### Resumé

Originalsprog | Engelsk |
---|---|

Tidsskrift | Computational Statistics & Data Analysis |

Vol/bind | 110 |

Sider (fra-til) | 145-159 |

ISSN | 0167-9473 |

DOI | |

Status | Udgivet - jun. 2017 |

### Emneord

- Semiparametric regression
- Measurement error
- Side information

### Citer dette

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*Computational Statistics & Data Analysis*, bind 110, s. 145-159. https://doi.org/10.1016/j.csda.2017.01.003

**Generalized Partially Linear Regression with Misclassified Data and an Application to Labour Market Transitions.** / Dlugosz, Stephan; Mammen, Enno; Wilke, Ralf.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

TY - JOUR

T1 - Generalized Partially Linear Regression with Misclassified Data and an Application to Labour Market Transitions

AU - Dlugosz, Stephan

AU - Mammen, Enno

AU - Wilke, Ralf

PY - 2017/6

Y1 - 2017/6

N2 - Large data sets that originate from administrative or operational activity are increasingly used for statistical analysis as they often contain very precise information and a large number of observations. But there is evidence that some variables can be subject to severe misclassification or contain missing values. Given the size of the data, a flexible semiparametric misclassification model would be good choice but their use in practise is scarce. To close this gap a semiparametric model for the probability of observing labour market transitions is estimated using a sample of 20 m observations from Germany. It is shown that estimated marginal effects of a number of covariates are sizeably affected by misclassification and missing values in the analysis data. The proposed generalized partially linear regression extends existing models by allowing a misclassified discrete covariate to be interacted with a nonparametric function of a continuous covariate.

AB - Large data sets that originate from administrative or operational activity are increasingly used for statistical analysis as they often contain very precise information and a large number of observations. But there is evidence that some variables can be subject to severe misclassification or contain missing values. Given the size of the data, a flexible semiparametric misclassification model would be good choice but their use in practise is scarce. To close this gap a semiparametric model for the probability of observing labour market transitions is estimated using a sample of 20 m observations from Germany. It is shown that estimated marginal effects of a number of covariates are sizeably affected by misclassification and missing values in the analysis data. The proposed generalized partially linear regression extends existing models by allowing a misclassified discrete covariate to be interacted with a nonparametric function of a continuous covariate.

KW - Semiparametric regression

KW - Measurement error

KW - Side information

KW - Semiparametric regression

KW - Measurement error

KW - Side information

UR - https://sfx-45cbs.hosted.exlibrisgroup.com/45cbs?url_ver=Z39.88-2004&url_ctx_fmt=info:ofi/fmt:kev:mtx:ctx&ctx_enc=info:ofi/enc:UTF-8&ctx_ver=Z39.88-2004&rfr_id=info:sid/sfxit.com:azlist&sfx.ignore_date_threshold=1&rft.object_id=954926232411

U2 - 10.1016/j.csda.2017.01.003

DO - 10.1016/j.csda.2017.01.003

M3 - Journal article

VL - 110

SP - 145

EP - 159

JO - Computational Statistics & Data Analysis

JF - Computational Statistics & Data Analysis

SN - 0167-9473

ER -