1. Quantile regression is desired if conditional quantile functions are of interest.2. Quantile regression is desired if conditional quantile functions are of interest. 3. However, the main attraction of quantile regression goes beyond that. 4. It is the reciprocal of the pdf composed with the quantile function. 5. Statistical applications of quantile functions are discussed extensively by Gilchrist. 6. Thus the 90 % quantile is exactly 4 " 3 / 5. 7. Koenker is mostly known for his contributions to quantile regression. 8. Quantile normalization is frequently used in microarray data analysis.9. #Continuous probability distribution, with finite support, and closed-form quantile function 10. The cumulative distribution function is computed by numerically inverting the quantile function given above.
