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Statistical hypothesis testing

About: Statistical hypothesis testing is a research topic. Over the lifetime, 19580 publications have been published within this topic receiving 1037815 citations. The topic is also known as: statistical hypothesis testing & confirmatory data analysis.


Papers
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Journal ArticleDOI
TL;DR: A simple graphical method is developed to test if spatial autocorrelation affects a training set, a Monte Carlo geostatistical simulation is developed as a null model to test the significance of transfer functions in Autocorrelated environments, and a cross-validation scheme is introduced that is more robust to autoc orrelation.

208 citations

Journal ArticleDOI
TL;DR: In this article, the authors propose inferential procedures for error correction models in structural form, with particular attention paid to the issues of exogeneity of conditioning variables and identification of cointegration parameters as well as short run parameters.

208 citations

Journal ArticleDOI
TL;DR: In this article, a detailed exposition of the statistical notion of stationarity and statistical testing of dynamic functional connectivity is presented. But, the authors do not consider the effect of the random sampling variability of static functional connectivity.

208 citations

Book ChapterDOI
29 Jun 2006
TL;DR: A family of statistical models for social network evolution over time is proposed, which represents an extension of Exponential Random Graph Models (ERGMs), and examples of their use for hypothesis testing and classification are given.
Abstract: We propose a family of statistical models for social network evolution over time, which represents an extension of Exponential Random Graph Models (ERGMs). Many of the methods for ERGMs are readily adapted for these models, including MCMC maximum likelihood estimation algorithms. We discuss models of this type and give examples, as well as a demonstration of their use for hypothesis testing and classification.

207 citations

Journal ArticleDOI
TL;DR: In this paper, a model selection approach based on the Akaike information criterion (AIC) was used in geographical data by modelling patterns of mammal species in South America represented in a grid system with 2° of resolution, as a function of five environmental explanatory variables.
Abstract: Aim Although parameter estimates are not as affected by spatial autocorrelation as Type I errors, the change from classical null hypothesis significance testing to model selection under an information theoretic approach does not completely avoid problems caused by spatial autocorrelation. Here we briefly review the model selection approach based on the Akaike information criterion (AIC) and present a new routine for Spatial Analysis in Macroecology (SAM) software that helps establishing minimum adequate models in the presence of spatial autocorrelation. Innovation We illustrate how a model selection approach based on the AIC can be used in geographical data by modelling patterns of mammal species in South America represented in a grid system (n = 383) with 2° of resolution, as a function of five environmental explanatory variables, performing an exhaustive search of minimum adequate models considering three regression methods: non-spatial ordinary least squares (OLS), spatial eigenvector mapping and the autoregressive (lagged-response) model. The models selected by spatial methods included a smaller number of explanatory variables than the one selected by OLS, and minimum adequate models contain different explanatory variables, although model averaging revealed a similar rank of explanatory variables. Main conclusions We stress that the AIC is sensitive to the presence of spatial autocorrelation, generating unstable and overfitted minimum adequate models to describe macroecological data based on non-spatial OLS regression. Alternative regression techniques provided different minimum adequate models and have different uncertainty levels. Despite this, the averaged model based on Akaike weights generates consistent and robust results across different methods and may be the best approach for understanding of macroecological patterns.

206 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023267
2022696
2021959
2020998
20191,033
2018943