2160 search results for "regression"

Geomorph 2.1 Now Available!

June 2, 2014
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Geomorph users,We have uploaded version 2.1 to CRAN. The windows and mac binaries have been compiled and the tarball is available.Version 2.1 comes with some small changes and new features: Mike Collyer has now officially joined the geomorph ...

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A new gitbook – learnR

May 30, 2014
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Gitbook is rather a relatively new concept on the web. It provides a user-friendly framework for authors to write and produce online books with beautiful illustrations and responsive interactions. It allows authors to write in Markdown syntax, which is very easy to learn and use, so that they can focus more on the contents they try to...

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A new gitbook – learnR

May 30, 2014
By

Gitbook is rather a relatively new concept on the web. It provides a user-friendly framework for authors to write and produce online books with beautiful illustrations and responsive interactions. It allows authors to write in Markdown syntax, which is very easy to learn and use, so that they can focus more on the contents they try to...

Read more »

Trimming the Fat from glm() Models in R

May 30, 2014
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Trimming the Fat from glm() Models in R

One of the attractive aspects of logistic regression models (and linear models in general) is their compactness: the size of the model grows in the number of coefficients, not in the size of the training data. With R, though, glm models are not so concise; we noticed this to our dismay when we tried to Related posts:

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Bayesian nonparametric weighted sampling inference

May 28, 2014
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Bayesian nonparametric weighted sampling inference

Yajuan Si, Natesh Pillai, and I write: It has historically been a challenge to perform Bayesian inference in a design-based survey context. The present paper develops a Bayesian model for sampling inference using inverse-probability weights. We use a hierarchical approach in which we model the distribution of the weights of the nonsampled units in the The post

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Twinkle,twinkle little STAR

May 26, 2014
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Twinkle,twinkle little STAR

At the recent R/Finance 2014 conference in Chicago I gave a talk on Smooth Transition AR models and a new package for estimating them called twinkle. In this blog post I will provide a short outline of the models and an introduction to the package and its features. Financial markets have a strong cyclical component

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Twinkle,twinkle little STAR

May 26, 2014
By
Twinkle,twinkle little STAR

At the recent R/Finance 2014 conference in Chicago I gave a talk on Smooth Transition AR models and a new package for estimating them called twinkle. In this blog post I will provide a short outline of the models and an introduction to the package and its features. Financial markets have a strong cyclical component

Read more »

Using sentiment analysis to predict ratings of popular tv series

May 26, 2014
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Using sentiment analysis to predict ratings of popular tv series

Unless you’ve been living under a rock for the last few years, you have probably heard of TV shows such as Breaking Bad, Mad Men, How I Met Your Mother or Game of Thrones. While I generally don’t spend a whole lot of time watching TV, I have also undergone some pretty intense binge-watching sessions

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A Sequence of 9 Courses on Data Science Starts on Coursera on 2 June and 7 July 2014

May 26, 2014
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A Sequence of 9 Courses on Data Science Starts on Coursera on 2 June and 7 July 2014

A sequence of 9 courses on Data Science will start on Coursera on 2 June and 7 July 2014, to be lectured by(Associate/Assistant) Professors of Johns Hopkins University. The courses are designed for students to learn to become Data Scientists … Continue reading →

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Regularization implementation in R : Bais and Variance diagnosis

May 22, 2014
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Regularization implementation in R : Bais and Variance diagnosis

Welcome to this blog post. In previous posts I discussed about the linear regression and logistic regression in detail. We used Andrew NG’s ML class dataset to fit linear regression and logistic regression. We also discussed about step by step implementation in R along with cost function and gradient descent. In this post I will The post Regularization...

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