Tagged Questions

Autocorrelation is the correlation of a series of data with itself at some lag. This is an important topic particularly in the analysis of time-series data.

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How to analyze longitudinal count data: accounting for temporal autocorrelation in GLMM?

Hello statistical gurus and R programming wizards, I am interested in modeling animal captures as a function of environmental conditions and day of the year. As part of another study, I have counts ...
145 views

Stuck at analyzing large and complex data set

I've got an extremely large and complex dataset and getting frustrated with the analysis. In essence, my target question is a simple one. I am comparing insect flower visitation on >30 plant types. ...
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Inducing autocorrelation by fitting the wrong ARMA?

I am trying to fit an ARMA(p,q) model to the mean equation of my return series. The problem is, that the acf and pacf are pretty not usable, i.e. it is hard to find a good model to take account of the ...
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Estimating model error in $k$-nearest neighbours with strongly spatially autocorrelated training data

In the palaeoclimate world, palaeoecologists have used spatial training sets of say sea-surface temperture (SST) and related this to micro-organisms living at the locations where SST was measured. A ...
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Making new variable instead of correcting for temporal autocorrelation in a GLMM. Is it a valid alternative?

I am doing some forest disturbance research, in which the aim is to predict the probabilities of wind damage occurrence in forest stands of different site (altitude, slope steepness) and stand ...
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What is the best way to compare fluctuations of two signals?

I have some data acquired by an acoustic sensor with 1 Hz sampling rate. Due to some inevitable issues, I have some noise in my signal, saying 10% pollution. I'm looking for a reliable method for ...
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Issues with fitting a variogram

I am trying to fit a spherical variogram to some synthetic data using the code available at http://www.mathworks.com/matlabcentral/fileexchange/25948-variogramfit. However, I have some doubts. I ...
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If you run OLS regression on cross sectional data, should you test for autocorrelation in residuals?

I have a set of observations, independent of time. I am wondering whether I should run any autocorrelation tests? It seems to me that it makes no sense, since there's no time component in my data. ...
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Spatial autocorrelation of non-homogeneous point data in irregularly shaped sample plots

Let's assume we registered the position of all individuals of different plant species within 3 irregularly shaped sample plots that are very close the each other. As an example, I put 2 pictures with ...
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(Quantile regression) AR(1) variable in the design matrix

I'm not doing a pure QAR (quantile auto regression) but I do have a lagged dependent variable (AR(1)) as a predictor. I'm using the quantreg package in ...
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How to estimate measurement error on spatially autocorrelated gridded data when aggregating grid cells

I would like to estimate the measurement error when aggregating (via arithmetic mean) gridded spatial data. The goal is to come up with the mean elevation (or some other spatially continuous ...
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Characterizing a time-series using autocorrelation lag values

I am seeking to characterize time-series data (specifically parameters derived from sensor data) for 18 patients collected over 20 days using autocorrelation (see plot below of autocorrelation ...
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Temporal autocorrelation in perMANOVA?

I have a data set where samples are collected once per year for 15 years at a number of sites. I am worried that these data are temporally autocorrelated and was trying to figure out if I need to ...
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Convergence problems created by jittered coordinates

I am creating a mixed model and including a spatial correlation. My data points include lat long values although some are duplicated. I have two questions about dealing with these. To specify the ...
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Spatial autocorrelation — GLM, autocovariate, MEM (Moran's eigenvector mapping)

I am currently working on two marine species distribution modelling and also on their overlap distribution. For this I use a binomial logistic regression model (GLM) with response being, respectively, ...
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Confusion related to estimation of nugget

I am generating some simulated data from a multivariate gaussian distribution with a covariance matrix sigma. To add some noise, I added an identity matrix to the covariance matrix which depicts ...
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Estimating the autocorrelation function with some noisy observations

I am wondering how to estimate the actual correlation function when I have some noisy samples of some space. Lets say, I have a space and the locations in the space are variables following a ...
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HMC: How many dimensions is too many?

From what I have read Hamiltonian Monte Carlo is the "goto" MCMC method when your problem is high dimensional. Practically speaking, how many dimensions 10's, ...
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Correlated error term residual in logit regression what are my options?

I have estimated a model, with many interactions of both continuous and factor explanatory variables, which is to be used for prediction. My model has performed reasonably in out of sample testing. ...
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Confusion related to modelling of temporal correlation

I was a bit confused about the modelling of temporal correlation in a certain paper. Lets say, I have vector $\bf{x}$ of dimension m, and a time series $\bf{x_1},\bf{x_2},...\bf{x_N}$. Now I want to ...
564 views

Newey-West t-statistics

I have a time-series which is autocorrelated by construction, and might be heteroscedastic. I have calculated the sample mean of this time-series, and would like to calculate the t-statistic ...
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Calculating R-squared values from a semivariogram

I have some spatially autocorrelated vegetation data, and would like to know the how well tree size measured at one location can predict tree size in plots 100m away. I've made a semivariogram of ...
202 views

Lag Selection Modelling 'Pseudo' Panel Data

I have what I would call a pseudo panel, where my dependent variable varies over time and space (regional death counts), but my x variable of interest does not (national wage time series). Basically, ...
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Using autocorrelation plots to choose the number of inputs for a neural network predicting time series

A neural network applied to time series needs to have the number of input nodes defined. Each input is applied to a time point previous to the current point being predicted. If $D$ is the number of ...
669 views

Panel Data: In a fixed effects model, does auto-correlation introduce bias?

Given a panel of countries over time, a fixed effects estimator makes sense to control for country-specific effects. My intuition tells me that if the dependent variable is correlated with lags of the ...
274 views

Estimating noise correlation in augmented state vector Kalman filter

How do I estimate the noise correlation matrix (psi function) in the augmented state approach of Kalman filter? Can I do something like this: \text{noise}_2 = \psi \cdot ...
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Panel data: IV with different frequencies

I would like to estimate a standard logit (panel data): $\text{logit}(P(y_{i,t}=1))= \alpha + \beta_1 x^1_{i,t} + \beta_2 x^2_{i,t} +\epsilon_{i,t}$. The problem I am facing, however, is that $x^2$ ...
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Forecasting time-series ahead by multiple time horizons

Suppose that I have daily data on the population of a small village, given by $Y(t)$, as well as daily data on various factors that are relevant to the size of the population in the future, given by ...
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The ACF of a unit root process

If the correlogram of $Y_t$ displays slow decay (i.e., non-stationarity), is this indicative of a unit root? I reasoned that for the model $Y_t = Y_{t-1} + u_t$, the ACF is equal to 1 for all lags, ...
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test multicollinearity for multinomial logit regression

I'd like to create a multinomial logit regression and thus I should check multicollinearity and autocorrelation. All my variables are nominal scale with four categories. I found the perturb package in ...