New in forecast 5.0

January 27, 2014
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New in forecast 5.0

Last week, version 5.0 of the forecast package for R was released. There are a few new functions and changes made to the package, which is why I increased the version number to 5.0. Thanks to Earo Wang for helping with this new version. Handling missing values and outliers Data cleaning is often the first step that data scientists and analysts take to ensure statistical modelling is supported by good…

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Twitter sucks, and people are gullible as f…

January 26, 2014
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Hey, and I did it in less than 140 characters! The above was my response to this item which David Hogg forwarded to me. The next thing you know, people are going to claim that women are three times as likely to wear red pink when . . . Naaah, forget about it, that would […]The post Twitter sucks, and people are gullible as f… appeared first on Statistical Modeling,…

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Soccer Intervention, A Story in Semi-Demi Hierarchical Models with a Different Number of Hierarchies in Treatment and Control

January 26, 2014
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Was doing a data analysis and power calculation for a proposed group randomized study, and came across an interesting feature where the resulting model for the data will necessarily be different for treatment and control. Treatment will have 3 hierarch...

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Infoviz on top of stat graphic on top of spreadsheet

January 26, 2014
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Infoviz on top of stat graphic on top of spreadsheet

Kaiser points to this infoviz from MIT’s Technology Review: Kaiser writes: What makes the designer want to tilt the reader’s head? This chart is unreadable. It also fails the self-sufficiency test. All 13 data points are printed onto the chart. You really don’t need the axis, and the gridlines. A further design flaw is the […]The post Infoviz on top of stat graphic on top of spreadsheet appeared first on…

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Tuning optim with parscale

January 26, 2014
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I often get questions what is the use of parscale parameter in optim procedure in GNU R. Therefore I have decided to write a simple example showing its usage and importance. The function I test is a simplified version of estimation problem I had to sol...

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Converting plots to data II

January 26, 2014
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Converting plots to data II

There were quite some reactions on lasts week's post converting plots to data. Two additional programs were mentioned; WebPlotDigitzier and DataThief. Note that google or wikipedia has a bunch of alternatives. Datathief is shareware, a java program, so...

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Alexander Aitken

January 26, 2014
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Alexander Aitken

Alexander Aitken was one of New Zealand's greatest mathematicians - see my earlier post. As an econometrician, you may be very surprised how much you owe him!Want to check out more about this amazing man? See www.nzedge.com/alexander-aitken/ .© 2014, ...

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Introduction to the particle Gibbs sampler

January 25, 2014
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Introduction to the particle Gibbs sampler

Introduction Particle MCMC (the use of approximate SMC proposals within exact MCMC algorithms) is arguably one of the most important developments in computational Bayesian inference of the 21st Century. The key concepts underlying these methods are described in a famously impenetrable “read paper” by Andrieu et al (2010). Probably the most generally useful method outlined […]

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Introduction to the particle Gibbs sampler

January 25, 2014
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Introduction to the particle Gibbs sampler

Introduction Particle MCMC (the use of approximate SMC proposals within exact MCMC algorithms) is arguably one of the most important developments in computational Bayesian inference of the 21st Century. The key concepts underlying these methods are described in a famously impenetrable “read paper” by Andrieu et al (2010). Probably the most generally useful method outlined […]

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Big Data Events

January 25, 2014
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Big Data Events

The Big Data discussion builds momentum in Official Statistics. In October 2012 at the UNECE High-level Seminar on Modernization of Statistical …Continue reading →

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Big Data Events

January 25, 2014
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Big Data Events

The Big Data discussion builds momentum in Official Statistics. In October 2012 at the UNECE High-level Seminar on Modernization of Statistical …Continue reading →

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Vectorisation is your best friend: replacing many elements in a character vector

January 25, 2014
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As with any programming language, R allows you to tackle the same problem in many different ways or styles. These styles differ both in the amount of code, readability, and speed. In this post I want to illustrate this by… See more ›

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Xihong Lin on sparsity and density

