Abhinandan Dalal, Diganta Mukherjee, Subhrajyoty Roy
arXiv 4 May 2020 · Statistics — Applications
arXiv:2005.02814 · PDF · DOI · OpenAlex · Extracted main text
Tea auctions across India occur as an ascending open auction, conducted online. Before the auction, a sample of the tea lot is sent to potential bidders and a group of tea tasters. The seller's reserve price is a confidential function of the tea taster's valuation, which also possibly acts as a signal to the bidders. In this paper, we work with the dataset from a single tea auction house, J Thomas, of tea dust category, on 49 weeks in the time span of 2018-2019, with the following objectives in mind: $\bullet$ Objective classification of the various categories of tea dust (25) into a more manageable, and robust classification of the tea dust, based on source and grades. $\bullet$ Predict which tea lots would be sold in the auction market, and a model for the final price conditioned on sale. $\bullet$ To study the distribution of price and ratio of the sold tea auction lots. $\bullet$ Make a detailed analysis of the information obtained from the tea taster's valuation and its impact on the final auction price. The model used has shown various promising results on cross-validation. The importance of valuation is firmly established through analysis of causal relationship between the valuation and the actual price. The authors hope that this study of the properties and the detailed analysis of the role played by the various factors, would be significant in the decision making process for the players of the auction game, pave the way to remove the manual interference in an attempt to automate the auction procedure, and improve tea quality in markets.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Wikipedia Tea leaf grading | 0.644 | 2 | 2 | 100% |
| 2 | Food and Agriculture Organization of the United Nations (2016) Report of the working group on climate change of the fao intergovernmental group on tea | 0.585 | 3 | 1 | 100% |
| 3 | Judea Pearl (2010) An introduction to causal inference | 0.405 | 1 | 1 | 100% |
| 4 | Food and Agriculture Organization of the United Nations (2015) World tea production and trade: Current and future development | 0.405 | 1 | 1 | 100% |
| 5 | http://www.fao.org/economic/est/est-commodities/tea/en/ | 0.405 | 1 | 1 | 100% |
| 6 | Food and Agriculture Organization of the United Nations (2018) Committee on commodity problems | 0.405 | 1 | 1 | 100% |
| 7 | Geographical map of assam https://assam.gov.in/ | 0.405 | 1 | 1 | 100% |
| 8 | V. Krishna (2009) Auction Theory | 0.405 | 1 | 1 | 100% |
| 9 | Dan Levin and James L. Smith (1996) Optimal reservation prices in auctions | 0.405 | 1 | 1 | 100% |
| 10 | Rudolph Emil Kalman (1960) A new approach to linear filtering and prediction problems | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 21 scored citations.