Assessing and Improving Prediction and Classification: Theory and Algorithms in C++

© Timothy Masters 2018

Timothy MastersAssessing and Improving Prediction and Classificationhttps://doi.org/10.1007/978-1-4842-3336-8_3

3. Resampling for Assessing Parameter Estimates

Timothy Masters¹ 

(1)

Ithaca, New York, USA

 

• Parameter Estimates

• Bias and Variance of Estimators

• Bootstrap Estimation of Bias and Variance

• Confidence Intervals

• Hypothesis Tests for Parameter Values

• Jackknife Estimates of Bias and Variance

• Bootstrapping Dependent Data

We often collect a random sample of cases from a population, let this sample interact with a model in some way, and then examine with interest a number (or several numbers) that result from the interaction. For example, we may use the random sample to train a model and then examine one or more of the model’s learned ...