Model selection ≈ Model selection
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mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models Open
Finite mixture models are being used increasingly to model a wide variety of random phenomena for clustering, classification and density estimation. mclust is a powerful and popular package which allows modelling of data as a Gaussian fini…
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A brief introduction to mixed effects modelling and multi-model inference in ecology Open
The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex …
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SMS: Smart Model Selection in PhyML Open
Model selection using likelihood-based criteria (e.g., AIC) is one of the first steps in phylogenetic analysis. One must select both a substitution matrix and a model for rates across sites. A simple method is to test all combinations and …
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Variable selection – A review and recommendations for the practicing statistician Open
Statistical models support medical research by facilitating individualized outcome prognostication conditional on independent variables or by estimating effects of risk factors adjusted for covariates. Theory of statistical models is well‐…
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Analyzing mixing systems using a new generation of Bayesian tracer mixing models Open
The ongoing evolution of tracer mixing models has resulted in a confusing array of software tools that differ in terms of data inputs, model assumptions, and associated analytic products. Here we introduce MixSIAR, an inclusive, rich, and …
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Variable selection strategies and its importance in clinical prediction modelling Open
Clinical prediction models are used frequently in clinical practice to identify patients who are at risk of developing an adverse outcome so that preventive measures can be initiated. A prediction model can be developed in a number of ways…
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kuenm: an R package for detailed development of ecological niche models using Maxent Open
Background Ecological niche modeling is a set of analytical tools with applications in diverse disciplines, yet creating these models rigorously is now a challenging task. The calibration phase of these models is critical, but despite rece…
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Estimation of Panel Vector Autoregression in Stata Open
Panel vector autoregression (VAR) models have been increasingly used in applied research. While programs specifically designed to fit time-series VAR models are often included as standard features in most statistical packages, panel VAR mo…
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Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches † Open
Background: With the development of smart grids, accurate electric load forecasting has become increasingly important as it can help power companies in better load scheduling and reduce excessive electricity production. However, developing…
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Why is it difficult to accurately predict the COVID-19 epidemic? Open
Since the COVID-19 outbreak in Wuhan City in December of 2019, numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported. These model predictions have shown a wide range of variations. In our…
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ENMeval 2.0: Redesigned for customizable and reproducible modeling of species’ niches and distributions Open
Quantitative evaluations to optimize complexity have become standard for avoiding overfitting of ecological niche models (ENMs) that estimate species’ potential geographic distributions. ENMeval was the first R package to make such evaluat…
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A solution to minimum sample size for regressions Open
Regressions and meta-regressions are widely used to estimate patterns and effect sizes in various disciplines. However, many biological and medical analyses use relatively low sample size (N), contributing to concerns on reproducibility. W…
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<span>block</span> <span>CV</span> : An <span>r</span> package for generating spatially or environmentally separated folds for <i>k</i> ‐fold cross‐validation of species distribution models Open
When applied to structured data, conventional random cross‐validation techniques can lead to underestimation of prediction error, and may result in inappropriate model selection. We present the r package block CV , a new toolbox for cross‐…
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Variable selection with stepwise and best subset approaches Open
While purposeful selection is performed partly by software and partly by hand, the stepwise and best subset approaches are automatically performed by software. Two R functions stepAIC() and bestglm() are well designed for stepwise and best…
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Smoothing Parameter and Model Selection for General Smooth Models Open
This article discusses a general framework for smoothing parameter estimation for models with regular likelihoods constructed in terms of unknown smooth functions of covariates. Gaussian random effects and parametric terms may also be pres…
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Model building strategy for logistic regression: purposeful selection Open
Logistic regression is one of the most commonly used models to account for confounders in medical literature. The article introduces how to perform purposeful selection model building strategy with R. I stress on the use of likelihood rati…
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Forecasting of demand using ARIMA model Open
The work presented in this article constitutes a contribution to modeling and forecasting the demand in a food company, by using time series approach. Our work demonstrates how the historical demand data could be utilized to forecast futur…
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A practical guide to selecting models for exploration, inference, and prediction in ecology Open
Selecting among competing statistical models is a core challenge in science. However, the many possible approaches and techniques for model selection, and the conflicting recommendations for their use, can be confusing. We contend that muc…
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Model selection may not be a mandatory step for phylogeny reconstruction Open
Determining the most suitable model for phylogeny reconstruction constitutes a fundamental step in numerous evolutionary studies. Over the years, various criteria for model selection have been proposed, leading to debate over which criteri…
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Adjusting for Publication Bias in Meta-Analysis Open
We review and evaluate selection methods, a prominent class of techniques first proposed by Hedges (1984) that assess and adjust for publication bias in meta-analysis, via an extensive simulation study. Our simulation covers both restricti…
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An introduction to Bayesian inference in gravitational-wave astronomy: Parameter estimation, model selection, and hierarchical models Open
This is an introduction to Bayesian inference with a focus on hierarchical models and hyper-parameters. We write primarily for an audience of Bayesian novices, but we hope to provide useful insights for seasoned veterans as well. Examples …
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Model averaging in ecology: a review of Bayesian, information‐theoretic, and tactical approaches for predictive inference Open
In ecology, the true causal structure for a given problem is often not known, and several plausible models and thus model predictions exist. It has been claimed that using weighted averages of these models can reduce prediction error, as w…
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Model Selection Techniques: An Overview Open
In the era of big data, analysts usually explore various statistical models or machine-learning methods for observed data to facilitate scientific discoveries or gain predictive power. Whatever data and fitting procedures are employed, a c…
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Half-Normal Plots and Overdispersed Models in <i>R</i>: The <b>hnp</b> Package Open
Count and proportion data may present overdispersion, i.e., greater variability than expected by the Poisson and binomial models, respectively. Different extended generalized linear models that allow for overdispersion may be used to analy…
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Scalable and Generalizable Social Bot Detection through Data Selection Open
Efficient and reliable social bot classification is crucial for detecting information manipulation on social media. Despite rapid development, state-of-the-art bot detection models still face generalization and scalability challenges, whic…
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A Conceptual Introduction to Bayesian Model Averaging Open
Many statistical scenarios initially involve several candidate models that describe the data-generating process. Analysis often proceeds by first selecting the best model according to some criterion and then learning about the parameters o…
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The relative performance of AIC, AIC<sub>C</sub>and BIC in the presence of unobserved heterogeneity Open
Summary Model selection is difficult. Even in the apparently straightforward case of choosing between standard linear regression models, there does not yet appear to be consensus in the statistical ecology literature as to the right approa…
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Cross validation for model selection: A review with examples from ecology Open
Specifying, assessing, and selecting among candidate statistical models is fundamental to ecological research. Commonly used approaches to model selection are based on predictive scores and include information criteria such as Akaike's inf…
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A review of deep learning applications for genomic selection Open
Background Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In rec…
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Legacy, Rather Than Adequacy, Drives the Selection of Hydrological Models Open
The findings of hydrological modeling studies depend on which model was used. Although hydrological model selection is a crucial step, experience suggests that hydrologists tend to stick to the model they have experience with, and rarely s…