2206 search results for "regression"

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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Quick History 2: GLMs, R and large data sets

May 22, 2014
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by Joseph Rickert In last week’s post, I sketched out the history of Generalized Linear Models and their implementations. In this post I’ll attempt to outline how GLM functions evolved in R to handle large data sets. The first function to make it possible to build GLM models with datasets that are too big to fit into memory was...

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Ensemble Methods Part 3: Revolution Analytics Big Data Random Forest Function

May 20, 2014
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Ensemble Methods Part 3: Revolution Analytics Big Data Random Forest Function

by Mike Bowles In two previous posts, A Thumbnail History of Ensemble Methods and Ensemble Packages in R, Mike Bowles — a machine learning expert and serial entrepreneur — laid out a brief history of ensemble methods and described a few of the many implementations in R. In this post Mike takes a detailed look at the Random Forests...

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RMOA: Massive online data stream classifications with R & MOA

RMOA: Massive online data stream classifications with R & MOA

For those of you who don't know MOA. MOA stands for Massive On-line Analysis and is an open-source framework that allows to build and run experiments of machine learning or data mining on evolving data streams. The website of MOA (http://moa.cms.waikato.ac.nz) indicates it contains machine learning algorithms for classification, regression, clustering, outlier detection and recommendation engines.   For R users...

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Can We do Better than R-squared?

May 16, 2014
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Can We do Better than R-squared?

Blog post: R-squared can mislead us. Here are two related statistics for a better assessment of regression models.

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