Statistical inference for unknown parameters of stochastic SIS epidemics on complete graphs

In this paper, statistical inference and prediction issue of left truncated and right censored dependent competing risk data are studied. When the latent lifetime is distributed by Marshall–Olkin bivariate Rayleigh distribution, the maximum likelihood estimates of unknown parameters are established, and corresponding approximate confidence intervals are also constructed by using a Fisher information matrix and asymptotic approximate theory. Furthermore, Bayesian estimates and associated high posterior density credible intervals of unknown parameters are provided based on general flexible priors. In addition, when there is an order restriction between unknown parameters, the point and interval estimates based on classical and Bayesian frameworks are discussed too. Besides, the prediction issue of a censored sample is addressed based on both likelihood and Bayesian methods. Finally, extensive simulation studies are conducted to investigate the performance of the proposed methods, and two real-life examples are presented for illustration purposes.

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Diffusion in Social Networks as SIS Epidemics: Beyond Full Mixing and Complete Graphs

IEEE Journal of Selected Topics in Signal Processing ◽

10.1109/jstsp.2014.2314858 ◽

2014 ◽

Vol 8 (4) ◽

pp. 537-551 ◽

Cited By ~ 15

Author(s):

June Zhang ◽

Jose M. F. Moura

Keyword(s):

Social Networks ◽

Complete Graphs ◽

Sis Epidemics

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Bias Adaptive Statistical Inference Learning Agents for Learning from Human Feedback

The International FLAIRS Conference Proceedings ◽

10.32473/flairs.v34i1.128471 ◽

2021 ◽

Vol 34 (1) ◽

Author(s):

Jonathan I Watson

Keyword(s):

Normal Distribution ◽

Statistical Inference ◽

Feedback Signal ◽

Unknown Parameters ◽

Learning Behaviors ◽

Learning Agents ◽

Novel Technique ◽

Inference Learning

We present a novel technique for learning behaviors from ahuman provided feedback signal that is distorted by system-atic bias. Our technique, which we refer to as BASIL, modelsthe feedback signal as being separable into a heuristic evalu-ation of the utility of an action and a bias value that is drawnfrom a parametric distribution probabilistically, where thedistribution is defined by unknown parameters. We presentthe general form of the technique as well as a specific algo-rithm for integrating the technique with the TAMER algo-rithm for bias values drawn from a normal distribution. Wetest our algorithm against standard TAMER in the domain ofTetris using a synthetic oracle that provides feedback undervarying levels of distortion. We find our algorithm can learnvery quickly under bias distortions that entirely stymie thelearning of classic TAMER.

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The Numerical and its Application in the Interval Estimates and Hypothesis Test

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.143-144.1391 ◽

2010 ◽

Vol 143-144 ◽

pp. 1391-1395

Author(s):

Xin Chun Wang ◽

Xing Hua Ma ◽

Bing Han

Keyword(s):

Confidence Interval ◽

Hypothesis Testing ◽

Statistical Inference ◽

Hypothesis Test ◽

Interval Estimation ◽

Parameters Estimation ◽

Unknown Parameters ◽

Significance Level ◽

The Relationship ◽

Statistical Work

The whole unknown parameters estimation and hypothesis testing is the most common and most commonly used statistical inference,so clarify the relationship between them is very important.Clearing both unity and not uniformity will help to amend the emergence of some specious argument of statistical work. In this paper, some differences were analyzed on interval estimation and hypothesis testing of statistical inference theory,the scope of their application of two methods was discussed, the dualityof Neyman —Pearson hypothesis testing and confidence interval was described Then provided the method of determining the unknown parameters’ confidence interval in hypothesis test.at the same time, it provided the ideas how to solve the problem of refusal field of hypothesis test througn confidence interval . As the confidence interval of the statistics varies with the selected significance level and sample size, it is heavily influenced by subjective factors.

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