Publication:

Most Likely Transformations: The mlt Package

Date

Date

Date
2020
Journal Article
Published version

Citations

Citation copied

Hothorn, T. (2020). Most Likely Transformations: The mlt Package. Journal of Statistical Software, 92(1), v092.i01. https://doi.org/10.18637/jss.v092.i01

Abstract

Abstract

Abstract

The mlt package implements maximum likelihood estimation in the class of conditional transformation models. Based on a suitable explicit parameterization of the unconditional or conditional transformation function using infrastructure from package basefun, we show how one can define, estimate, and compare a cascade of increasingly complex transformation models in the maximum likelihood framework. Models for the unconditional or conditional distribution function of any univariate response variable are set-up and estimated in the same c

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63 since deposited on 2021-01-25
Acq. date: 2025-11-13

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94 since deposited on 2021-01-25
Acq. date: 2025-11-13

Additional indexing

Creators (Authors)

  • Hothorn, Torsten
    affiliation.icon.alt

Journal/Series Title

Journal/Series Title

Journal/Series Title

Volume

Volume

Volume
92

Number

Number

Number
1

Page range/Item number

Page range/Item number

Page range/Item number
v092.i01

Item Type

Item Type

Item Type
Journal Article

Dewey Decimal Classifikation

Dewey Decimal Classifikation

Dewey Decimal Classifikation

Language

Language

Language
English

Publication date

Publication date

Publication date
2020-01-01

Date available

Date available

Date available
2021-01-25

Publisher

Publisher

Publisher

ISSN or e-ISSN

ISSN or e-ISSN

ISSN or e-ISSN
1548-7660

OA Status

OA Status

OA Status
Gold

Free Access at

Free Access at

Free Access at
DOI

Metrics

Downloads

63 since deposited on 2021-01-25
Acq. date: 2025-11-13

Views

94 since deposited on 2021-01-25
Acq. date: 2025-11-13

Citations

Citation copied

Hothorn, T. (2020). Most Likely Transformations: The mlt Package. Journal of Statistical Software, 92(1), v092.i01. https://doi.org/10.18637/jss.v092.i01

Gold Open Access
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