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generalized linear models [PDF]
Predictive Analytics, 2001 Generalized linear models provide a general framework for handling regression modeling for normal and non-normal data, including multiple linear regression, ANOVA, logistic regression, Poisson regression and log-linear models for contingency tables. All the major statistical packages include facilities for fitting generalized linear models.
A. I. Khuri
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Trained Transformers Learn Linear Models In-Context [PDF]
Journal of machine learning research, 2023 Attention-based neural networks such as transformers have demonstrated a remarkable ability to exhibit in-context learning (ICL): Given a short prompt sequence of tokens from an unseen task, they can formulate relevant per-token and next-token ...
Ruiqi Zhang, Spencer Frei, P. Bartlett
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What learning algorithm is in-context learning? Investigations with linear models [PDF]
International Conference on Learning Representations, 2022 Neural sequence models, especially transformers, exhibit a remarkable capacity for in-context learning. They can construct new predictors from sequences of labeled examples $(x, f(x))$ presented in the input without further parameter updates.
Ekin Akyürek +4 more
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Probability and Statistical Inference, Third Edition, 2021 This research aims to identify claims modeling in the compulsory car insurance branch in Egyptian insurance companies by using the data of Misr Property Insurance Company and Suez-Canal Insurance Company, using generalized linear models, and applying the
Jonathan May
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Set Identified Linear Models [PDF]
Econometrica, 2011 We analyze the identification and estimation of parameters β satisfying the incomplete linear moment restrictions E(z⊤(xβ−y)) = E(z⊤u(z)), where z is a set of instruments and u(z) an unknown bounded scalar function. We first provide empirically relevant examples of such a setup.
Bontemps, Christian +2 more
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Machine Learning Applied to the Search for Nonlinear Features in Breeding Populations
Frontiers in Artificial Intelligence, 2022 Large plant breeding populations are traditionally a source of novel allelic diversity and are at the core of selection efforts for elite material. Finding rare diversity requires a deep understanding of biological interactions between the genetic makeup
Iulian Gabur +4 more
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Comparison of explanatory power of linear and nonlinear models predicts expected stock returns [PDF]
فصلنامه بورس اوراق بهادار, 2022 One of the most challenging topics in finance and accounting is balancing returns and risk. If so, it is important for the market to identify trends in stock returns to predict the future.
Abbas Adham +2 more
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Notulae Scientia Biologicae, 2023 It is noteworthy that in the last decade, there has been an increase in the number of studies predicting normal diameter, total height, and stem volume based on stump dimensions.
Karen A. VARGAS-FLORES +3 more
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Remote Sensing, 2021 Remote sensing is one of the modern methods that have significantly developed over the last two decades and, nowadays, it provides a new means for forest monitoring.
Veronika Kopačková-Strnadová +4 more
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Regularization Paths for Generalized Linear Models via Coordinate Descent.
Journal of Statistical Software, 2010 We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multinomial regression problems while the penalties include ℓ(1) (the lasso), ℓ(2 ...
J. Friedman, T. Hastie, R. Tibshirani
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