2047 search results for "regression"

A question of model uncertainty

A question of model uncertainty

It has been several months since my last post on classification tree models, because two things have been consuming all of my spare time.  The first is that I taught a night class for the University of Connecticut’s Graduate School of Business, introducing R to students with little or no prior exposure to either R or programming.  My hope...

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Near-zero variance predictors. Should we remove them?

March 6, 2014
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Near-zero variance predictors. Should we remove them?

Datasets come sometimes with predictors that take an unique value across samples. Such uninformative predictor is more common than you might think. This kind of predictor is not only non-informative, it can break some models you may want to fit to your data (see example below). Even more common is the presence of predictors that

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Advances in scalable Bayesian computation [day #2]

March 5, 2014
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Advances in scalable Bayesian computation [day #2]

And here is the second day of our workshop Advances in Scalable Bayesian Computation gone! This time, it sounded like the “main” theme was about brains… In fact, Simon Barthelmé‘s research originated from neurosciences, while Dawn Woodard dissected a brain (via MRI) during her talk! (Note that the BIRS website currently posts Simon’s video as

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Forecasting weekly data

March 4, 2014
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Forecasting weekly data

This is another situation where Fourier terms are useful for handling the seasonality. Not only is the seasonal period rather long, it is non-integer (averaging 365.25/7 = 52.18). So ARIMA and ETS models do not tend to give good results, even with a period of 52 as an approximation. Regression with ARIMA errors The simplest approach is a regression...

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Guidebook for growth curve analysis

March 3, 2014
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Guidebook for growth curve analysis

I don't usually like to use complex statistical methods, but every once in a while I encounter a method that is so useful that I can't avoid using it. Around the time I started doing eye-tracking research (as a post-doc with Jim Magnuson), people were ...

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Exploratory data analysis techniques

March 3, 2014
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Exploratory data analysis techniques

In my previous blog post I have explained the steps needed to solve a data analysis problem. Going further, I will be discussing in-detail each and every step of Data Analysis. In this post, we shall discuss about exploratory Analysis.What is Exploratory Analysis?“Understanding data visually”Exploratory Analysis means analyzing the datasets to summarize their main characteristics,...

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Predicting movie ratings with IMDb data and R

March 2, 2014
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Predicting movie ratings with IMDb data and R

It’s Oscars season again so why not explore how predictable (my) movie tastes are. This has literally been a million dollar problem and obviously I am not gonna solve it here, but it’s fun and slightly educational to do some number … Continue reading →

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Simple Pharmacokinetics with Jags

March 2, 2014
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Simple Pharmacokinetics with Jags

In this post I want to analyze a first order pharmocokinetcs problem: the data of study problem 9, chapter 3 of Rowland and Tozer (Clinical pharmacokinetics and pharmacodynamics, 4th edition) with Jags. It is a surprising simple set of data, but still there is enough to play around with.Data, model and analysisThe data is simple enough. One subject's concentration...

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Oldies but Goldies: Statistical Graphics Books

March 1, 2014
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Oldies but Goldies: Statistical Graphics Books

I just wanted to plug for three classical books on statistical graphics that I really enjoyed reading. The books are old (that is, older than me) but still relevant and together they give a sense of the development of exploratory graphics in general and the graphics system in R specifically as all three books were written at Bell Labs...

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Using CART for Stock Market Forecasting

February 28, 2014
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Using CART for Stock Market Forecasting

There is an enormous body of literature both academic and empirical about market forecasting. Most of the time it mixes two market features: Magnitude and Direction. In this article I want to focus on identifying the market direction only. The goal I set myself, is to identify market conditions when the odds are significantly biased

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