<p>Plots show auto-correlation of model residuals to 400 lags (400 days) for A) GLM with no random effects or temporal correlation structure, B) GLMM with random site effect and no temporal correlation structure and C) GLMM with random site effect and continuous AR-1 correlation structure. Over-fit models include all explanatory variables and interactions under consideration. Final models include only significant predictor variables after model selection.</p
The sample autocorrelation function is defined by the mean lagged products (LPs) of random observati...
A new family of statistics is proposed to test for the presence of serial correlationin linear regre...
<p>A and B: autocorrelation function (ACF) of normalized residuals of the AR1-model fitted to data o...
<p>The x-axis gives the number of lags in weeks and, the y-axis, the value of the correlation coeffi...
<p>We calculated ACF and PACF for linearly de-trended aggregated SREAS CPUE data (a, b), residuals o...
<p>ACF=autocorrelation function, PACF=partial autocorrelation fuction. As their correlation values a...
<p>A and B show ACF and PACF of the training set. C and D show ACF and PACF of the training set afte...
Autocorrelation for a lag period of 20 days (x-axis) was first assessed without further adjustment (...
Almost all spikes fell within the estimated 95% uncertainty bounds at varying lags apart from the co...
<p>, are uncorrelated. If the error term is uncorrelated, it proves that there exists strong randomn...
<p>The AIC for each model was used to select the model which best described the error structure (sho...
<p>A and B: autocorrelation function (ACF) of normalized residuals of the base-model fitted to data ...
<p>Simulated data sets with known levels of temporal auto-correlation between residuals (spanning th...
Autocorrelation for a lag period of 20 days (x-axis) was first assessed without further adjustment (...
<p>The error autocorrelation was one of the evaluation parameters in the modelling process. As shown...
The sample autocorrelation function is defined by the mean lagged products (LPs) of random observati...
A new family of statistics is proposed to test for the presence of serial correlationin linear regre...
<p>A and B: autocorrelation function (ACF) of normalized residuals of the AR1-model fitted to data o...
<p>The x-axis gives the number of lags in weeks and, the y-axis, the value of the correlation coeffi...
<p>We calculated ACF and PACF for linearly de-trended aggregated SREAS CPUE data (a, b), residuals o...
<p>ACF=autocorrelation function, PACF=partial autocorrelation fuction. As their correlation values a...
<p>A and B show ACF and PACF of the training set. C and D show ACF and PACF of the training set afte...
Autocorrelation for a lag period of 20 days (x-axis) was first assessed without further adjustment (...
Almost all spikes fell within the estimated 95% uncertainty bounds at varying lags apart from the co...
<p>, are uncorrelated. If the error term is uncorrelated, it proves that there exists strong randomn...
<p>The AIC for each model was used to select the model which best described the error structure (sho...
<p>A and B: autocorrelation function (ACF) of normalized residuals of the base-model fitted to data ...
<p>Simulated data sets with known levels of temporal auto-correlation between residuals (spanning th...
Autocorrelation for a lag period of 20 days (x-axis) was first assessed without further adjustment (...
<p>The error autocorrelation was one of the evaluation parameters in the modelling process. As shown...
The sample autocorrelation function is defined by the mean lagged products (LPs) of random observati...
A new family of statistics is proposed to test for the presence of serial correlationin linear regre...
<p>A and B: autocorrelation function (ACF) of normalized residuals of the AR1-model fitted to data o...