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The book "Nonlinear Regression Analysis and Its Applications" (2007) by Bates & Watts springs to mind as an immediate suggestion. It is co-authored by one of the masters of regression algorithm design (D. Bates). Note that is is not exactly fresh; the edition I link is published on 2007 but most of the material is from the 1989 edition. That being said, ...

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Non-linear regression is a mature and broad topic, that's why I doubt that there are many recent review papers. The only papers that I can think of are: Motulsky HJ, Ransnas LA: "Fitting Curves to Data Using Nonlinear Regression: A Practical and Nonmathematical Review." The FASEB Journal, 1(5), 365-374 <- As the name says, a nonmathematical review so not ...

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Maybe this question is now answered by the fate of the site. The site does not exist, and its content have winded up at The Encyclopedia of Mathematics.

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Normal distribution, along with binomial distribution, are the two most popular examples of distributions used for introducing Bayesian statistics. You can found them described in any handbook on Bayesian statistics, for example the two references mentioned by Kevin Murphy in the refereed paper [Bis06] C. Bishop. Pattern recognition and machine ...

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To verify the solution suggested in the great answer by @user20160 I prepared a toy example that demonstrates it. As suggested by @user20160, I am posting the code as a supplement to the answer. For explanations of this approach, check the other answer. First, let's generate the independent variable and append the column of ones to it, to use matrix ...

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Here's an approach for solving this type of problem using latent variable models. It's not a specific model, but a general way to formulate a model by breaking the description of the system into two parts: the relationship between individual inputs and (unobserved) individual outputs, and the relationship between individual outputs and (observed) aggregate ...

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Different approaches could be appropriate depending on your goal. I'll describe one approach in case your goal is group-level prediction. You could use the individual-level features to build a bunch of aggregated features for each group (mean, std, median, max, min, ...). You now have richer features for each group which are likely to perform well on the ...

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I don't think either of these is exactly a substitute for Statistical Sleuth (which I also like) but it's a pair of books that will help a lot: Statistics by Freedman, Pisani and Purves. A really good text that will teach you a lot but won't overwhelm you with math. Statistics as Principled Argument by Robert Abelson. An excellent and very informal guide ...

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Not only a problem book, but a text organized around problems, with many problems: Lectures on Contemporary Probability by Gregory F. Lawler, Lester N. Coyle.

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Probability Through Problems by Marek Capinski, Tomasz Jerzy Zastawniak

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"Challenging Mathematical Problems With Elementary Solutions, Vol. 1: Combinatorial Analysis and Probability Theory" by A.M. and I.M. Yaglom

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