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Theory of Statistics Course Details - Statistical Inference Concepts, Exams, and Assignments

Master's program course covering statistical inference, hypothesis testing, Bayesian inference, and computer-intensive methods. Includes course contents, teaching methods, and prerequisite skills.

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Theory of Statistics Course Details - Statistical Inference Concepts, Exams, and Assignments

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  1. 732A36 Theory of Statistics Course within the Master’s program in Statistics and Data mining Fall semester 2011

  2. Course details • Course web: www.ida.liu.se/~732A36 • Course responsible, tutor and examiner: Anders Nordgaard • Course period: Nov 2011-Jan 2012 • Examination: Written exam in January 2012, Compulsory assignments • Course literature: “Garthwaite PH, Jolliffe IT and Jones B (2002). Statistical Inference. 2nd ed. Oxford University Press, Oxford. ISBN 0-19-857226-3” Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  3. Course contents • Statistical inference in general • Point estimation (unbiasedness, consistency, efficiency, sufficiency, completeness) • Information and likelihood concepts • Maximum-likelihood and Method-of-moment estimation • Classical hypothesis testing (Power functions, the Neyman-Pearson lemma , Maximum Likelihood Ratio Tests, Wald’s test) • Confidence intervals • … Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  4. Course contents, cont. • Statistical decision theory (Loss functions, Risk concepts, Prior distributions, Sequential tests) • Bayesian inference (Estimation, Hypothesis testing, Credible intervals, Predictive distributions) • Non-parametric inference • Computer intensive methods for estimation Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  5. Details about teaching and examination • Teaching is (as usual) sparse: A mixture between lectures and problem seminars • Lectures: Overview and some details of each chapter covered. No full-cover of the contents! • Problem seminars: Discussions about solutions to recommended exercises. Students should be prepared to provide solutions on the board! • Towards the end of the course a couple of larger compulsory assignments (that need solutions to be worked out with the help of a computer) will be distributed. • The course is finished by a written exam Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  6. Prerequisities • Good understanding of calculus an algebra • Good understanding of the concepts of expectations (including variance calculations) • Familiarity with families of probability distributions (Normal, Exponential, Binomial, Poisson, Gamma (Chi-square), Beta, …) • Skills in computer programming (e.g. with R , SAS, Matlab,) Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  7. Statistical inference in general Population Model Sample Conclusions about the population is drawn from the sample with assistance from a specified model Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  8. The two paradigms: Neyman-Pearson (frequentistic) and Bayesian Population Model • Neyman-Pearson: • Model specifies the probability distribution for data obtained in a sample including a number of unknown population parameters • Bayesian: • Model specifies the probability distribution for data obtained in a sample and a probability distribution (prior) for each of the unknown population parameters of that distribution Sample Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  9. How is inference made? • Point estimation: Find the “best” approximations of an unknown population parameter • Interval estimation: Find a range of values that with high certainty covers the unknown population parameter • Can be extended to regions if the parameter is multidimensional • Hypothesis testing: Give statements about the population (values of parameters, probability distributions, issues of independence,…) along with a quantitative measure of “certainty” Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  10. Tools for making inference • Criteria for a point estimate to be “good” • “Algorithmic” methods to find point estimates (Maximum Likelihood, Least Squares, Method-of-Moments) • Classical methods of constructing hypothesis test (Neyman-Pearson lemma, Maximum Likelihood Ratio Test,…) • Classical methods to construct confidence intervals (regions) • Decision theory (make use of loss and risk functions, utility and cost) to find point estimates and hypothesis tests • Using prior distributions to construct tests , credible intervals and predictive distributions (Bayesian inference) Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  11. Tools for making inference… • Using theory of randomization to form non-parametric tests (tests not depending on any probability distribution behind data) • Computer intensive methods (bootstrap and cross-validation techniques) • Advanced models from data that make use of auxiliary information (explanatory variables): Generalized linear models, Generalized additive models, Spatio-temporal models, … Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  12. The univariate population-sample model • The population to be investigated is such that the values that comes out in a sample x1, x2 , …are governed by a probability distribution • The probability distribution is represented by a probability density (or mass) function f(x ) • Alternatively, the sample values can be seen as the outcomes of independent random variables X1, X2, … all with probability density (or mass) function f(x ) Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  13. Point estimation (frequentistic paradigm) • We have a sample x = (x1 , … , xn ) from a population • The population contains an unknown parameter  • The functional forms of the distributional functions may be known or unknown, but they depend on the unknown  . • Denote generally by f(x ;  ) the probability density or mass function of the distribution • A point estimate of  is a function of the sample values such that its values should be close to the unknown . Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  14. “Standard” point estimates • The sample mean is a point estimate of the population mean  • The sample variance s2 is a point estimate of the population variance  2 • The sample proportion p of a specific event (a specific value or range of values) is a point estimate of the corresponding population proportion  Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  15. Assessing a point estimate • A point estimate has a sampling distribution • Replace the sample observations x1 , … , xn with their corresponding random variables X1 , … , Xn in the functional expression: •  The point estimate is a random variable that is observed in the sample (point estimator) • As a random variable the point estimator must have a probability distribution than can be deduced from f (x ;  ) • The point estimator /estimate is assessed by investigating the its sampling distribution, in particular the mean and the variance. Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  16. Unbiasedness • A point estimator is unbiased for  if the mean of its sampling distribution is equal to  • The bias of a point estimate for  is • Thus, a point estimate with bias = 0 is unbiased, otherwise it is biased Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  17. Examples (within the univariate population-sample model) • The sample mean is always unbiased for estimating the population mean • Is the sample mean an unbiased estimate of the population median? • Why do we divide by n–1 in the sample variance (and not by n )? Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  18. Consistency • A point estimator is (weakly) consistent if • Thus, the point estimator should converge in probability to  • Theorem: A point estimator is consistent if • Proof: Use Chebyshev’s inequality in terms of Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  19. Examples • The sample mean is a consistent estimator of the population mean. What probability law can be applied? • What do we require for the sample variance to be a consistent estimator of the population variance? Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  20. Efficiency • Assume we have two unbiased estimators of  , i.e. • The efficiency of an unbiased estimator is defined as Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  21. Example • Let Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  22. Likelihood function • For a sample x • the likelihood function for is defined as • the log-likelihood function is measure how likely (or expected) the sample is Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  23. Fisher information • The (Fisher) Information about  contained in a sample x is defined as • Theorem: Under some regularity conditions (interchangeability of integration and differentiation) In particular the range of X cannot depend on  (such as in a population where X  U(0, ) ) Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  24. Why is it measure of information for  Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  25. Example • X Exp( ) Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  26. Cramér-Rao inequality • Under the same regularity conditions as for the previous theorem the following holds for any unbiased estimator • The lower bound is attained if and only if Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  27. Proof: Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  28. Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  29. Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  30. Example • X Exp( ) Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  31. Sufficiency • A function T of the sample values of a sample x, i.e. T = T(x)=T(x1 , … , xn ) is a statistic that is sufficient for the parameter  if the conditional distribution of the sample random variables does not depend on , i.e. • What does it mean in practice? • If T is sufficient for  then no more information about  than what is contained in T can be obtained from the sample. • It is enough to work with T when deriving point estimates of  Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  32. Example Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  33. Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  34. The factorization theorem: T is sufficient for  if and only if the likelihood function can be written i.e. can be factorized using two non-negative functions such that the first depends on x only through the statistics T and also on  and the second does not depend on  Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  35. Example, cont • X Exp( ) Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

  36. Department of Computer and Information Science (IDA) Linköpings universitet, Sweden

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