1488 search results for "regression"

Late-April flotsam

April 25, 2012
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Late-April flotsam

It has been month and a half since I compiled a list of statistical/programming internet flotsam and jetsam. Via Lambda The Ultimate: Evaluating the Design of the R Language: Objects and Functions For Data Analysis (PDF). A very detailed evaluation … Continue reading →

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Short versus long papers, in academic journals

April 24, 2012
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Short versus long papers, in academic journals

This Monday, during my talk on quantile regressions (at the Montreal R-meeting), we've seen how those nice graphs could be interpreted, with the evolution of the slope of the linear regression, as a function of the probability level. One illustrati...

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Varying Window Length for Linear Models on Stocks

April 24, 2012
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Varying Window Length for Linear Models on Stocks

In a previous post, we discussed ideas generated by a Timely Portfolio post about Linear Models on Stock. I wanted to see if there was a relationship between the window length of the running mean of the linear regression slope estimate and the running mean of the correlation between fitted and observed values. The parameters

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Talk on quantiles at the R Montreal group

April 23, 2012
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Talk on quantiles at the R Montreal group

This afternoon, I will be giving a two-hour talk at McGill on quantiles, quantile regressions, confidence regions, bagplots and outliers. Before defining (properly) quantile regressions, we will mention regression on (local) quantiles, as on the gr...

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Probit/Logit Marginal Effects in R

April 23, 2012
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Probit/Logit Marginal Effects in R

The common approach to estimating a binary dependent variable regression model is to use either the logit or probit model. Both are forms of generalized linear models (GLMs), which can be seen as modified linear regressions that allow the dependent variable to originate from non-normal distributions. The coefficients in a linear regression model are marginal

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R is not just for nerds….it has drop-down menus!

April 20, 2012
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R is not just for nerds….it has drop-down menus!

JGR LogoThis post introduces how to use the Java Gui for R (JGR, pronounced Jaguar) along with the Deducer package (manual here) to get a fairly full featured graphical user interface for R.InstallationNote: Be sure you are logged into an account with ...

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Generating all subsets of a set

April 20, 2012
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Generating all subsets of a set

Recently I have calculated Banzhaf power index. I required generation of all subsets of a given set. The code given there was a bit complex and I have decided to write a simple function calculating it. As an example of its application I reproduce Figur...

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Simple tools for building a recommendation engine

April 19, 2012
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By Joseph Rickert Revolution’s resident economist, Saar Golde, is very fond of saying that “90% of what you might from a recommendation engine can be achieved with simple techniques”. To illustrate this point (without doing a lot of work), we downloaded the million row movie dataset from www.grouplens.org with the idea of just taking the first obvious exploratory step:...

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R Quickie: Custom Panel Functions and Default Arguments

April 16, 2012
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Sometimes the basic functionality in lattice graphics isn't enough. Custom "panel functions" are one approach to fully customizing the lattice graphics system. Two examples are given below illustrating how to define an (inline) custom panel function fo...

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Instrumental Variables without Traditional Instruments

April 14, 2012
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Instrumental Variables without Traditional Instruments

Typically, regression models in empirical economic research suffer from at least one form of endogeneity bias. The classic example is economic returns to schooling, where researchers want to know how much increased levels of education affect income. Estimation using a simple linear model, regressing income on schooling, alongside a bunch of control variables, will typically

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