Probabilistic Energy Forecasting

October 14, 2013
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Probabilistic Energy Forecasting

The International Journal of Forecasting is calling for papers on probabilistic energy forecasting. Here are the details (taken from Tao Hong’s blog). In today’s competitive and dynamic environment, more and more decision making processes in the energy industry are relying on probabilistic forecasts. The applications of probabilistic energy forecasts spread across planning and operations of the entire energy value chain. We are seeking papers from researchers working on the areas…

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Sunday data/statistics link roundup (10/13/13)

October 13, 2013
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A really interesting comparison between educational and TV menus (via Rafa). On a related note, it will be interesting to see how/whether the traditional educational system will be disrupted. I'm as into the MOOC thing as the next guy, but I'm … Continue reading →

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When Luca meets Laura

October 13, 2013
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When Luca meets Laura

How to explain important demographic indicators? Try it by telling the story of Laura and Luca. Statistical storytelling at its …Continue reading »

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Remembering the Gist, But Not the Details: One-Dimensional Representation of Consumer Ratings

October 13, 2013
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Remembering the Gist, But Not the Details:  One-Dimensional Representation of Consumer Ratings

In survey research, it makes a difference how the question is asked.  "How would you rate the service you received at that restaurant?" is not the same as "Did you have to wait to be seated, to order your meal, to be served your food, or to pay yo...

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Prices of houses in the Netherlands

October 13, 2013
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Prices of houses in the Netherlands

The last couple of days I read a number of times about stabilization in house prices which had been dropping due to the crisis. And you get hit by numbers such as change against Q2 2013 or Q3 2012. These are accompanied by reasons why this or that quar...

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New issue of Symposium magazine

October 13, 2013
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New issue of Symposium magazine

“Where academia meets public life”: The Changing Face of Violence Joel F. Harrington A debate has kicked off among scholars on whether we have become inherently more peaceful. A more important question is whether we actually understand the many forms violence takes. The Professor as Digital Native Interview with Mary Beard Why Central Bank Transparency […]The post New issue of Symposium magazine appeared first on Statistical Modeling, Causal Inference, and…

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Sir David Cox: a comment on the post, “Was Hosiasson pulling Jeffreys’ leg?”

October 12, 2013
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Sir David Cox: a comment on the post, “Was Hosiasson pulling Jeffreys’ leg?”

David Cox sent me a letter relating to my post of Oct.5, 2013. He has his own theory as to who might have been doing the teasing! I’m posting it  here, with his permission:  Dear Deborah I was interested to see the correspondence about Jeffreys and the possible teasing by Neyman’s associate. It brought a […]

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Stochastic Optimization in R by Parallel Tempering

October 12, 2013
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Stochastic Optimization in R by Parallel Tempering

I’ve written a few posts now about using parallel tempering to sample from complicated multi-modal target distributions but there are also other benefits and uses to this algorithm. There is a nice post on Darren Wilkinson’s blog about using tempered posteriors for marginal likelihood calculations. There is also another area where parallel tempering finds application, […] The post Stochastic Optimization in R by Parallel Tempering appeared first on Lindons Log.

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Visualization, “big data”, and EDA

October 12, 2013
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Dean Eckles writes: Given your ongoing discussion of info viz for different goals, you might be interested in Sinan Aral’s new article: This touches on several info viz themes: - Viz for yourself (or your team) vs. visualizations to share the final conclusions - Viz for identifying promising features for use in modeling - Viz […]The post Visualization, “big data”, and EDA appeared first on Statistical Modeling, Causal Inference, and…

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Using ggplot2

October 12, 2013
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Using ggplot2

I have a standard code for ggplot2 which I use to make line graphs, scatter plots, and histograms. For lines or scatters: p<- ggplot(x, aes(x=Year, y=Rank, colour=Uni, group=Uni)) #colour lines by variable Uni #group Uni labelled variables in the same line Then:  p + #you get an error if not for this step geom_line(size=1.2) + […]

