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Ancestral Inference from Functional Data: Statistical Methods and Numerical Examples
comparative analysis Ornstein-Uhlenbeck process non-parametric Bayesian infer-ence functional phylogenetics ancestral reoncon-struction
2012/9/17
Many biological characteristics of evolutionary inter-est are not scalar variables but continuous functions.Here we use phylogenetic Gaussian process regres-sion to model the evolution of simulated fu...
Causal Inference on Time Series using Structural Equation Models
Causal Inference Time Series Structural Equation Models
2012/9/19
Causal inference uses observations to infer the causal structure of the data generating system.We study a class of functional models that we call Time Series Models with Independent Noise (TiMINo). Th...
Robust Bayesian inference of networks using Dirichlet t-distributions
Bayesian inference Dirichlet process graphical model Markov chain Monte Carlo t-distribution.
2012/9/18
Bayesian graphical modeling provides an appealing way to obtain uncertainty esti-mates when inferring network structures, and much recent progress has been made for Gaussian models. These models have ...
Riemannian statistics geometry: A counterpart approach of inference geometry
Geometrical Methods in Statistics
2011/7/19
Riemannian statistics geometry is proposed in this work as a counterpart approach of inference geometry.
KARMA: Kalman-based autoregressive moving average modeling and inference for formant and antiformant tracking
autoregressive moving average modeling inference for formant
2011/7/19
Vocal tract resonance characteristics in acoustic speech signals are classically tracked using frame-by-frame point estimates of formant frequencies followed by candidate selection and smoothing using...
Parametric inference and forecasting in continuously invertible volatility models
Invertibility volatility models parametric estimation
2011/7/6
We introduce the notion of continuously invertible volatility models that relies on some Lyapunov condition and some regularity condition.
Discussion of "Statistical Inference: The Big Picture" by R. E. Kass
Discussion Statistical Inference The Big Picture R. E. Kass
2011/7/6
Rob Kass presents a fascinating vision of a “post”-Bayes/frequentist-controversy world in which prac-tical utility of statistical models is the guiding prin-ciple for statistical inference.
Discussion of "Statistical Inference: The Big Picture" by R. E. Kass
Discussion Statistical Inference Big Picture R. E. Kass
2011/7/5
Kass states (page 5) that Figure 3 is not a good general description of statistical inference and that Figure 1 is more accurate. I completely agree. Kass states (page 5) that “It is important for stu...
Discussion of "Statistical Inference: The Big Picture" by R. E. Kass
Discussion Statistical Inference The Big Picture R. E. Kass
2011/7/5
In this piece, Rob Kass brings to bear his insights from a long career in both theoretical and applied statistics to reflect on the disconnect between what we teach and what we do.
Semiparametric inference in mixture models with predictive recursion marginal likelihood
Density estimation Dirichlet process mixture empirical Bayes filtering algorithm
2011/7/5
Predictive recursion is an accurate and computationally efficient algorithm for nonparametric estimation of mixing densities in mixture models. In semiparametric mixture models, however, the algorithm...
Statistical Inference: The Big Picture
Bayesian confidence frequentist statistical education
2011/7/5
Statistics has moved beyond the frequentist-Bayesian controversies of the past. Where does this leave our ability to interpret results?
We investigate statistical inference across time scales. We take as toy model the estimation of the intensity of a discretely observed compound Poisson process with symmetric Bernoulli jumps.
Performance of capacity inference methods under colored interference
capacity inference methods Performance colored interference
2011/6/21
In this paper, we address the problem of fast point-to-point channel capacity estimation in the situation where
the receiver undergoes unknown colored interference from multiple sources, whereas the ...
Asymptotic Inference of Autocovariances of Stationary Processes
Autocovariance blocks of blocks bootstrapping Box-Pierce test extreme value distribution moderate deviation normal comparison physical dependence measure short range dependence stationary process summability of cumulants
2011/6/17
The paper presents a systematic theory for asymptotic inference of autocovariances of
stationary processes.We consider nonparametric tests for serial correlations based on the maximum (or
L1) and th...
Fast approximate inference with INLA: the past, the present and the future
Latent Gaussian models Bayesian Integrated Nested Laplace Approximation
2011/6/17
Latent Gaussian models are an extremely popular, flexible class of models. Bayesian inference for
these models is, however, tricky and time consuming. Recently, Rue, Martino and Chopin introduced
th...