© 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 
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 ...

Get Assessing and Improving Prediction and Classification: Theory and Algorithms in C++ now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.