**hallucigenia + statistical_software**
14

smooth v2.0.0. What’s new

july 2017 by hallucigenia

Good news, everyone! smooth package has recently received a major update. The version on CRAN is now v2.0.0. I thought that this is a big deal, so I decided to pause for a moment and explain what has happened, and why this new version is interesting.

First of all, there is a new function, ves(), that implements Vector Exponential Smoothing model. This model allows estimating several series together and capture possible interactions between them. It can be especially useful if you need to forecast several similar products and can assume that smoothing parameter or initial seasonal indices are similar across all the series. Let’s say, you want to produce forecasts for several SKUs of cofvefe. You may unite the data of their sales in a vector and use one and the same smoothing parameter across the series using the parameter persistence="group". However, if you think that sales of one type of cofvefe may influence the sales of the other one, you may take this into account and set persistence="dependent". You can also switch between "group" or "individual" initial values, initialSeason, transition and phi (damping parameter). Just keep in mind that vector models can be greedy in the number of parameters and in order to use them efficiently, you my need to have large samples.

smoothing_and_penalization
statistical_software
statistics:additive_models
statistics:time_series
R_packages
First of all, there is a new function, ves(), that implements Vector Exponential Smoothing model. This model allows estimating several series together and capture possible interactions between them. It can be especially useful if you need to forecast several similar products and can assume that smoothing parameter or initial seasonal indices are similar across all the series. Let’s say, you want to produce forecasts for several SKUs of cofvefe. You may unite the data of their sales in a vector and use one and the same smoothing parameter across the series using the parameter persistence="group". However, if you think that sales of one type of cofvefe may influence the sales of the other one, you may take this into account and set persistence="dependent". You can also switch between "group" or "individual" initial values, initialSeason, transition and phi (damping parameter). Just keep in mind that vector models can be greedy in the number of parameters and in order to use them efficiently, you my need to have large samples.

july 2017 by hallucigenia

hadley/tidyr · GitHub

statistical_software
r_packages
R
data_management
statistical_computing

june 2014 by hallucigenia

tidyr - Easily tidy data with spread and gather functions.

june 2014 by hallucigenia

A Primer on Regression Splines

may 2014 by hallucigenia

"B-splines constitute an appealing method for the nonparametric estimation of a range of statis- tical objects of interest. In this primer we focus our attention on the estimation of a conditional mean, i.e. the ‘regression function’."

statistics
non_parametrics
regression
statistical_software
review
splines
may 2014 by hallucigenia

Modified Bessel function of the second kind: Introduction to the Bessel functions (subsection Bessels/05)

april 2014 by hallucigenia

How to calculate the derivative of a Bessel function. Handy for figuring out a gradient for a Von Mises distribution...

Take-home: Deriv of besselI(x,nu=0) = 0.5*(besselI(x, -1)-besselI(x,1))

math_and_stats
numeric_methods
statistical_software
Take-home: Deriv of besselI(x,nu=0) = 0.5*(besselI(x, -1)-besselI(x,1))

april 2014 by hallucigenia

Crossfilter

january 2013 by hallucigenia

Crossfilter is a JavaScript library for exploring large multivariate datasets in the browser. Crossfilter supports extremely fast (<30ms) interaction with coordinated views, even with datasets containing a million or more records; we built it to power analytics for Square Register, allowing merchants to slice and dice their payment history fluidly.

Since most interactions only involve a single dimension, and then only small adjustments are made to the filter values, incremental filtering and reducing is significantly faster than starting from scratch. Crossfilter uses sorted indexes (and a few bit-twiddling hacks) to make this possible, dramatically increasing the performance of live histograms and top-K lists. For more details on how Crossfilter works, see the API reference.

statistical_methods
statistical_software
web_applications
Programming
java
Since most interactions only involve a single dimension, and then only small adjustments are made to the filter values, incremental filtering and reducing is significantly faster than starting from scratch. Crossfilter uses sorted indexes (and a few bit-twiddling hacks) to make this possible, dramatically increasing the performance of live histograms and top-K lists. For more details on how Crossfilter works, see the API reference.

january 2013 by hallucigenia

bundles : statistics

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