1491 search results for "regression"

Unit root versus breaking trend: Perron’s criticism

November 4, 2011
By
Unit root versus breaking trend: Perron’s criticism

I came across an ingenious simulation by Perron during my Time-series lecture which I thought was worth sharing. The idea was to put your model to a further test of breaking trend before accepting the null of unit root. Let me try and illustrate this in simple language. A non-stationary time series is one that has its mean changing...

Read more »

Confidence interval for predictions with GLMs

November 4, 2011
By
Confidence interval for predictions with GLMs

Consider a (simple) Poisson regression . Given a sample where , the goal is to derive a 95% confidence interval for given , where is the prediction. Hence, we want to derive a confidence interval for the prediction, not the potential observation...

Read more »

Confidence interval for predictions with GLMs

November 4, 2011
By
Confidence interval for predictions with GLMs

Consider a (simple) Poisson regression . Given a sample where , the goal is to derive a 95% confidence interval for given , where is the prediction. Hence, we want to derive a confidence interval for the prediction, not the potential observation, i.e. the dot on the graph below > r=glm(dist~speed,data=cars,family=poisson) > P=predict(r,type="response", + newdata=data.frame(speed=seq(-1,35,by=.2))) > plot(cars,xlim=c(0,31),ylim=c(0,170)) > abline(v=30,lty=2)...

Read more »

How much does "Beta" change depending on time?

November 1, 2011
By
How much does "Beta" change depending on time?

You may often use "Beta" to measure the market exposure of your portfolio because it's easy to calculate.Since I have been wondering how much "Beta" change depending on time, more precisely writing, data-set and the period of return time series, I...

Read more »

Bootstrapping a Single Statistic (k=1) The following example…

November 1, 2011
By
Bootstrapping a Single Statistic (k=1)

The following example…

Bootstrapping a Single Statistic (k=1) The following example generates the bootstrapped 95% confidence interval for R-squared in the linear regression of miles per gallon (mpg) on car weight (wt) and displacement (disp). The data source is mtcars. The...

Read more »

Using Sparse Matrices in R

October 31, 2011
By
Using Sparse Matrices in R

Introduction I’ve recently been working with a couple of large, extremely sparse data sets in R. This has pushed me to spend some time trying to master the CRAN packages that support sparse matrices. This post describes three of them: the Matrix, slam and glmnet packages. The first two packages provide data storage classes for

Read more »

Testing for…

October 31, 2011
By
Testing for…

Input Output Testing for regression Input: advertising=c(1,2,3,4,5) sales=c(1,1,2,2,4) sales.Reg=lm(sales~advertising) summary(sales.Reg) Output: > advertising=c(1,2,3,4,5) > sales=c(1,1,2,2,4) > > sales.Reg=lm(sales~advertising) > summary...

Read more »

Learning R: Project 1, Part 2

October 30, 2011
By
Learning R: Project 1, Part 2

So it's been a week since I started down this path.  I worked most of this out over last weekend, went to a conference, had hectic week at work, and then realized I lost my work.  Gah.I'll be posting my general thoughts on R later.  Most...

Read more »

Bayesian ideas and data analysis

October 30, 2011
By
Bayesian ideas and data analysis

Here is another Bayesian textbook that appeared recently. I read it in the past few days and, despite my obvious biases and prejudices, I liked it very much! It has a lot in common (at least in spirit) with our Bayesian Core, which may explain why I feel so benevolent towards Bayesian ideas and

Read more »

Modelling with R: part 5

October 30, 2011
By

In our exercise of learning modelling in R, we have till now succeeded in doing the following:Importing the dataPreparing and transforming the dataRunning a logistic regressionCreating a decision treeSpecifically, we created a decision tree using the r...

Read more »