Advanced Mathematical Statistics Notes for MS/MPhil
“Advanced Mathematical Statistics” typically refers to a higher-level study in statistical theory that delves into sophisticated methods and theoretical foundations of statistical inference.
Here is an explanation of the key components mentioned in your question:
- Criteria and Methods for Estimators: This involves understanding the properties and criteria for good estimators, such as unbiasedness, efficiency, and consistency.
- Classical and Newer Methods of Estimation: Exploring traditional estimation methods and newer techniques developed in recent statistical research.
- Deriving Estimators (Bayes Methods, MLE): The derivation of estimators using different approaches, including Bayesian methods and MLE.
- Cramer-Rao and its Extension:
- Cramer-Rao Bound: A theoretical limit on the variance of any unbiased estimator of a parameter.
- Extension of Cramer-Rao Bound: Modifications or extensions to the Cramer-Rao bound for specific scenarios.
- Bias Reduction by Jackknifing, Rao-Blackwellization, Basu’s Theorem:
- Jackknifing: A resampling technique used for bias reduction and variance estimation.
- Rao-Blackwellization: Improving estimators by taking conditional expectations.
- Basu’s Theorem: A result in mathematical statistics providing conditions under which conditional independence implies independence.
- Estimation in Parametric and Nonparametric Methods:
- Parametric Methods: Estimation techniques assuming a specific parametric model for the data.
- Nonparametric Methods: Estimation without assuming a specific parametric form for the underlying distribution.
- Testing Hypotheses:
- Parametric Methods: Hypothesis testing within the context of specific parametric models.
- Neyman-Pearson Lemma: A fundamental result in hypothesis testing that provides guidelines for constructing tests with optimal properties.
- Uniformly Most Powerful Tests: Tests that maximize power uniformly over a parameter space.
- Unbiased Tests: Hypothesis tests that maintain unbiasedness.
- Large Sample Theory, Asymptotically Best Procedures:
- Large Sample Theory: Theoretical study of statistical procedures as sample sizes become large.
- Asymptotically Best Procedures: Procedures that become optimal as sample sizes tend to infinity.
- Testing Under Nuisance Parameters, Review of Tests for Normal Distribution:
- Nuisance Parameters: Parameters that are not of primary interest but need consideration in testing.
- Review of Tests for Normal Distribution: Evaluating statistical tests specifically designed for normal distribution data.
“Advanced Mathematical Statistics” covers a wide range of sophisticated statistical concepts and methods, emphasizing theoretical foundations and advanced techniques used in statistical inference. The topics mentioned involve a deep understanding of estimation, hypothesis testing, large sample theory, and the application of these methods in various contexts, from parametric models to nonparametric approaches.
Keep visiting our website www.RanaMaths.com
Advanced Mathematical Statistics notes can also be downloaded from here.