arbiter
JournalsPricingAccount

Linear regression with randomly double-truncated data

Gordon Frank & Achim Dörre

South African Statistical Journal2022https://doi.org/10.37920/sasj.2017.51.1.1article
ABDC B
Weight
0.26

What the paper says

Non-parametric estimation for a linear regression model under random double-truncation is investigated, i.e. the variables are observed if and only if the dependent variable lies in a random interval. The method requires only weak distribution assumptions to ensure identifiability, but does not require any specific distribution family for any variable, neither for the truncation variables nor for the error term. By using non-parametric estimators of several distribution functions, consistent and asymptotically normal estimators are established. A simulation study shows the tendency that the lower the probability of observation, the higher the mean squared error of the estimators, even for the same number of observations. Finally, the method is applied to a doubly truncated data set of German companies, where the age-at-insolvency is of interest. Keywords: Insolvency risk, Linear regression, Non-parametric, Random double-truncation

4 citations

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.37920/sasj.2017.51.1.1

Or copy a formatted citation

@article{gordon2022,
  title        = {{Linear regression with randomly double-truncated data}},
  author       = {Gordon Frank & Achim Dörre},
  journal      = {South African Statistical Journal},
  year         = {2022},
  doi          = {https://doi.org/https://doi.org/10.37920/sasj.2017.51.1.1},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Linear regression with randomly double-truncated data

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.26

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.20 × 0.15 = 0.03
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.