10.5 Decision theory and loss functions

Decision theory blends probability and utility to make optimal choices under uncertainty. It's all about weighing the consequences of our actions when we don't have all the facts, using math to guide us through the fog of the unknown.

Loss functions are the beating heart of decision theory, putting a number on the pain of being wrong. They help us figure out the best moves in everything from investing to diagnosing diseases, balancing the risks of different types of mistakes.

Decision Theory Fundamentals

Framework and Components

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• Decision theory combines probability theory and utility theory to make optimal choices under uncertainty
• Statistical decision theory focuses on making inferences and decisions based on observed data and statistical models
• Key components include:
  • Decision space (set of possible actions)
  • Parameter space (set of possible true states of nature)
  • Sample space (set of possible observations)
  • Loss function (quantifies consequences of decisions)
• Bayesian decision theory incorporates prior beliefs about parameters into the decision-making process
• Provides formal approach to balancing trade-offs between different types of errors in statistical inference (Type I and Type II errors)

Risk and Applications

• Risk defined as expected loss guides selection of optimal decision rules
• Risk calculated by integrating loss function over probability distribution of unknown parameters
• Applications in various fields:
  • Economics (investment decisions)
  • Finance (portfolio optimization)
  • Medicine (treatment selection)
  • Machine learning (model selection and hyperparameter tuning)

Loss Functions for Decisions

Types and Properties

• Loss function quantifies cost or penalty associated with making a particular decision when true state of nature known
• Common types:
  • Squared error loss: $L(\theta, \hat{\theta}) = (\theta - \hat{\theta})^2$
  • Absolute error loss: $L(\theta, \hat{\theta}) = |\theta - \hat{\theta}|$
  • 0-1 loss for classification: $L(y, \hat{y}) = I(y \neq \hat{y})$
• Symmetric loss functions penalize overestimation and underestimation equally (squared error loss)
• Asymmetric loss functions assign different penalties to different types of errors (e.g., Linex loss)
• Proper loss functions ensure optimal decision corresponds to true underlying probability distribution (log loss for probability estimation)

Evaluation and Selection

• Used to evaluate performance of estimators, classifiers, and other statistical procedures
• Choice of loss function should reflect specific goals and constraints of decision-making problem
• Examples:
  • Financial forecasting: asymmetric loss function to penalize underestimation more heavily
  • Medical diagnosis: custom loss function balancing false positives and false negatives based on clinical impact

Optimal Decision Rules

Minimizing Expected Loss

• Optimal decision rule minimizes expected loss (risk) over all possible decisions and parameter values
• Bayes decision rule minimizes posterior expected loss, incorporating prior information about parameters
• Optimal point estimates for different loss functions:
  • Squared error loss: posterior mean (Bayesian) or minimum mean squared error estimator (frequentist)
  • Absolute error loss: posterior median (Bayesian) or minimum absolute error estimator (frequentist)
• In hypothesis testing, Neyman-Pearson lemma derives optimal decision rule maximizing power subject to Type I error rate constraint

Alternative Approaches

• Minimax decision rule minimizes maximum possible loss, providing conservative approach when prior information unavailable or unreliable
• Empirical risk minimization principle for deriving optimal decision rules in machine learning:
  • Expected loss approximated using observed data
  • Example: Support Vector Machines minimize hinge loss on training data

Sensitivity of Decision Rules

Analysis Techniques

• Sensitivity analysis examines how changes in loss function affect optimal decision rule and its performance
• Influence function quantifies effect of small perturbations in loss function on optimal decision
• Comparative analysis of decision rules under different loss functions identifies trade-offs between error types or costs
• Robustness of decision rule refers to ability to maintain good performance under different loss functions or model assumptions

Implications and Considerations

• Choice of loss function can significantly impact bias-variance trade-off in statistical estimation and prediction
• In some cases, decision rules relatively insensitive to small changes in loss function, exhibiting form of stability
• Understanding sensitivity crucial for assessing reliability and generalizability of statistical inferences and decisions
• Examples:
  • Regularization in machine learning (L1 vs L2 regularization) affects model sparsity and feature selection
  • Robust statistics uses loss functions less sensitive to outliers (Huber loss)