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| Lasso-regression× | Panel Data Fixed Effects Model× | Poisson- og negativ binomialregression× | |
|---|---|---|---|
| Fagområde≠ | Maskinlæring | Økonometri | Økonometri |
| Familie≠ | Machine learning | Regression model | Regression model |
| Oprindelsesår≠ | 1996 | 2014 | 1998 |
| Ophavsperson≠ | Tibshirani, R. | Hsiao (textbook treatment); within transformation of panel data | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| Type≠ | Regularized linear regression (L1 penalty) | Panel data regression | Generalized linear model for count data |
| Oprindelig kilde≠ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| Aliasser | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| Relaterede≠ | 4 | 5 | 4 |
| Resumé≠ | Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter. | The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014). | Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred. |
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