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Best model selection through information criteria (AIC and log-likelihood).

Figshare2022-12-01 更新2026-04-28 收录

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https://figshare.com/articles/dataset/Best_model_selection_through_information_criteria_AIC_and_log-likelihood_/21659902 数据链接链接失效反馈

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Best model selection through information criteria (AIC and log-likelihood).

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2022-12-01

进化生物学

模型选择

The binary-state speciation and extinction (BiSSE) model has been used in many instances to identify state-dependent diversification and reconstruct ancestral states. However, recent studies have show

DataONE2016-10-26 更新100

Appendix C. Model selection details for models with percentage serotiny as the response.

模型选择

植物学

Model selection details for models with percentage serotiny as the response.

DataCite Commons2020-09-03 更新50

Model selection results on the response of genetic relatedness to all possible linear combinations of depth, space, Hotspots, and Hothours.

遗传学

模型选择

Only those models with ΔAIC = AICi—AICmin < 5 are shown. Number of parameters (K), log likelihood [log(L)], Akaike’s information criterion (AIC), and Akaike weights (w+) are reported for each model. M

NIAID Data Ecosystem40

l 1 -penalized Pairwise Difference Estimation for a High-dimensional Censored Regression Model

高维回归

模型选择

High-dimensional data are nowadays readily available and increasingly common in various fields of empirical economics. This paper considers estimation and model selection for a high-dimensional censor

DataCite Commons2022-01-24 更新90

Supplement 4. Complete AICc results for three experiments at Konza Prairie LTER site (Manhattan, KS, USA).

生态学

模型选择

File List Supplement4.csv (MD5: 9c2a02597ef066d593745194c0a92bb0) Description Model selection results from analysis of the relationship between a priori 1- and 2-variable weather models and total spec

DataCite Commons2020-09-03 更新40

Best model selection through information criteria (AIC and log-likelihood).

资源简介：

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