January 25, 2014
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I pointed Xihong Lin to this post from last month regarding Hastie and Tibshirani’s “bet on sparsity principle.” I argued that, in the worlds in which I work, in social and environmental science, every contrast is meaningful, even if not all of them can be distinguished from noise given a particular dataset. That is, I […]The post Xihong Lin on sparsity and density appeared first on Statistical Modeling, Causal Inference,…

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Overnight vs. Intraday ETF Returns

January 25, 2014
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I haven’t done much “googling” before posting, so this topic might have been covered elsewhere but I think it’s  really worth sharing or repeating anyway. A lot has been written about the source of  ETF returns (some insights might be found here). In a nutshell some analysis found that the bulk of the return is made […]

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Machine Learning Lesson of the Day – The “No Free Lunch” Theorem

Machine Learning Lesson of the Day – The “No Free Lunch” Theorem

A model is a simplified representation of reality, and the simplifications are made to discard unnecessary detail and allow us to focus on the aspect of reality that we want to understand.  These simplifications are grounded on assumptions; these assumptions may hold in some situations, but may not hold in other situations.  This implies that […]

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Testing Up, or Testing Down?

January 24, 2014
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Testing Up, or Testing Down?

Students are told that if you're going to go in for sequential testing, when determining the specification of a model, then the sequence that you follow should be "from the general to the specific". That is, you should start off with a "large" model, a...

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Advice to a Prospective Biostatistician

January 24, 2014
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This is advice to a prospective student wondering whether to go into public health/epi or biostatistics. I'm willing to blindly argue for biostatistics, but prospective students might find it more useful if I frame the issues so they can decide for the...

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CO2 Emissions per Dollar

January 24, 2014
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CO2 Emissions per Dollar

Originally posted on CoolStatsBlog:For all the flak China receives about its greenhouse gas emissions, the average Chinese produces less than a third the amount of CO2 than his American counterpart. It just so happens that there are 1.3 billion Chinese, and 0.3 billion Americans, so China ends up producing more CO2. Carbon dioxide and…

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Rob Hyndman on Forecasting

January 24, 2014
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Rob Hyndman on Forecasting

If you have an interest in forecasting, especially economic forecasting, the Rob Hyndman's name will be familiar to you. Hailing from my old stamping ground - Monash University - Rob is one of the world's top forecasting experts. Without going int...

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Phil 6334: Slides from Day #1: Four Waves in Philosophy of Statistics

January 24, 2014
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Phil 6334: Slides from Day #1: Four Waves in Philosophy of Statistics

First installment 6334 syllabus (Mayo and Spanos) D. Mayo slides from Day #1: Jan 23, 2014   I will post seminar slides here (they will generally be ragtag affairs), links to the papers are in the syllabus.Filed under: Phil 6334 class material, Phi...

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Parables vs. stories

January 24, 2014
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Parables vs. stories

God is in every leaf of every tree, but he is not in every leaf of every parable. Let me explain with a story. A few months ago I read the new book, Doing Data Science, by Rachel Schutt and Cathy O’Neal, and I came across the following motivation for comprehensive integration of data sources, […]The post Parables vs. stories appeared first on Statistical Modeling, Causal Inference, and Social Science.

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Statistics and Chemistry Lesson of the Day – Illustrating Basic Concepts in Experimental Design with the Synthesis of Ammonia

Statistics and Chemistry Lesson of the Day – Illustrating Basic Concepts in Experimental Design with the Synthesis of Ammonia

To summarize what we have learned about experimental design in the past few Applied Statistics Lessons of the Day, let’s use an example from physical chemistry to illustrate these basic principles. Ammonia (NH3) is widely used as a fertilizer in industry.  It is commonly synthesized by the Haber process, which involves a reaction between hydrogen […]

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Peer Review, Part 5: The Importance of Gatekeepers

January 24, 2014
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Peer Review, Part 5: The Importance of Gatekeepers

The purpose of peer review is to separate the wheat from the chaff, the good from the bad, the brilliant from the clinically insane – you get the picture. But why? Why filter and not just let anybody publish whatever they want? Why Gatekeepers? And Why Gates? In the old days, there was the resource […]

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