Raquel Prado

Q: What Schools Are Affiliated With Raquel Prado

Raquel Prado is affiliated with the following schools: University of California, Berkeley, Duke University, University of California, Santa Cruz

Raquel Prado's AcademicInfluence.com Rankings

Raquel Prado

Mathematics

#7905

World Rank

#10740

Historical Rank

Statistics

#1029

World Rank

#1129

Historical Rank

mathematics Degrees

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Mathematics

Raquel Prado's Degrees

PhD Statistics University of California, Berkeley
Masters Statistics University of California, Berkeley

Why Is Raquel Prado Influential?

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According to Wikipedia, Raquel Prado is a Venezuelan Bayesian statistician. She is a professor of statistics in the Jack Baskin School of Engineering of the University of California, Santa Cruz, and has been elected president of the International Society for Bayesian Analysis for the 2019 term.

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Raquel Prado's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Time Series: Modeling, Computation, and Inference (2010) (269)
Evaluation and Comparison of EEG Traces: Latent Structure in Nonstationary Time Series (1999) (109)
EEG-Based Estimation of Mental Fatigue: Convergent Evidence for a Three-State Model (2007) (93)
New methods of time series analysis of non-stationary EEG data: eigenstructure decompositions of time varying autoregressions (1999) (63)
Bayesian Inference on Latent Structure in Time Series (1998) (48)
Multichannel electroencephalographic analyses via dynamic regression models with time‐varying lag–lead structure (2001) (40)
Bayesian time-varying autoregressions: Theory, methods and applications (2000) (37)
Time series modelling (2005) (35)
Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions (1997) (29)
Multivariate time series modeling and classification via hierarchical VAR mixtures (2006) (25)
Understanding the Impact of Stroke on Brain Motor Function: A Hierarchical Bayesian Approach (2016) (21)
Introduction to Design of Experiments (2008) (21)
Multi-channel EEG analyses via dynamic regression models with time-varying lag/lead structure (1999) (19)
Latent Structure In Non-Stationary Time Series (1998) (15)
The 2004 Venezuelan Presidential Recall Referendum: Discrepancies Between Two Exit Polls and Official Results (2011) (14)
Sequential estimation of mixtures of structured autoregressive models (2013) (13)
Dynamic Bayesian temporal modeling and forecasting of short-term wind measurements (2020) (12)
Exploring dependence between brain signals in a monkey during learning (2012) (12)
Bayesian Spectral Modeling for Multiple Time Series (2019) (12)
Bayesian Forecasting and Dynamic Models (2020) (12)
Joint Bayesian Estimation of Voxel Activation and Inter-regional Connectivity in fMRI Experiments (2020) (11)
Bayesian mixture modeling for spectral density estimation (2017) (11)
Spectral Decompositions of Multiple Time Series: A Bayesian Non-parametric Approach (2013) (11)
Sequential parameter learning and filtering in structured autoregressive state-space models (2013) (11)
A Bayesian Variable Selection Approach Yields Improved Detection of Brain Activation From Complex-Valued fMRI (2018) (10)
Time Series (2021) (10)
Assessing the Effect of Selection at the Amino Acid Level in Malaria Antigen Sequences Through Bayesian Generalized Linear Models (2008) (7)
Structured priors for multivariate time series (2006) (5)
Comparison of Bayesian , maximum likelihood and parsimony methods for detecting positive selection ( Research Article ) (5)
Characterizing molecular adaptation: a hierarchical approach to assess the selective influence of amino acid properties (2010) (5)
Bayesian Mixed Effect Sparse Tensor Response Regression Model with Joint Estimation of Activation and Connectivity (2019) (4)
Efficient Bayesian PARCOR approaches for dynamic modeling of multivariate time series (2019) (4)
Multistate models for mental fatigue (2018) (3)
Wavelet based Bayesian models for characterizing chagasic high-resolution ECG signals (2000) (3)
Parsimonious Bayesian sparse tensor regression using the Tucker decomposition (2022) (2)
Detecting Selection in DNA Sequences : Bayesian Modelling and Inference (2006) (2)
A Bayesian Model for Activation and Connectivity in Task-related fMRI Data (2019) (1)
Classification of high resolution ECG from chagasic patients with wavelet based Bayesian models (2001) (1)
Bayesian factor models in characterizing molecular adaptation (2013) (1)
Bayesian semiparametric regression models to characterize molecular evolution (2012) (1)
A Bayesian Variable Selection Approach Yields to Improved Brain Activation From Complex-Valued fMRI (2016) (1)
Bayesian semiparametric regression models to characterize molecular evolution (2012) (1)
Mixture models in time series (2021) (0)
Book Reviews (2010) (0)
Bayesian Methods for Phylogeny Independent Detection of Positively Selected Amino Acid Sites (2003) (0)
Dynamic linear models (2021) (0)
Bayesian tensor regression using the Tucker decomposition for sparse spatial modeling (2022) (0)
General classes of multivariate dynamic models (2021) (0)
Traditional time domain models (2021) (0)
Fast inference for time-varying quantiles via flexible dynamic models with application to the characterization of atmospheric rivers (2022) (0)
The frequency domain (2021) (0)
Dynamic Bayesian Models: Inference and Forecasting (2016) (0)
Bayesian spatiotemporal modeling on complex-valued fMRI signals via kernel convolutions. (2022) (0)
Hierarchical dynamic PARCOR models for analysis of multiple brain signals (2023) (0)
Fast Bayesian inference on spectral analysis of multivariate stationary time series (2022) (0)
Notation, definitions, and basic inference (2021) (0)
exdqlm: An R Package for Estimation and Analysis of Flexible Dynamic Quantile Linear Models (2021) (0)
Latent factor models (2021) (0)
Bayesian scalar-on-tensor regression using the Tucker decomposition for sparse spatial modeling ﬁnds promising results analyzing neuroimaging data (2022) (0)
General state-space models and sequential Monte Carlo methods (2021) (0)
Topics and examples in multiple time series (2021) (0)
Comparison and assessment of large-scale surface temperature in climate model simulations (2019) (0)
Spectral Decompositions of Multiple Time Series: A Bayesian Non-parametric Approach (2013) (0)
State-space TVAR models (2021) (0)
Vector AR and ARMA models (2021) (0)
Sequential parameter learning and filtering in structured autoregressive state-space models (2011) (0)

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Other Resources About Raquel Prado

en.wikipedia.org

What Schools Are Affiliated With Raquel Prado?

Raquel Prado is affiliated with the following schools:

Raquel Prado's Academic­Influence.com Rankings