@article{benjaminiControlFalseDiscovery2001,
  title = {The Control of the False Discovery Rate in Multiple Testing under Dependency},
  author = {Benjamini, Yoav and Yekutieli, Daniel},
  year = 2001,
  month = aug,
  journal = {The Annals of Statistics},
  volume = {29},
  number = {4},
  pages = {1165--1188},
  publisher = {Institute of Mathematical Statistics},
  issn = {0090-5364, 2168-8966},
  doi = {10.1214/aos/1013699998},
  abstract = {Benjamini and Hochberg suggest that the false discovery rate may be the appropriate error rate to control in many applied multiple testing problems. A simple procedure was given there as an FDR controlling procedure for independent test statistics and was shown to be much more powerful than comparable procedures which control the traditional familywise error rate. We prove that this same procedure also controls the false discovery rate when the test statistics have positive regression dependency on each of the test statistics corresponding to the true null hypotheses. This condition for positive dependency is general enough to cover many problems of practical interest, including the comparisons of many treatments with a single control, multivariate normal test statistics with positive correlation matrix and multivariate \$t\$. Furthermore, the test statistics may be discrete, and the tested hypotheses composite without posing special difficulties. For all other forms of dependency, a simple conservative modification of the procedure controls the false discovery rate. Thus the range of problems for which a procedure with proven FDR control can be offered is greatly increased.},
  keywords = {47N30,62G30,62J15,comparisons with control,discrete test statistics,FDR,Hochberg's procedure,MTP2 densities,Multiple comparisons procedures,multiple endpoints many-to-one comparisons,positive regression dependency,Simes'equality,unidimensional latent variables},
  file = {/home/nm/Zotero/storage/EG5F8PXP/Benjamini and Yekutieli - 2001 - The control of the false discovery rate in multipl.pdf}
}

@article{currieroCrossCorrelationMaps2005,
  title = {Cross Correlation Maps: A Tool for Visualizing and Modeling Time Lagged Associations},
  shorttitle = {Cross Correlation Maps},
  author = {Curriero, Frank C. and Shone, Scott M. and Glass, Gregory E.},
  year = 2005,
  journal = {Vector Borne and Zoonotic Diseases (Larchmont, N.Y.)},
  volume = {5},
  number = {3},
  pages = {267--275},
  issn = {1530-3667},
  doi = {10.1089/vbz.2005.5.267},
  abstract = {It has long been recognized that arthropod populations fluctuate with changes in environmental conditions and these changes occur at various spatial and temporal scales. Empirical studies that have explored associations between vector abundance and the environment often considered meterological events as leading indicators with their effects traditionally restricted to single points in time, such as precipitation 12 days prior to trapping. Field experience, however, suggests that the duration of these environmental effects on vectors often extends over a range or interval of time. Such a scenario is not directly interpretable from cross correlation plots routinely employed to visualize and identify time lag associations. Cross correlation maps are introduced as a way to generalize cross correlation plots and to visualize the effects of environmental conditions over intervals of time. This graphical method is flexible and can include different characterizations of environmental effects, as well as interactions among environmental variables. A time series of daily trapped female Ochlerotatus sollicitans mosquitos and leading meterologic conditions were used for demonstration. Associations shown in cross correlation maps were consistent with the arthropod biology and trapping efficacy and were also stronger than those identified at single time points using cross correlation plots. Poisson regression models for vector abundance built using meterological variables with both single and interval based leading time lags were compared. The approach based on the leading meterological events allowed to extend over time intervals reproduced the Oc. sollicitans daily population dynamics better than the traditional approach.},
  langid = {english},
  keywords = {Animals,Arthropod Vectors,Arthropods,Culicidae,Environment,Female,Male,Maryland,Models Biological,Population Dynamics,Population Surveillance,Regression Analysis,Seasons,Time Factors,Weather},
  file = {/home/nm/Zotero/storage/F49JCCAD/Curriero et al. - 2005 - Cross correlation maps a tool for visualizing and.pdf}
}

@article{vandepolIdentifyingBestClimatic2016,
  title = {Identifying the Best Climatic Predictors in Ecology and Evolution},
  author = {{van de Pol}, Martijn and Bailey, Liam D. and McLean, Nina and Rijsdijk, Laurie and Lawson, Callum R. and Brouwer, Lyanne},
  year = 2016,
  month = oct,
  journal = {Methods in Ecology and Evolution},
  volume = {7},
  number = {10},
  pages = {1246--1257},
  publisher = {John Wiley \& Sons, Ltd},
  doi = {10.1111/2041-210X.12590},
  abstract = {Summary Ecologists and many evolutionary biologists relate the variation in physiological, behavioural, life-history, demographic, population and community traits to the variation in weather, a key environmental driver. However, identifying which weather variables (e.g. rain, temperature, El Ni\~no index), over which time period (e.g. recent weather, spring or year-round weather) and in what ways (e.g. mean, threshold of temperature) they affect biological responses is by no means trivial, particularly when traits are expressed at different times among individuals. A literature review shows that a systematic approach for identifying weather signals is lacking and that the majority of studies select weather variables from a small number of competing hypotheses that are founded on unverified a priori assumptions. This is worrying because studies that investigate the nature of weather signals in detail suggest that signals can be complex. Using suboptimal or wrongly identified weather signals may lead to unreliable projections and management decisions. We propose a four-step approach that allows for more rigorous identification and quantification of weather signals (or any other predictor variable for which data are available at high temporal resolution), easily implementable with our new R package ?climwin?. We compare our approach with conventional approaches and provide worked examples. Although our more exploratory approach also has some drawbacks, such as the risk of overfitting and bias that our simulations show can occur at low sample and effect sizes, these issues can be addressed with the right knowledge and tools. By developing both the methods to fit critical weather windows to a wide range of biological responses and the tools to validate them and determine sample size requirements, our approach facilitates the exploration and quantification of the biological effects of weather in a rigorous, replicable and comparable way, while also providing a benchmark performance to compare other approaches to.},
  keywords = {bias,climate change,climate sensitivity,cross-validation,false positive,precision,Rpackage climwin,sample size,sliding window,weather},
  file = {/home/nm/Zotero/storage/VDVZVB25/van de Pol et al. - 2016 - Identifying the best climatic predictors in ecolog.pdf}
}
