Posts Tagged ‘ Uncategorized ’

Female world leaders by year of election

July 29, 2016
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Female world leaders by year of election

This week Hillary Clinton became the first woman to be nominated for president of the US by a major political party. Although this is a first for the US, many other countries have already passed this milestone. In fact, 60 countries have already elected women as presidents and prime ministers. […] The post Female world leaders by year of election appeared first on The DO Loop.

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How to visualize a kernel density estimate

July 27, 2016
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How to visualize a kernel density estimate

A kernel density estimate (KDE) is a nonparametric estimate for the density of a data sample. A KDE can help an analyst determine how to model the data: Does the KDE look like a normal curve? Like a mixture of normals? Is there evidence of outliers in the data? In […] The post How to visualize a kernel density estimate appeared first on The DO Loop.

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Statistical model building and the SELECT procedures in SAS

July 25, 2016
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Statistical model building and the SELECT procedures in SAS

Last week I read an interesting paper by Bob Rodriguez: "Statistical Model Building for Large, Complex Data: Five New Directions in SAS/STAT Software." In it, Rodriguez summarizes five modern techniques for building predictive models and highlights recent SAS/STAT procedures that implement those techniques. The paper discusses the following high-performance (HP) […] The post Statistical model building and the SELECT procedures in SAS appeared first on The DO Loop.

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A scalable particle filter in Scala

July 22, 2016
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A scalable particle filter in Scala

Introduction Many modern algorithms in computational Bayesian statistics have at their heart a particle filter or some other sequential Monte Carlo (SMC) procedure. In this blog I’ve discussed particle MCMC algorithms which use a particle filter in the inner-loop in order to compute a (noisy, unbiased) estimate of the marginal likelihood of the data. These … Continue reading A scalable particle filter in Scala

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Do you write unnecessary SAS statements?

July 20, 2016
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Do you write unnecessary SAS statements?

I'm addicted to you. You're a hard habit to break. Such a hard habit to break. —  Chicago, "Hard Habit To Break" Habits are hard to break. For more than 20 years I've been putting semicolons at the end of programming statements in SAS, C/C++, and Java/Javascript. But lately I've been […] The post Do you write unnecessary SAS statements? appeared first on The DO Loop.

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Color markers in a scatter plot by a third variable in SAS

July 18, 2016
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Color markers in a scatter plot by a third variable in SAS

One of my favorite new features in PROC SGPLOT in SAS 9.4m2 is addition of the COLORRESPONSE= and COLORMODEL= options to the SCATTER statement. By using these options, it is easy to color markers in a scatter plot so that the colors indicate the values of a continuous third variable. […] The post Color markers in a scatter plot by a third variable in SAS appeared first on The DO…

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Absorbing Markov chains in SAS

July 13, 2016
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Absorbing Markov chains in SAS

Last week I showed how to represent a Markov transition matrix in the SAS/IML matrix language. I also showed how to use matrix multiplication to iterate a state vector, thereby producing a discrete-time forecast of the state of the Markov chain system. This article shows that the expected behavior of […] The post Absorbing Markov chains in SAS appeared first on The DO Loop.

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Break a sentence into words in SAS

July 11, 2016
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Break a sentence into words in SAS

Two of my favorite string-manipulation functions in the SAS DATA step are the COUNTW function and the SCAN function. The COUNTW function counts the number of words in a long string of text. Here "word" means a substring that is delimited by special characters, such as a space character, a […] The post Break a sentence into words in SAS appeared first on The DO Loop.

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Markov transition matrices in SAS/IML

July 7, 2016
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Markov transition matrices in SAS/IML

Many computations in elementary probability assume that the probability of an event is independent of previous trials. For example, if you toss a coin twice, the probability of observing "heads" on the second toss does not depend on the result of the first toss. However, there are situations in which […] The post Markov transition matrices in SAS/IML appeared first on The DO Loop.

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