Second-order smoothing of spatial point patterns with small sample sizes: a family of kernels

Williams and Bargh (2008) reported that holding a hot cup of coffee caused participants to judge a person’s personality as warmer, and that holding a therapeutic heat pad caused participants to choose rewards for other people rather than for themselves. These experiments featured large effects (r = .28 and .31), small sample sizes (41 and 53 participants), and barely statistically significant results. We attempted to replicate both experiments in field settings with more than triple the sample sizes (128 and 177) and double-blind procedures, but found near-zero effects (r = –.03 and .02). In both cases, Bayesian analyses suggest there is substantially more evidence for the null hypothesis of no effect than for the original physical warmth priming hypothesis.

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G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes

Scientific Reports ◽

10.1038/s41598-021-81110-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Florent Le Borgne ◽

Arthur Chatton ◽

Maxime Léger ◽

Rémi Lenain ◽

Yohann Foucher

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Statistical Power ◽

Small Sample ◽

Causal Effects ◽

Small Samples ◽

Support Vector ◽

Sample Sizes ◽

Super Learner ◽

Small Sample Sizes

AbstractIn clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. G-computation is an alternative because of its high statistical power. Machine learning is also increasingly used because of its possible robustness to model misspecification. In this paper, we aimed to propose an approach that combines machine learning and G-computation when both the outcome and the exposure status are binary and is able to deal with small samples. We evaluated the performances of several methods, including penalized logistic regressions, a neural network, a support vector machine, boosted classification and regression trees, and a super learner through simulations. We proposed six different scenarios characterised by various sample sizes, numbers of covariates and relationships between covariates, exposure statuses, and outcomes. We have also illustrated the application of these methods, in which they were used to estimate the efficacy of barbiturates prescribed during the first 24 h of an episode of intracranial hypertension. In the context of GC, for estimating the individual outcome probabilities in two counterfactual worlds, we reported that the super learner tended to outperform the other approaches in terms of both bias and variance, especially for small sample sizes. The support vector machine performed well, but its mean bias was slightly higher than that of the super learner. In the investigated scenarios, G-computation associated with the super learner was a performant method for drawing causal inferences, even from small sample sizes.

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Filtering spatial point patterns using kernel densities

Spatial Statistics ◽

10.1016/j.spasta.2020.100487 ◽

2021 ◽

Vol 41 ◽

pp. 100487

Author(s):

Brian E. Vestal ◽

Nichole E. Carlson ◽

Debashis Ghosh

Keyword(s):

Point Patterns ◽

Spatial Point ◽

Spatial Point Patterns

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Envelope tests for spatial point patterns with and without simulation

Ecosphere ◽

10.1002/ecs2.1365 ◽

2016 ◽

Vol 7 (6) ◽

Cited By ~ 22

Author(s):

Thorsten Wiegand ◽

Pavel Grabarnik ◽

Dietrich Stoyan

Keyword(s):

Point Patterns ◽

Spatial Point ◽

Spatial Point Patterns

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Analysis of Variance for Replicated Spatial Point Patterns in Clinical Neuroanatomy

Journal of the American Statistical Association ◽

10.1080/01621459.1991.10475087 ◽

1991 ◽

Vol 86 (415) ◽

pp. 618-625 ◽

Cited By ~ 65

Author(s):

Peter J. Diggle ◽

Nicholas Lange ◽

Francine M. Beneš

Keyword(s):

Analysis Of Variance ◽

Point Patterns ◽

Spatial Point ◽

Spatial Point Patterns

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