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Earthquake dynamics

October 12, 2013
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Earthquake dynamics

I just upload on http://hal.archives-ouvertes.fr/hal-00871883 a joint paper entitled Modeling earthquake dynamics. In this paper, we investigate questions arising in Parsons & Geist (2012). Pseudo causal models connecting magnitudes and waiting times are consider, through generalized regression. We do use conditional model (magnitude given previous waiting time, and conversely) as an extension to joint distribution model described in Nikoloulopoulos & Karlis (2008). On the one hand, we fit a Pareto distribution…

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Books to Read While the Algae Grow in Your Fur, September 2013

October 11, 2013
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Betsy Sinclair, The Social Citizen: Peer Networks and Political Behavior Introspectively, it seems pretty obvious that our political behavior and attitudes are influenced by our friends and family; this is an attempt to document and quantify that inf...

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October First Is Too Late

October 11, 2013
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Have some delayed book-chat from April, May, June, July, August and September.

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Triple Header (Next Week at the Statistics / Machine Learning Seminars)

October 11, 2013
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Attention conservation notice: Only relevant if you (1) really care about statistics, and (2) will be in Pittsburgh on Monday. Through a fortuitous concourse of calendars, we will have three outstanding talks on Monday, 14 October 2013. In chronolog...

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Functions as Objects (Introduction to Statistical Computing)

October 11, 2013
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Lecture 10: Functions in R are objects, just like everything else, and so can be both arguments to and return values of functions, with no special machinery required. Examples from math (especially calculus) of functions with other functions as argum...

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Lab: Testing Our Way to Outliers (Introduction to Statistical Computing)

October 11, 2013
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In which we use Tukey's rule for identifying outliers as an excuse to learn about debugging and testing. Assignment Introduction to Statistical Computing

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Homework: Dimensions of Anomaly (Introduction to Statistical Computing)

October 11, 2013
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In which we continue to practice the arts of debugging and testing, while learning about making our code more general, handling awkward special cases, and pondering what it means to say that an observation is an outlier. Assignment, data, deliberatel...

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Simple Optimization (Introduction to Statistical Computing)

October 11, 2013
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Lecture 10: Basics from calculus about minima. Taylor series. Gradient descent and Newton's method. Curve-fitting by optimization. Illustrations with optim and nls. R for examples Reading: recipes 13.1 and 13.2 in The R Cookbook; chapters I.1, II.1 a...

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Abstraction and Refactoring (Introduction to Statistical Computing)

October 11, 2013
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Lecture 11: Abstraction as a way to make programming more friendly to human beings. Refactoring as a form of abstraction. The rectification of names. Consolidation of related values into objects. Extracting common operations. Defining general operat...

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Lab: I Can Has Likelihood Surface? (Introduction to Statistical Computing)

October 11, 2013
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In which we practice passing functions as arguments to other functions, by way of an introduction to likelihood and its maximization; and, incidentally, work more with plotting in R. Assignment Introduction to Statistical Computing

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Homework: I Made You a Likelihood Function, But I Ate It (Introduction to Statistical Computing)

October 11, 2013
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In which we continue to practice using functions as arguments and as return values, while learning something about the standard error of maximum likelihood estimates, and about the modularity of methods like the jack-knife. Assignment Introductio...

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Split, Apply, Combine: Using Basic R (Introduction to Statistical Computing)

October 11, 2013
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Lecture 12: Design patterns and their benefits: clarity on what is to be done, flexibility about how to do it, ease of adapting others' solutions. The split/apply/combine pattern: divide big structured data sets up into smaller, related parts; apply ...

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Split, Apply, Combine: Using plyr (Introduction to Statistical Computing)

October 11, 2013
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Lecture 13, Split/apply/combine II: using plyr. Abstracting the split/apply/combine pattern: using a single function to appropriately split up the input, apply the function, and combine the results, depending on the type of input and output data. Sy...

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