**statistics:time_series**8

VAST: Spatio-temporal analysis of univariate or multivariate data, e.g., standardizing data for multiple species or stage

september 2017 by hallucigenia

VAST

Is an R package for implementing a spatial delta-generalized linear mixed model (delta-GLMM) for multiple categories (species, size, or age classes) when standardizing survey or fishery-dependent data.

Builds upon a previous R package SpatialDeltaGLMM (public available here), and has unit-testing to automatically confirm that VAST and SpatialDeltaGLMM give identical results (to the 3rd decimal place for parameter estimates) for several varied real-world case-study examples

Has built in diagnostic functions and model-comparison tools

Is intended to improve analysis speed, replicability, peer-review, and interpretation of index standardization methods

Background

This tool is designed to estimate spatial variation in density using spatially referenced data, with the goal of habitat associations (correlations among species and with habitat) and estimating total abundance for a target species in one or more years.

The model builds upon spatio-temporal delta-generalized linear mixed modelling techniques (Thorson Shelton Ward Skaug 2015 ICESJMS), which separately models the proportion of tows that catch at least one individual ("encounter probability") and catch rates for tows with at least one individual ("positive catch rates").

Submodels for encounter probability and positive catch rates by default incorporate variation in density among years (as a fixed effect), and can incorporate variation among sampling vessels (as a random effect, Thorson and Ward 2014) which may be correlated among categories (Thorson Fonner Haltuch Ono Winker In press).

Spatial and spatiotemporal variation are approximated as Gaussian Markov random fields (Thorson Skaug Kristensen Shelton Ward Harms Banante 2014 Ecology), which imply that correlations in spatial variation decay as a function of distance.

statistics:gams
statistics:time_series
statistics:fisheries
fisheries
fisheries:methods
statistics:bayesian
statistics:spatial
R_packages
Is an R package for implementing a spatial delta-generalized linear mixed model (delta-GLMM) for multiple categories (species, size, or age classes) when standardizing survey or fishery-dependent data.

Builds upon a previous R package SpatialDeltaGLMM (public available here), and has unit-testing to automatically confirm that VAST and SpatialDeltaGLMM give identical results (to the 3rd decimal place for parameter estimates) for several varied real-world case-study examples

Has built in diagnostic functions and model-comparison tools

Is intended to improve analysis speed, replicability, peer-review, and interpretation of index standardization methods

Background

This tool is designed to estimate spatial variation in density using spatially referenced data, with the goal of habitat associations (correlations among species and with habitat) and estimating total abundance for a target species in one or more years.

The model builds upon spatio-temporal delta-generalized linear mixed modelling techniques (Thorson Shelton Ward Skaug 2015 ICESJMS), which separately models the proportion of tows that catch at least one individual ("encounter probability") and catch rates for tows with at least one individual ("positive catch rates").

Submodels for encounter probability and positive catch rates by default incorporate variation in density among years (as a fixed effect), and can incorporate variation among sampling vessels (as a random effect, Thorson and Ward 2014) which may be correlated among categories (Thorson Fonner Haltuch Ono Winker In press).

Spatial and spatiotemporal variation are approximated as Gaussian Markov random fields (Thorson Skaug Kristensen Shelton Ward Harms Banante 2014 Ecology), which imply that correlations in spatial variation decay as a function of distance.

september 2017 by hallucigenia

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

Kernel Cookbook

gaussian_processes
statistics:multivariate
statistics:time_series

january 2016 by hallucigenia

A reference manual for creating covariance functions.

january 2016 by hallucigenia

CRAN - Package spBayes

june 2015 by hallucigenia

Fits univariate and multivariate spatio-temporal models with Markov chain Monte Carlo (MCMC).

statistics:spatial
statistics:time_series
statistics:multivariate
R_packages
R
to_try
june 2015 by hallucigenia

Nonlinear Time Series: Theory, Methods and Applications with R Examples (Chapman & Hall/CRC Texts in Statistical Science) - Kindle edition by Randal Douc, Eric Moulines, David Stoffer. Professional & Technical Kindle eBooks @ Amazon.com.

time_series
books
to_read
non_parametrics
statistics:time_series
statistics:multivariate

may 2015 by hallucigenia

Nonlinear Time Series: Theory, Methods and Applications with R Examples (Chapman & Hall/CRC Texts in Statistical Science) - Kindle edition by Randal Douc, Eric Moulines, David Stoffer. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Nonlinear Time Series: Theory, Methods and Applications with R Examples (Chapman & Hall/CRC Texts in Statistical Science).

may 2015 by hallucigenia

Bayesian Spectrum and Chirp Analysis - E.T. Jaynes

august 2014 by hallucigenia

We seek optimal methods of estimating power spectrum and chirp (frequency change)

rate for the case that one has incomplete noisy data on values y(t) of a time series. The Schuster

periodogram turns out to be a \sucient statistic" for the spectrum, a generalization playing the

same role for chirped signals. However, the optimal processing is not a linear ltering operation like

the Blackman{Tukey smoothing of the periodogram, but a nonlinear operation. While suppressing

noise/side lobe artifacts it achieves the same kind of improved resolution that the Burg method did

for noiseless data.

spectral_theory
fourier_analysis
statistical_methods
statistics:time_series
rate for the case that one has incomplete noisy data on values y(t) of a time series. The Schuster

periodogram turns out to be a \sucient statistic" for the spectrum, a generalization playing the

same role for chirped signals. However, the optimal processing is not a linear ltering operation like

the Blackman{Tukey smoothing of the periodogram, but a nonlinear operation. While suppressing

noise/side lobe artifacts it achieves the same kind of improved resolution that the Burg method did

for noiseless data.

august 2014 by hallucigenia

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