ArticleComputational statistics & data analysis2011
Generalized Additive Models and Inflated Type I Error Rates of Smoother Significance Tests.
Article in Computational statistics & data analysis, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
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- Association of gait and cognition after surgery in patients with idiopathic normal pressure hydrocephalus.Scientific reports · 2023Article
- Normal variation in pelvic roll motion pattern during straight-line trot in hand in warmblood horses.Scientific reports · 2023Article
- PERSonality, Ehical, and PROfessional quality of life in Pediatric/Adult Intensive Nurses study: PERSEPRO PAIN study.PloS one · 2022Article
- Comparative Analysis of Total and Size-Fractionated ChlorophyllFrontiers in microbiology · 2022Article
- Environmental and Household-Based Spatial Risks for Tungiasis in an Endemic Area of Coastal Kenya.Tropical medicine and infectious disease · 2021Article
- Fluctuating reproductive rates in Hawaii's humpback whales,Royal Society open science · 2019Article
- Spatial analyses of ALS incidence in Denmark over three decades.Amyotrophic lateral sclerosis & frontotemporal degeneration · 2018Article
- Sexual partnership age pairings and risk of HIV acquisition in rural South Africa.AIDS (London, England) · 2017Article
- The Michaelis Complex of Arginine Kinase Samples the Transition State at a Frequency That Matches the Catalytic Rate.Journal of the American Chemical Society · 2017Article
- Geographic coverage of male circumcision in western Kenya.Medicine · 2017Article
- Fibrosis markers, hip fracture risk, and bone density in older adults.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2016Observational
- Spatial variation in mortality risk for hematological malignancies near a petrochemical refinery: A population-based case-control study.Environmental research · 2015Article
- Evaluation of the performance of smoothing functions in generalized additive models for spatial variation in disease.Cancer informatics · 2015Article
- Individual-level space-time analyses of emergency department data using generalized additive modeling.BMC public health · 2012Article
- Spatial-temporal analysis of non-Hodgkin lymphoma risk using multiple residential locations.Spatial and spatio-temporal epidemiology · 2012Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Generalized additive models (GAMs) have distinct advantages over generalized linear models as they allow investigators to make inferences about associations between outcomes and predictors without placing parametric restrictions on the associations. The variable of interest is often smoothed using a locally weighted regression (LOESS) and the optimal span (degree of smoothing) can be determined by minimizing the Akaike Information Criterion (AIC). A natural hypothesis when using GAMs is to test whether the smoothing term is necessary or if a simpler model would suffice. The statistic of interest is the difference in deviances between models including and excluding the smoothed term. As approximate chi-square tests of this hypothesis are known to be biased, permutation tests are a reasonable alternative. We compare the type I error rates of the chi-square test and of three permutation test methods using synthetic data generated under the null hypothesis. In each permutation method a distribution of differences in deviances is obtained from 999 permuted datasets and the null hypothesis is rejected if the observed statistic falls in the upper 5% of the distribution. One test is a conditional permutation test using the optimal span size for the observed data; this span size is held constant for all permutations. This test is shown to have an inflated type I error rate. Alternatively, the span size can be fixed a priori such that the span selection technique is not reliant on the observed data. This test is shown to be unbiased; however, the choice of span size is not clear. A third method is an unconditional permutation test where the optimal span size is selected for observed and permuted datasets. This test is unbiased though computationally intensive.
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Registered trials
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