Modeling and inference regression model future alsfrs

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: 38 37 36 0 0.5 1 1.5 2 Months 2.5 3 3.5 Featurizing Time Series Data ALSFRS Score 40 slope 0 slope  ­1 39 38 slope  ­2 37 36 0 0.5 1 1.5 Months 2 2.5 3 3.5 Featurizing Time Series Data Deriva;ve ;me series ALSFRS ALSFRS Score 40 Slope 0 slope 0 slope  ­1 39  ­0.5  ­1 38  ­1.5 slope  ­2 37  ­2 36  ­2.5 0 0.5 1 1.5 2 Months 2.5 3 3.5 Featurizing Time Series Data Deriva;ve ;me series ALSFRS ALSFRS Score 40 Slope 0 slope 0 slope  ­1 39  ­0.5  ­1 38  ­1.5 slope  ­2 37  ­2 36  ­2.5 0 0.5 1 1.5 2 Months 2.5 3 3.5 Featurizing Time Series Data ALSFRS Score 40 Deriva;ve ;me series ALSFRS Slope 0 Features  ­0.5 extracted Mean =  ­1  ­1 SD = 1 Max = 0 Min =  ­2  ­1.5 Last =  ­2 Slope =  ­0.5  ­2 etc. 39 38 37 36  ­2.5 0 0.5 1 1.5 2 Months 2.5 3 3.5 Featurizing Time Series Data   435 temporal features extracted   Problem: Missing data •  Average pa-ent missing 10% of features Room for •  One pa-ent missing 55% of features! improvement •  Missing values imputed using median heuris-c   Problem: Outliers •  Nonsense values: Number of liters recorded as MDMD •  Units incorrectly recorded ⇒ Wrong conversions •  Extreme values   Treated as missing if > 4 standard devia-ons from mean Open Ques;on: Regression robust to (sparse) covariate outliers? Modeling and...
View Full Document

This note was uploaded on 02/03/2014 for the course STATS 202 taught by Professor Taylor during the Fall '09 term at Stanford.

Ask a homework question - tutors are online