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Community Bot
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"Bootstrap validation"/"resampling cross-validation" is new to me, but was discussed by the answer to this questionthis question. I gather it involves 2 types of data: the real data and simulated data, where a given set of simulated data is generated from the real data by resampling-with-replacement until the simulated data has the same size as the real data. I can think of two approaches to using such data types: (1) fit the model once, evaluate it many times on many simulated data sets; (2) fit the model many times using each of many simulated data sets, each time evaluate it against the real data. Which (if either) is best?

"Bootstrap validation"/"resampling cross-validation" is new to me, but was discussed by the answer to this question. I gather it involves 2 types of data: the real data and simulated data, where a given set of simulated data is generated from the real data by resampling-with-replacement until the simulated data has the same size as the real data. I can think of two approaches to using such data types: (1) fit the model once, evaluate it many times on many simulated data sets; (2) fit the model many times using each of many simulated data sets, each time evaluate it against the real data. Which (if either) is best?

"Bootstrap validation"/"resampling cross-validation" is new to me, but was discussed by the answer to this question. I gather it involves 2 types of data: the real data and simulated data, where a given set of simulated data is generated from the real data by resampling-with-replacement until the simulated data has the same size as the real data. I can think of two approaches to using such data types: (1) fit the model once, evaluate it many times on many simulated data sets; (2) fit the model many times using each of many simulated data sets, each time evaluate it against the real data. Which (if either) is best?

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Mike Lawrence
Mike Lawrence
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What is the procedure for "bootstrap validation" (a.k.a. "resampling cross-validation")?

"Bootstrap validation"/"resampling cross-validation" is new to me, but was discussed by the answer to this question. I gather it involves 2 types of data: the real data and simulated data, where a given set of simulated data is generated from the real data by resampling-with-replacement until the simulated data has the same size as the real data. I can think of two approaches to using such data types: (1) fit the model once, evaluate it many times on many simulated data sets; (2) fit the model many times using each of many simulated data sets, each time evaluate it against the real data. Which (if either) is best?