The Big Data Newsvendor: Practical Insights from Machine Learning Analysis
A 2/6/2014 revision to this paper is available at http://hdl.handle.net/1721.1/85658.
| मुख्य लेखकों: | , |
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| स्वरूप: | Working Paper |
| भाषा: | en_US |
| प्रकाशित: |
2013
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| विषय: | |
| ऑनलाइन पहुंच: | http://hdl.handle.net/1721.1/81412 |
| _version_ | 1826203709707649024 |
|---|---|
| author | Rudin, Cynthia Vahn, Gah-Yi |
| author_facet | Rudin, Cynthia Vahn, Gah-Yi |
| author_sort | Rudin, Cynthia |
| collection | MIT |
| description | A 2/6/2014 revision to this paper is available at http://hdl.handle.net/1721.1/85658. |
| first_indexed | 2024-09-23T12:42:07Z |
| format | Working Paper |
| id | mit-1721.1/81412 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:42:07Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | mit-1721.1/814122019-04-11T06:21:41Z The Big Data Newsvendor: Practical Insights from Machine Learning Analysis Rudin, Cynthia Vahn, Gah-Yi big data, newsvendor, machine learning, Sample Average Approximation, statistical learning theory A 2/6/2014 revision to this paper is available at http://hdl.handle.net/1721.1/85658. We present a version of the newsvendor problem where one has n observations of p features as well as past demand. We consider both \big data" (p=n = O(1)) as well as small data (p=n = o(1)). For small data, we provide a linear programming machine learning algorithm that yields an asymptotically optimal order quantity. We also derive a generalization bound based on algorithmic stability, which is an upper bound on the expected out-of-sample cost. For big data, we propose a regularized version of the algorithm to address the curse of dimensionality. A generalization bound is derived for this case as well, bounding the out-of-sample cost with a quantity that depends on n and the amount of regularization. We apply the algorithm to analyze the newsvendor cost of nurse sta_ng using data from the emergency room of a large teaching hospital and show that (i) incorporating appropriate features can reduce the out-of-sample cost by up to 23% relative to the featureless Sample Average Approximation approach, and (ii) regularization can automate feature-selection while controlling the out-of-sample cost. By an appropriate choice of the newsvendor underage and overage costs, our results also apply to quantile regression. 2013-10-17T03:23:41Z 2013-10-17T03:23:41Z 2013-10-16 Working Paper http://hdl.handle.net/1721.1/81412 en_US MIT Sloan School of Management Working Paper;5036-13 http://hdl.handle.net/1721.1/85658 http://hdl.handle.net/1721.1/85658 Attribution-NonCommercial-NoDerivs 3.0 United States http://creativecommons.org/licenses/by-nc-nd/3.0/us/ application/pdf |
| spellingShingle | big data, newsvendor, machine learning, Sample Average Approximation, statistical learning theory Rudin, Cynthia Vahn, Gah-Yi The Big Data Newsvendor: Practical Insights from Machine Learning Analysis |
| title | The Big Data Newsvendor: Practical Insights from Machine Learning Analysis |
| title_full | The Big Data Newsvendor: Practical Insights from Machine Learning Analysis |
| title_fullStr | The Big Data Newsvendor: Practical Insights from Machine Learning Analysis |
| title_full_unstemmed | The Big Data Newsvendor: Practical Insights from Machine Learning Analysis |
| title_short | The Big Data Newsvendor: Practical Insights from Machine Learning Analysis |
| title_sort | big data newsvendor practical insights from machine learning analysis |
| topic | big data, newsvendor, machine learning, Sample Average Approximation, statistical learning theory |
| url | http://hdl.handle.net/1721.1/81412 |
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