# 841 search results for "LaTeX"

## Typos…

October 5, 2010
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Edward Kao just sent another typo found both in  Monte Carlo Statistical Methods (Problem 3.21) and in Introducing Monte Carlo Methods with R (Exercise 3.17), namely that should be I also got another email from Jerry Sin mentioning that matrix summation in the matrix commands of Figure 1.2 of Introducing Monte Carlo Methods with R

## The ARIMAX model muddle

October 4, 2010
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There is often confusion about how to include covariates in ARIMA models, and the presentation of the subject in various textbooks and in R help files has not helped the confusion. So I thought I’d give my take on the issue. To keep it simple, I will only describe non-seasonal ARIMA models although the ideas

## Typo in Example 5.18

October 2, 2010
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Edward Kao pointed out several typos in Example 5.18 of Monte Carlo Statistical Methods. First, the customers in area i should be double-indexed, i.e. which implies in turn that . Then the summary T should be defined as and as given that the first m customers have the fifth plan missing. Filed under: Books, R,

## UCLA Statistics: Analyzing Thesis/Dissertation Lengths

September 29, 2010
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As I am working on my dissertation and piecing together a mess of notes, code and output, I am wondering to myself “how long is this thing supposed to be?” I am definitely not into this to win the prize for longest dissertation. I just want to say my piece, make my point and move on. I’ve heard that...

## Forecasting with long seasonal periods

September 28, 2010
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I am often asked how to fit an ARIMA or ETS model with data having a long seasonal period such as 365 for daily data or 48 for half-hourly data. Generally, seasonal versions of ARIMA and ETS models are designed for shorter periods such as 12 for monthly data or 4 for quarterly data. The

## Galton & simulation

September 27, 2010
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Stephen Stigler has written a paper in the Journal of the Royal Statistical Society Series A on Francis Galton’s analysis of (his cousin) Charles Darwin’ Origin of Species, leading to nothing less than Bayesian analysis and accept-reject algorithms! “On September 10th, 1885, Francis Galton ushered in a new era of Statistical Enlightenment with an address

## Riemann, Langevin & Hamilton [reply]

September 27, 2010
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Here is a (prompt!) reply from Mark Girolami corresponding to the earlier post: In preparation for the Read Paper session next month at the RSS, our research group at CREST has collectively read the Girolami and Calderhead paper on Riemann manifold Langevin and Hamiltonian Monte Carlo methods and I hope we will again produce a

## Effective sample size

September 23, 2010
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In the previous days I have received several emails asking for clarification of the effective sample size derivation in “Introducing Monte Carlo Methods with R” (Section 4.4, pp. 98-100). Formula (4.3) gives the Monte Carlo estimate of the variance of a self-normalised importance sampling estimator (note the change from the original version in Introducing Monte

## A (fast!) null model of bipartite networks

September 12, 2010
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$A (fast!) null model of bipartite networks$

One of the challenges for ecologists working with trophic/interaction networks is to understand their organization. One of the possible approaches is to compare them across a random model, with more or less constraints, in order to estimate the departure from randomness. To this effect, null models have been developed. The basic idea behind a null