2046 search results for "regression"

machine learning [book review, part 2]

October 21, 2013
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machine learning [book review, part 2]

The chapter (Chap. 3) on Bayesian updating or learning (a most appropriate term) for discrete data is well-done in Machine Learning, a probabilistic perspective if a bit stretched (which is easy with 1000 pages left!). I like the remark (Section 3.5.3) about the log-sum-exp trick. While lengthy, the chapter (Chap. 4) on Gaussian models has

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How Do You Write Your Model Definitions?

October 20, 2013
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How Do You Write Your Model Definitions?

I’m often irritated by that when a statistical method is explained, such as linear regression, it is often characterized by how it can be calculated rather than by what model is assumed and fitted. A typical example of this is that linear regression is often described as a method that uses ordinary least squares to calculate the best...

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Optimizing a multivariable function parameters using a random method, genetic algorithm and simulated annealing in R

October 19, 2013
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Optimizing a multivariable function parameters using a random method, genetic algorithm and simulated annealing in R

Say that you are implementing a non-linear regression analysis, which is shortly described by wikipedia as:"In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear ...

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Introduction to Feature selection for bioinformaticians using R, correlation matrix filters, PCA & backward selection

October 17, 2013
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Introduction to Feature selection for bioinformaticians using R, correlation matrix filters, PCA & backward selection

Bioinformatics is becoming more and more a Data Mining field. Every passing day, Genomics and Proteomics yield bucketloads of multivariate data (genes, proteins, DNA, identified peptides, structures), and every one of these biological data units are described by a number of features: length, physicochemical properties, scores, etc. Careful consideration of which features to select when trying...

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A Bayesian Model for a Function Increasing by Chi-Squared Jumps (in Stan)

October 16, 2013
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A Bayesian Model for a Function Increasing by Chi-Squared Jumps (in Stan)

(This article was first published on David Chudzicki's Blog, and kindly contributed to R-bloggers) This post will describe a way I came up with of fitting a function that’s constrained to be increasing, using Stan. If you want practical help, standard statistical approaches, or expert research, this isn’t the place for you (look up “isotonic regression” or “Bayesian isotonic...

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That’s Smooth

October 10, 2013
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That’s Smooth

I had someone ask me the other day how to take a scatterplot and draw something other than a straight line through the graph using Excel.  Yes, it can be done in Excel and it’s really quite simple, but there are some limitations when using the stock Excel dialog screens. So it is probably in

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Please, never use my codes without checking twice (at least)!

October 9, 2013
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Please, never use my codes without checking twice (at least)!

I wanted to get back on some interesting experience, following a discussion I had with Carlos after my class, this morning. Let me simplify the problem, and change also the dataset. Consider the following dataset > db = read.table("http://freakonometrics.free.fr/db2.txt",header=TRUE,sep=";") Let me change also one little thing (in the course, we use the age of people as explanatory variables, so...

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Fast Bayesian Inference with INLA

October 9, 2013
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Fast Bayesian Inference with INLA

I am currently a research fellow and 4th year PhD candidate within the INLA group.  If you deal with Bayesian models and have never heard about INLA, I sincerely think you should spend a small portion of your time to at least know what it is. If you have heard about it before, you know how nice

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Some heuristics about spline smoothing

October 8, 2013
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Some heuristics about spline smoothing

Let us continue our discussion on smoothing techniques in regression. Assume that . where is some unkown function, but assumed to be sufficently smooth. For instance, assume that  is continuous, that exists, and is continuous, that  exists and is also continuous, etc. If  is smooth enough, Taylor’s expansion can be used. Hence, for which can also be writen as for...

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Too crude to be true?

October 8, 2013
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Too crude to be true?

The key to programming is being lazy; it has actually been called a virtue by some. When I discovered the update() function it blew me away. Within short I had created a monster based upon this tiny function, allowing quick and easy output of regression tables that contain crude and adjusted estimates. In this post I’ll show...

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