The chapter highlights the Bayesian approach to statistical inference. It discusses asymptotic inference. The chapter also discusses nonparametric methods of statistical inference.ISHWAR V. BASAWAB.L.S. PRAKASA RAOStatistical Inference for Stochastic Processes...
Let’s look at a real flu vaccine study for an example of making a statistical inference. The scientists for this study want to evaluate whether a flu vaccine effectively reduces flu cases in the general population. However, the general population is much too large to include in their study,...
In this paper, we investigate the problem of statistical inference for model parameters based on gradient-free stochastic optimization methods that use only function values rather than gradients. We first present central limit theorem results... X Chen,Z Lai,H Li,... 被引量: 0发表: 2021年 A...
Statistical methods are mathematical formulas, models, and techniques that are used in statistical analysis of raw research data. The application of statistical methods extracts information from research data and provides different ways to assess the robustness of research outputs. ...
“analysed in depth … some attractive properties” of the likelihood concept, I must point out that I am not now among the “modern exponents” of the likelihood concept. Further, after suggesting that the notion of prior likelihood was plausible as an extension or analogue of the usual ...
受限统计推断方法(Constrained statistical inference-based methods):这类方法在统计分析中加入了一些先验知识或假设,以提高分析的准确性或效率。受限可以是指对参数的范围、分布形式或数据之间关系的假定等。这种方法尤其在数据量不足或有额外信息可用时...
Peter Bühlmannis Professor of Statistics at ETH Zürich. His main research areas are high-dimensional statistical inference, machine learning, graphical modeling, nonparametric methods, and statistical modeling in the life sciences. He is currently editor of the Annals of Statistics. He was awarded a...
Texts in Statistical Science(共72册),这套丛书还有 《Statistics in Research and Development》《Statistical Analysis of Financial Data》《Analysis of Categorical Data with R》《Linear Models with R》《Bayesian Data Analysis (3/e)》等。 喜欢读"Understanding Advanced Statistical Methods"的人也喜欢 ··...
…I believe this text would be an excellent choice for my Bayesian class since it seems to cover a good number of introductory topics and giv the student a good introduction to the modern computational tools for Bayesian inference with illustrations using R. (Journal of the American Statistical ...
频率论方法通过大量独立实验将概率解释为统计均值(大数定律);贝叶斯方法则将概率解释为信念度(degree of belief)(不需要大量的实验)。当考虑的试验次数非常少的时候,贝叶斯方法的解释非常有用。此外,贝叶斯理论将我们对于随机过程的先验知识纳入考虑,当我们获得的数据越来越多的时候,这个先验的概率分布就会被更新到后验...