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July 2008 Blog Posts (5)

Iterative Algorithm for Linear Regression

I am trying to solve the regression Y=AX where Y is the response, X the input, and A the regression coefficients. I came up with the following iterative algorithm:

A_k+1 = cYU + A_k (I-cXU),

where:

c is an arbitrary constant
U is an arbitrary matrix such that YU has same dimension as A. For instance U = transposed(X)…

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Added by Vincent Granville on July 30, 2008 at 6:00pm — No Comments

Statistical Software Survey, by the Institute for Operations Research and the Management Sciences

This survey of products is an update of the survey published in 2005. The biennial statistical software survey in this issue provides capsule information about 44 products selected from 31 vendors. The tools range from general tools that cover the standard techniques of inference and estimation as well as specialized activities such as nonlinear regression, forecasting and design of experiments. The product information contained in the survey was obtained from product vendors and is summarized… Continue

Added by Vincent Granville on July 26, 2008 at 12:00am — 2 Comments

SEO, AdWords and Google Analytics

See: My Analytics Blog

Added by Anthony Sharot on July 21, 2008 at 7:49pm — No Comments

Data Mining Combined With Predictive Modeling Equal 3D Data Visualization

The interaction and cooperation between computers and the human brain is at a crossroad. There are some who believe that decision support systems should be completely automated. There are others who believe that there are many areas of business, technology, and science that have not been discovered yet, and, hence, only part of a decision support system can be automated. I subscribe to the latter proposition.

Computer science is, at its core, an attempt to replicate the processing,… Continue

Added by Vincent Granville on July 10, 2008 at 11:30pm — No Comments

Scorecards: Logistic, Ridge and Logic Regression

In the context of credit scoring, one tries to develop a predictive model using a regression formula such as Y = Σ w_i R_i, where Y is the logarithm of odds ratio (fraud vs. non fraud). In a different but related framework, we are dealing with a logistic regression where Y is binary, e.g. Y = 1 means fraudulent transaction, Y = 0…

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Added by Vincent Granville on July 8, 2008 at 5:00pm — No Comments