modules that directly implement the management process are the status assessment module of organizational and technical systems and the management module. It is shown that in traditional organizational and technical systems, including the decision-maker, the

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17

Wang, Fangru, and Catherine L. Ross. "Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 47 (2018): 35–45. http://dx.doi.org/10.1177/0361198118773556.

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The multinomial logit (MNL) model and its variations have been dominating the travel mode choice modeling field for decades. Advantages of the MNL model include its elegant closed-form mathematical structure and its interpretable model estimation results based on random utility theory, while its main limitation is the strict statistical assumptions. Recent computational advancement has allowed easier application of machine learning models to travel behavior analysis, though research in this field is not thorough or conclusive. In this paper, we explore the application of the extreme gradient b

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18

"A Novel Intelligent Technique of Invariant Statistical Embedding and Averaging via Pivotal Quantities for Optimization or Improvement of Statistical Decision Rules under Parametric Uncertainty." WSEAS TRANSACTIONS ON MATHEMATICS 19 (March 3, 2020). http://dx.doi.org/10.37394/23206.2020.19.3.

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In the present paper, for intelligent constructing efficient (optimal, uniformly non-dominated, unbiased, improved) statistical decisions under parametric uncertainty, a new technique of invariant embedding of sample statistics in a decision criterion and averaging this criterion over pivots’ probability distributions is proposed. This technique represents a simple and computationally attractive statistical method based on the constructive use of the invariance principle in mathematical statistics. Unlike the Bayesian approach, the technique of invariant statistical embedding and averaging via

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19

Liu, Shengjie, Jun Gao, Yuling Zheng, Lei Huang, and Fangrong Yan. "Bayesian Two-Stage Adaptive Design in Bioequivalence." International Journal of Biostatistics 16, no. 1 (2019). http://dx.doi.org/10.1515/ijb-2018-0105.

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AbstractBioequivalence (BE) studies are an integral component of new drug development process, and play an important role in approval and marketing of generic drug products. However, existing design and evaluation methods are basically under the framework of frequentist theory, while few implements Bayesian ideas. Based on the bioequivalence predictive probability model and sample re-estimation strategy, we propose a new Bayesian two-stage adaptive design and explore its application in bioequivalence testing. The new design differs from existing two-stage design (such as Potvin’s method B, C)

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20

Majoros, William H., Young-Sook Kim, Alejandro Barrera, et al. "Bayesian estimation of genetic regulatory effects in high-throughput reporter assays." Bioinformatics, August 1, 2019. http://dx.doi.org/10.1093/bioinformatics/btz545.

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Abstract Motivation High-throughput reporter assays dramatically improve our ability to assign function to noncoding genetic variants, by measuring allelic effects on gene expression in the controlled setting of a reporter gene. Unlike genetic association tests, such assays are not confounded by linkage disequilibrium when loci are independently assayed. These methods can thus improve the identification of causal disease mutations. While work continues on improving experimental aspects of these assays, less effort has gone into developing methods for assessing the statistical significance of a

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Rappl, Anja, Andreas Mayr, and Elisabeth Waldmann. "More than one way: exploring the capabilities of different estimation approaches to joint models for longitudinal and time-to-event outcomes." International Journal of Biostatistics, April 5, 2021. http://dx.doi.org/10.1515/ijb-2020-0067.

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Abstract The development of physical functioning after a caesura in an aged population is still widely unexplored. Analysis of this topic would need to model the longitudinal trajectories of physical functioning and simultaneously take terminal events (deaths) into account. Separate analysis of both results in biased estimates, since it neglects the inherent connection between the two outcomes. Thus, this type of data generating process is best modelled jointly. To facilitate this several software applications were made available. They differ in model formulation, estimation technique (likelih

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Eiset, Andreas Halgreen, and Morten Frydenberg. "1215Considerations for using multiple imputation in propensity score-weighted analysis." International Journal of Epidemiology 50, Supplement_1 (2021). http://dx.doi.org/10.1093/ije/dyab168.186.

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Abstract Background Here we present the problems encountered when combining multiple imputation to handle missing data, propensity score-weighting to adjust for confounding and bootstrap to produce a percentile confidence interval. We apply our considerations to a research project estimating the association between long-distance migration and post-traumatic stress disorder with data from a sample of Syrian asylum seekers in Denmark and a sample of Syrian refugees in Lebanon. The exposure was “long-distance migration” defined as having migrated to Denmark instead of Lebanon and the outcome, PTS

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