Mining Imperfect Data: With Examples in R and Python
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Published By Society For Industrial And Applied Mathematics

9781611976267, 9781611976274

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Chapter 5: Dealing with Missing Data

Mining Imperfect Data: With Examples in R and Python ◽

10.1137/1.9781611976274.ch5 ◽

2020 ◽

pp. 233-274

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Chapter 6: Dealing with Other Data Anomalies

Mining Imperfect Data: With Examples in R and Python ◽

10.1137/1.9781611976274.ch6 ◽

2020 ◽

pp. 275-314

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Chapter 4: Dealing with Time-Series Outliers

Mining Imperfect Data: With Examples in R and Python ◽

10.1137/1.9781611976274.ch4 ◽

2020 ◽

pp. 177-231

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Chapter 1: What Is Imperfect Data?

Mining Imperfect Data: With Examples in R and Python ◽

10.1137/1.9781611976274.ch1 ◽

2020 ◽

pp. 1-69

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Back Matter

Mining Imperfect Data: With Examples in R and Python ◽

10.1137/1.9781611976274.bm ◽

2020 ◽

pp. 461-481

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Chapter 9: What Is a “Good” Data Characterization?

Mining Imperfect Data: With Examples in R and Python ◽

10.1137/1.9781611976274.ch9 ◽

2020 ◽

pp. 409-440

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Front Matter

Mining Imperfect Data: With Examples in R and Python ◽

10.1137/1.9781611976274.fm ◽

2020 ◽

pp. i-ix

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Chapter 2: Dealing with Univariate Outliers

Mining Imperfect Data: With Examples in R and Python ◽

10.1137/1.9781611976274.ch2 ◽

2020 ◽

pp. 71-121

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Chapter 7: Generalized Sensitivity Analysis

Mining Imperfect Data: With Examples in R and Python ◽

10.1137/1.9781611976274.ch7 ◽

2020 ◽

pp. 315-347

Keyword(s):

Sensitivity Analysis ◽

Generalized Sensitivity

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Chapter 3: Dealing with Multivariate Outliers

Mining Imperfect Data: With Examples in R and Python ◽

10.1137/1.9781611976274.ch3 ◽

2020 ◽

pp. 123-176

Keyword(s):

Multivariate Outliers

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