query: Why do we resample in bootstrap estimation?
positive_passages.docid: 7187
positive_passages.text: Explaining to laypeople why bootstrapping works
negative_passages.docid: 38201
negative_passages.text: How does a uniform prior lead to the same estimates from maximum likelihood and mode of posterior?
negative_passages.docid: 6158
negative_passages.text: How to resample in R without repeating permutations?
negative_passages.docid: 98154
negative_passages.text: Why use group lasso instead of lasso?
negative_passages.docid: 80644
negative_passages.text: Finding the unbiased variance estimator in high dimensional spaces
negative_passages.docid: 68257
negative_passages.text: Computing p-value using bootstrap with R
negative_passages.docid: 150891
negative_passages.text: What are examples where a "naive bootstrap" fails?
negative_passages.docid: 74904
negative_passages.text: Random initialization/order in neural network — bias or variance?
negative_passages.docid: 66805
negative_passages.text: Confusion about Maximum likelihood estimation
negative_passages.docid: 111931
negative_passages.text: Sampling distribution of regression coefficients
negative_passages.docid: 16157
negative_passages.text: Questions on parametric and non-parametric bootstrap
negative_passages.docid: 114934
negative_passages.text: Bootstrap Paradox in Interstellar
negative_passages.docid: 107720
negative_passages.text: Resampling / simulation methods: monte carlo, bootstrapping, jackknifing, cross-validation, randomization tests, and permutation tests
negative_passages.docid: 109534
negative_passages.text: Are there problems with arbitrary application of bootstrap?
negative_passages.docid: 129280
negative_passages.text: Neural networks: why do we randomize the training set?
negative_passages.docid: 19055
negative_passages.text: Bias of maximum likelihood estimators for logistic regression
negative_passages.docid: 26779
negative_passages.text: Rule of thumb for number of bootstrap samples