kdensity: An R package for kernel density estimation with parametric starts and asymmetric kernels

Moss, Jonas; Tveten, Martin

doi:10.21105/joss.01566

Cited by 20 publications

(14 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is the average of the individual indices

, with k being the number of previously conducted studies. We implemented this procedure as a kernel density estimation with a Gaussian kernel and a non-parametric bandwidth selector ( Moss and Tveten, 2019 ), so that the number of bins does not have to be chosen a priori.…”

Section: Conceptual Backgroundmentioning

confidence: 99%

A Similarity-Weighted Informative Prior Distribution for Bayesian Multiple Regression Models

König

2021

Front. Psychol.

View full text Add to dashboard Cite

Specifying accurate informative prior distributions is a question of carefully selecting studies that comprise the body of comparable background knowledge. Psychological research, however, consists of studies that are being conducted under different circumstances, with different samples and varying instruments. Thus, results of previous studies are heterogeneous, and not all available results can and should contribute equally to an informative prior distribution. This implies a necessary weighting of background information based on the similarity of the previous studies to the focal study at hand. Current approaches to account for heterogeneity by weighting informative prior distributions, such as the power prior and the meta-analytic predictive prior are either not easily accessible or incomplete. To complicate matters further, in the context of Bayesian multiple regression models there are no methods available for quantifying the similarity of a given body of background knowledge to the focal study at hand. Consequently, the purpose of this study is threefold. We first present a novel method to combine the aforementioned sources of heterogeneity in the similarity measure ω. This method is based on a combination of a propensity-score approach to assess the similarity of samples with random- and mixed-effects meta-analytic models to quantify the heterogeneity in outcomes and study characteristics. Second, we show how to use the similarity measure ωas a weight for informative prior distributions for the substantial parameters (regression coefficients) in Bayesian multiple regression models. Third, we investigate the performance and the behavior of the similarity-weighted informative prior distribution in a comprehensive simulation study, where it is compared to the normalized power prior and the meta-analytic predictive prior. The similarity measure ω and the similarity-weighted informative prior distribution as the primary results of this study provide applied researchers with means to specify accurate informative prior distributions.

show abstract

“…It is the average of the individual indices

Section: Conceptual Backgroundmentioning

confidence: 99%

A Similarity-Weighted Informative Prior Distribution for Bayesian Multiple Regression Models

König

2021

Front. Psychol.

View full text Add to dashboard Cite

show abstract

“…The H‐BK approach has been implemented by means of the kdensity()function included in the kdensity package (Moss and Tveten, ). The C‐BK method has been implemented through the chen99Kernel()function contained in the bde package (Santafe et al ., ).…”

Section: Real Data Analysismentioning

confidence: 99%

Modelling the Loss Given Default Distribution via a Family of Zero-and-one Inflated Mixture Models

Tomarchio

Punzo

2019

Journal of the Royal Statistical Society Series A: Statistics in Society

View full text Add to dashboard Cite

Summary The empirical distribution of the loss given default (LGD) has support [0,1], contains an excess of 0s and 1s, and is often multimodal on (0,1). Though some parametric models have been used in the credit risk literature to model the LGD distribution, these peculiarities call for more flexible approaches. Thus, we introduce a zero‐and‐one inflated mixture where a three‐level multinomial model is considered for the membership of the LGD values to the sets {0}, (0,1) and {1}, whereas a finite mixture of distributions is used on (0,1). To allow for more flexible shapes on (0,1), we consider component distributions already defined on (0,1) and distributions on (−∞,∞) mapped on (0,1) via the inverse logit transformation. This yields a family of 13 zero‐and‐one inflated mixture models. They are applied to two data sets of LGDs on loans: one from a European Bank and the other from the Bank of Italy. The best performers in our family, selected via classical information criteria, are then compared with several well‐established semiparameteric or non‐parametric approaches via a convenient simulation‐based procedure.

show abstract

“…= = 0, in our case). We 588 approximated the posterior probability at this collapse-point by smoothing our posterior 589 samples for = with a beta-kernel density estimator(Moss and Tveten, 2019). We interpret 590 the strength of significance for Bayes factors as proposed byJeffreys (1961), requiring at 591 least 'Substantial' support (BF > 3) to select the more complex model ( = > 0).592 593 RESULTS 594 support for the = > 0 model, while ∼32% of those replicates qualified as Substantial 601 support or greater.…”

mentioning

confidence: 99%

Modeling phylogenetic biome shifts on a planet with a past

Landis

Edwards

Donoghue

2019

Preprint

View full text Add to dashboard Cite

The spatial distribution of biomes has changed considerably over deep time, so the geographical opportunity for an evolutionary lineage to shift into a new biome may depend on how the availability and connectivity of biomes has varied temporally. To better understand how lineages shift between biomes in space and time, we developed a phylogenetic biome shift model in which each lineage shifts between biomes and disperses between regions at rates that depend on the lineage’s biome affinity and location relative to the spatiotemporal distribution of biomes at any given time. To study the behavior of the biome shift model in an empirical setting, we developed a literature-based representation of paleobiome structure for three mesic forest biomes, six regions, and eight time strata, ranging from the Late Cretaceous (100 Ma) through the present. We then fitted the model to a time-calibrated phylogeny of 119 Viburnum species to compare how the results responded to various realistic or unrealistic assumptions about paleobiome structure.Ancestral biome estimates that account for paleobiome dynamics reconstructed a warm temperate (or tropical) origin of Viburnum, which is consistent with previous fossil-based estimates of ancestral biomes. Imposing unrealistic paleobiome distributions led to ancestral biome estimates that eliminated support for tropical origins, and instead inflated support for cold temperate ancestry throughout the warmer Paleocene and Eocene. The biome shift model we describe is applicable to the study of evolutionary systems beyond Viburnum, and the core mechanisms of our model are extensible to the design of richer phylogenetic models of historical biogeography and/or lineage diversification. We conclude that biome shift models that account for dynamic geographical opportunities are important for inferring ancestral biomes that are compatible with our understanding of Earth history.

show abstract

kdensity: An R package for kernel density estimation with parametric starts and asymmetric kernels

Cited by 20 publications

References 8 publications

A Similarity-Weighted Informative Prior Distribution for Bayesian Multiple Regression Models

A Similarity-Weighted Informative Prior Distribution for Bayesian Multiple Regression Models

Modelling the Loss Given Default Distribution via a Family of Zero-and-one Inflated Mixture Models

Modeling phylogenetic biome shifts on a planet with a past

Contact Info

Product

Resources

About