scholarly journals Sample size issues in time series regressions of counts on environmental exposures

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ben G. Armstrong ◽  
Antonio Gasparrini ◽  
Aurelio Tobias ◽  
Francesco Sera
Keyword(s):  
Time Series ◽  
Sample Size ◽  
Environmental Exposures

scholarly journals An ARFIMA-based model for daily precipitation amounts with direct access to fluctuations

2020 ◽  
Vol 34 (10) ◽  
pp. 1487-1505
Author(s):  
Katja Polotzek ◽  
Holger Kantz
Keyword(s):  
Time Series ◽  
Sample Size ◽  
Confidence Intervals ◽  
Daily Precipitation ◽  
Rainfall Simulation ◽  
Direct Access ◽  
Effective Sample Size ◽  
Sample Mean ◽  
Daily Data ◽  
Synthetic Time Series

Abstract Correlations in models for daily precipitation are often generated by elaborate numerics that employ a high number of hidden parameters. We propose a parsimonious and parametric stochastic model for European mid-latitude daily precipitation amounts with focus on the influence of correlations on the statistics. Our method is meta-Gaussian by applying a truncated-Gaussian-power (tGp) transformation to a Gaussian ARFIMA model. The speciality of this approach is that ARFIMA(1, d, 0) processes provide synthetic time series with long- (LRC), meaning the sum of all autocorrelations is infinite, and short-range (SRC) correlations by only one parameter each. Our model requires the fit of only five parameters overall that have a clear interpretation. For model time series of finite length we deduce an effective sample size for the sample mean, whose variance is increased due to correlations. For example the statistical uncertainty of the mean daily amount of 103 years of daily records at the Fichtelberg mountain in Germany equals the one of about 14 years of independent daily data. Our effective sample size approach also yields theoretical confidence intervals for annual total amounts and allows for proper model validation in terms of the empirical mean and fluctuations of annual totals. We evaluate probability plots for the daily amounts, confidence intervals based on the effective sample size for the daily mean and annual totals, and the Mahalanobis distance for the annual maxima distribution. For reproducing annual maxima the way of fitting the marginal distribution is more crucial than the presence of correlations, which is the other way round for annual totals. Our alternative to rainfall simulation proves capable of modeling daily precipitation amounts as the statistics of a random selection of 20 data sets is well reproduced.

scholarly journals Time series models of environmental exposures: Good predictions or good understanding

2017 ◽  
Vol 154 ◽  
pp. 222-225 ◽  
Author(s):  
Adrian G. Barnett ◽  
Dimity Stephen ◽  
Cunrui Huang ◽  
Martin Wolkewitz
Keyword(s):  
Time Series ◽  
Environmental Exposures ◽  
Time Series Models

scholarly journals Measuring change in biological communities: multivariate analysis approaches for temporal datasets with low sample size

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11096
Author(s):  
Hannah L. Buckley ◽  
Nicola J. Day ◽  
Bradley S. Case ◽  
Gavin Lear
Keyword(s):  
Time Series ◽  
Sample Size ◽  
Statistical Power ◽  
Structural Changes ◽  
Linear Models ◽  
Environmental Changes ◽  
Short Time Series ◽  
Biological Communities ◽  
Extinction Events ◽  
Short Time

Effective and robust ways to describe, quantify, analyse, and test for change in the structure of biological communities over time are essential if ecological research is to contribute substantively towards understanding and managing responses to ongoing environmental changes. Structural changes reflect population dynamics, changes in biomass and relative abundances of taxa, and colonisation and extinction events observed in samples collected through time. Most previous studies of temporal changes in the multivariate datasets that characterise biological communities are based on short time series that are not amenable to data-hungry methods such as multivariate generalised linear models. Here, we present a roadmap for the analysis of temporal change in short-time-series, multivariate, ecological datasets. We discuss appropriate methods and important considerations for using them such as sample size, assumptions, and statistical power. We illustrate these methods with four case-studies analysed using the R data analysis environment.

Pemodelan dan Peramalan Deret Waktu Studi Kasus: Suhu Permukaan Laut di Selatan Jawa Timur

2017 ◽  
Vol 1 (2) ◽  
pp. 187-199
Author(s):  
Hutomo Atman Maulana ◽  
Muliah Muliah ◽  
Maria Zefaya Sampe ◽  
Farrah Hanifah
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sample Size ◽  
Time Series Data ◽  
Temperature Time Series ◽  
Sea Surface ◽  
Series Data ◽  
Forecast Time ◽  
Temperature Time

The sea surface temperature is one of the important components that can determine the potential of the sea. This research aims to model and forecast time series data of sea surface temperature by using a Box-Jenkins method. Data in this research are the sea surface temperatures in the South of East Java (January 1983-December 2013) with sample size of 372. 360 data will be used for modeling which is from January 1983 to December 2012, and data in 2013 will be used for forecasting. Based on the results of analysis time series, the appropriate models is SARIMA(1,0,0) (1,0,1)12 where can be written as Yt = 0,010039 + 0,734220Yt−1 + 0,014893Yt−12 − (0,734220)(0,014893)Yt−13 + 0,940726et−12 with  MSE of 0.07888096.Keywords: Sea surface temperature, time series, Box-Jenkins method

Sample size issues in time series regression studies

2016 ◽  
Vol 2016 (1) ◽  
Author(s):  
Benedict Armstrong* ◽  
Francesco Sera ◽  
Antonio Gasparrini
Keyword(s):  
Time Series ◽  
Sample Size ◽  
Time Series Regression
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