 # Statistical Decision Theory Abraham Wald (1902 - 1950) Wald’s test Rigorous proof of the consistency of MLE “Note on the consistency of the maximum likelihood.

## Presentation on theme: "Statistical Decision Theory Abraham Wald (1902 - 1950) Wald’s test Rigorous proof of the consistency of MLE “Note on the consistency of the maximum likelihood."— Presentation transcript:

Statistical Decision Theory Abraham Wald (1902 - 1950) Wald’s test Rigorous proof of the consistency of MLE “Note on the consistency of the maximum likelihood estimate”, Ann. Math. Statist., 20, 595-601.

Statistical Decision Theory and Hypothesis Testing. A major use of statistical inference is its application to decision making under uncertainty, say Parameter estimation Unlike classical statistics which is only directed towards the use of sampling information in making inferences about unknown numerical quantities, an attempt in decision theory is made to combine the sampling information with a knowledge of the consequences of our decisions.

Three elements in SDT State of Nature: θ, some unknown quantities, say parameters. Decision Space D : A space of all possible values of decisions/actions/rules/estimators Loss function L( θ, d(X) ): –a non-negative function on Θ x D. –a measure of how much we lose by choosing action d when θ is used. –In estimation, a measure of the accuracy of estimators d of θ.

For example, θ = 0 means “nuclear warhead is NOT headed to UBC” θ = 1 means “nuclear warhead is headed to UBC” L(0,0) = 0 L(0,1) = cost of moving L(θ, d): L(1,1) = cost of moving + cost of belongings we cannot move L(1,0) = loss of belongings + D ={0,1}={ Stay in Vancouver, Leave }

Common loss functions Univariate –L1 = | θ – d(x)| (absolute error loss) –L2 = ( θ – d(x)) 2 (squared error loss) Multivariate –(Generalized) Euclidean norm: [ θ – d(x)] T Q [ θ – d(x)], where Q is positive definite More generally, –Non-decreasing functions of L1 or Euclidean norm

Loss function, L (d(X), θ), is random Frequentist Bayesian LE (in Frequentist) (in Bayesian) Risk or Posterior risk

Estimator Comparison The Risk principle: the estimator d1(X) is better than another estimator d2(X) in the sense of risk if R(θ,d1) ≤ R(θ,d2) for all θ, with strict inequality for some θ. However, in general, it does not exist Best estimator (uniformly minimum risk estimator) d*(X) = arg min R(θ, d(X)) for all θ d Class of all estimators

For instance, only consider mean-unbiased estimators. In particular, UMVUE is the best unbiased estimator when L2 is used. Shrink the class of estimators. Then find the best estimator in this class. Class of estimators Smaller class of estimators I am the best!! Too Large

Weaken the optimality criterion by considering the maximum value of the risk over all θ. Then choose the estimator with the smallest maximum risk. The best estimator according to this minimax principle is called minimax estimator. Notice that the risk depends on θ. So, the risks of two estimators often cross each other. This is another possibility that the best estimator does not exist. d1d2 θ R(θ,d) WinnerLoser

Alternatively, we can find the best estimator by minimizing the average risk with respect to a prior π of θ in the Bayesian framework. The estimator having the smallest Bayes risk with a specific prior π is called the Bayes estimator (with π). Given a prior π of θ, the average risk of the estimator d(X) defined by is called Bayes risk, r π (d)

Under Bayes risk principle R(θ, d1) θ π(θ) π(θ) R(θ, d2) d1d2 WinnerLoser WinnerLoser

In general, it is not easy to find the Bayes estimator by minimizing the Bayes risk. However, if the Bayes risk of the Bayes estimator is finite, then the estimator minimizing the posterior risk and the Bayes estimator are the same.

Some examples for finding the Bayes estimator (1) Squared error loss: E[(Θ-d) 2 |x] = E[Θ 2 |x] -2d E[Θ|x] + d 2 min E[(Θ-d) 2 |x] d Minimize the posterior risk The minimizer of f(d) is E[Θ|x], i.e. the posterior mean.  f(d)

Some examples for finding the Bayes estimator (2) Absolute error loss: min E[|Θ-d| |x] d The minimizer is med[Θ|x], i.e. the posterior median. (3) Linear error loss: L( θ,d ) = K 0 (θ-d) if θ-d>=0 and = K 1 (d-θ) if θ-d<0 The K 0 /(K 0 +K 1 ) th quantile of the posterior is the Bayes estimator of θ.

Relationship between minimax and Bayes estimators In particular, if the Bayes estimator has a constant risk, then it is minimax. Denote by d π the Bayes estimator with respect to π. If the Bayes risk of d π is equal to the maximum risk of d π, i.e. Then the Bayes estimator d π is minimax.

Problems for the risk measure: The risk measure is too sensitive to the choice of loss function. All estimators are assumed to have finite risks. So, in general, the risk measure fails to use in problems with heavy tails or outliers.

Other measures: (1) Pitman measure of closeness, PMC: θ d1 d2 P θ ( )≥1/2 d1 is Pitman-closer to θ than d2 if the above condition holds for all θ.

Other measures: (2) Universal domination, u.d.: d1(X) is said to universally dominate d2(X) if, for all nondecreasing functions h and all θ, E θ [h(|| d1(X) - θ || Q )] ≤ E θ [h(|| d2(X) - θ || Q )]. (3) Stochastic domination, s.d.: d1(X) is said to stochastically dominate d2(X) if, for every c > 0 and all θ, P θ [|| d1(X) - θ || Q ≤ c ] ≥ P θ [|| d2(X) - θ || Q ≤ c ].

Problems: (1) Pitman measure of closeness, PMC: θ d1 d2 d3

Problems: (2) Universal domination, u.d.: d1(X) is said to universally dominate d2(X) if, for all nondecreasing functions h and all θ, E θ [h(|| d1(X) - θ || Q )] ≤ E θ [h(|| d2(X) - θ || Q )]. Expectation is a linear operator h(t) = at + b, a>0

θ [h(|| d1(X) - θ || Q )] For all nondecreasing functions h, ET where T has a property that T[h(y)] =h[T(y)]

Thank You!!

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