Talk:Quantile regression

From Wikipedia, the free encyclopedia
Jump to: navigation, search
WikiProject Statistics (Rated C-class, Mid-importance)
WikiProject icon

This article is within the scope of the WikiProject Statistics, a collaborative effort to improve the coverage of statistics on Wikipedia. If you would like to participate, please visit the project page or join the discussion.

C-Class article C  This article has been rated as C-Class on the quality scale.
 Mid  This article has been rated as Mid-importance on the importance scale.

Reorder point (inventory) as quantile estimation of the demand[edit]

The article does not discuss much about the practical application of quantiles. For retail and manufacturing, quantile regression is very handy for inventory management because it basically represents the reorder point in an inventory optimization context, see Disclaimer: I am a shareholder of Lokad, and might have a biased POV (so I prefer to let someone else deal with this part of the discussion on the primary article). --Joannes Vermorel (talk) 15:23, 23 February 2012 (UTC)

In the intuition section, I think the amount of change in the expected loss due to the shift of q by 1 is wrong. I get 0.5 times the integral of the density from negative infinity to q, minus 0.5 times the integral of the density from q to infinity, plus the integral of y-q-1 times the density from q to q+1. Even if what is written on the article now is correct, some explanation would be appreciated. Tet21tet (talk) 06:03, 27 May 2014 (UTC)

In the Quantile section, in the 4th line after "Define the loss function as...", the indicator function appears incorrect. Shouldn't it be I{y<u}? 2620:0:1000:167C:5891:BE8:3B61:E2B (talk) 16:07, 10 June 2015 (UTC)


Request: Define Equivariance in the Wikipedia article. — Preceding unsigned comment added by (talk) 19:43, 16 April 2012 (UTC)

Dr. Allen's comment on this article[edit]

Dr. Allen has reviewed this Wikipedia page, and provided us with the following comments to improve its quality:

I think the piece could emphasize that the method involves taking the absolute value of deviations around the median as opposed to squared deviations around the mean. This helps make it less sensitive to outliers. Some reference should be made to its applications in risk analysis, especially given the Basel Accords adoption of Value at Risk (VaR) which is based on breaches of a given quantile. Nobel laurate Robert Engle and Simone Manganelli have their CAViaR model (2004) "CAViaR: Conditional Autoregressive Value at

Risk by Regression Quantiles", Journal of Business and Economic Statistics, 22,4, pp: 367-381. Some reference could also be made to the use of quantile regression in fitting copulae. See for example: Bouyé, E., & Salmon, M. (2009). Dynamic copula quantile regressions and tail area dynamic dependence in Forex markets. The European Journal of Finance, 15 (7-8), 721-750.

We hope Wikipedians on this talk page can take advantage of these comments and improve the quality of the article accordingly.

Dr. Allen has published scholarly research which seems to be relevant to this Wikipedia article:

  • Reference : D.E. Allen & Abhay K Singh & R. Powell & Michael McAleer & James Taylor & Lyn Thomas, 2012. "The Volatility-Return Relationship: Insights from Linear and Non-Linear Quantile Regressions," Documentos de Trabajo del ICAE 2012-24, Universidad Complutense de Madrid, Facultad de Ciencias Economicas y Empresariales, Instituto Complutense de Analisis Economico.

ExpertIdeasBot (talk) 06:11, 9 July 2015 (UTC)