Results 61 to 70 of about 13,308 (240)
Bayesian Model Averaging in Causal Instrumental Variable Models
Journal of Applied Econometrics, EarlyView.ABSTRACT Instrumental variables are a popular tool to infer causal effects under unobserved confounding, but choosing suitable instruments is challenging in practice. We propose gIVBMA, a Bayesian model averaging procedure that addresses this challenge by averaging across different sets of instrumental variables and covariates in a structural equation ...
Gregor Steiner, Mark Steel
wiley +1 more source
Confidence Intervals of Risk Ratios for the Augmented Logistic Regression with Pseudo-Observations
StatsThe augmented logistic regression proposed by Diaz-Quijano directly provides risk ratios with an augmented dataset with the pseudo-observations. However, the standard errors of regression coefficients cannot be accurately estimated using either the ...
Hiroyuki Shiiba, Hisashi Noma
doaj +1 more source
Isolating transdiagnostic effects reveals specific genetic profiles in psychiatric disorders
JCPP Advances, EarlyView.Abstract Background Evidence indicates substantial genetic overlap between psychiatric diagnoses. Accounting for these transdiagnostic effects can sharpen research on disorder‐specific genetic architecture and patterns of comorbidity. Methods We applied genomic structural equation modeling to genome‐wide association study summary statistics from 11 ...
Engin Keser +6 more
wiley +1 more source
Revista de Biología Tropical, 2001 Los parámetros de crecimiento del pulpo (Octopus maya) de las costas de Yucatán (México) fueron estimados en siete años comprendidos entre 1983-1987, 1989 y 1992. A tales efectos se realizó un análisis de frecuencia de tallas de 19 251 pulpos muestreados
Marta Rocío Nepita Villanueva +1 more
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Jackknife Bias Reduction in the Presence of a Near-Unit Root
Econometrics, 2018 This paper considers the specification and performance of jackknife estimators of the autoregressive coefficient in a model with a near-unit root.
Marcus J. Chambers, Maria Kyriacou
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Uncertainty Quantification for Cardiac Diffusion Tensor Imaging Without Additional Datasets
Magnetic Resonance in Medicine, EarlyView.ABSTRACT Purpose Cardiac diffusion tensor imaging (cDTI) is subject to physiological noise, thermal noise, and signal corruption, which cause errors in diffusion measures. While a larger dataset can be decimated to investigate the general precision of measures from fitting smaller datasets, uncertainty quantification (UQ) methods for fitting entire ...
Sam Coveney +7 more
wiley +1 more source
Efficient Calculation of Jackknife Confidence Intervals for Rank Statistics
Journal of Statistical Software, 2005 An algorithm is presented for calculating concordance-discordance totals in a time of order N log N , where N is the number of observations, using a balanced binary search tree.
Roger Newson
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Nordic Journal of Botany, EarlyView.Cybianthus anthuriophyllus (Primulaceae) was previously known from eastern Ecuador and northern Peru. Here we document the first confirmed occurrences of this species in Colombia and develop a distribution model based on collections from the Andean–Amazonian foothills of Caquetá, Cauca, and Putumayo departments, in order to clarify its conservation ...
David Hoyos +4 more
wiley +1 more source
Justice in coexistence: Pastoralism and large carnivores on the Qinghai‐Tibet Plateau
People and Nature, EarlyView.Abstract The conflict between livestock husbandry and large carnivore conservation presents significant challenges in grassland ecosystems worldwide. Achieving sustainable coexistence among herders, livestock and large carnivores requires reconciling diverse perspectives and interests through equitable, inclusive and fair processes that address both ...
Yufang Gao, Yue Yu
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Intervalos de Confianza Jackknife para Cuantiles en Muestreo con Probabilidades Desiguales
TecnoLógicas, 2010 We consider the estimation of quantiles in finite population susing the jackknife technique. We use a jackknife variance estimator for unequal probability sampling that works better than the classical jackknife estimator.
Mario J. Pacheco, Hugo A. Brango
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