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Highly Cited

2008

Highly Cited

2008

The group lasso is an extension of the lasso to do variable selection on (predefined) groups of variables in linear regression… Expand

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Highly Cited

2007

Highly Cited

2007

Logistic regression analysis of high-dimensional data, such as natural language text, poses computational and statistical… Expand

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Highly Cited

2001

Highly Cited

2001

We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes… Expand

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Highly Cited

2001

Highly Cited

2001

We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a… Expand

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Highly Cited

2000

Highly Cited

2000

The Logic of Logistic Regression Interpreting Logistic Regression Coefficients Estimation and Model Fit Probit Analysis… Expand

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Highly Cited

1999

Highly Cited

1999

From the Publisher:
If you are a researcher or student with experience in multiple linear regression and want to learn about… Expand

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Highly Cited

1999

Highly Cited

1999

If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression… Expand

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Highly Cited

1989

Highly Cited

1989

Introduction to the Logistic Regression Model Multiple Logistic Regression Interpretation of the Fitted Logistic Regression Model… Expand

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Highly Cited

1988

Highly Cited

1988

Abstract We discuss the use of standard logistic regression techniques to estimate hazard rates and survival curves from censored… Expand

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Highly Cited

1978

Highly Cited

1978

Abstract Classifying an observation into one of several populations is discriminant analysis, or classification. Relating… Expand

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