19,142 research outputs found
Prediction of survival probabilities with Bayesian Decision Trees
Practitioners use Trauma and Injury Severity Score (TRISS) models for predicting the survival probability of an injured patient. The accuracy of TRISS predictions is acceptable for patients with up to three typical injuries, but unacceptable for patients with a larger number of injuries or with atypical injuries. Based on a regression model, the TRISS methodology does not provide the predictive density required for accurate assessment of risk. Moreover, the regression model is difficult to interpret. We therefore consider Bayesian inference for estimating the predictive distribution of survival. The inference is based on decision tree models which recursively split data along explanatory variables, and so practitioners can understand these models. We propose the Bayesian method for estimating the predictive density and show that it outperforms the TRISS method in terms of both goodness-of-fit and classification accuracy. The developed method has been made available for evaluation purposes as a stand-alone application
CVXR: An R Package for Disciplined Convex Optimization
CVXR is an R package that provides an object-oriented modeling language for
convex optimization, similar to CVX, CVXPY, YALMIP, and Convex.jl. It allows
the user to formulate convex optimization problems in a natural mathematical
syntax rather than the restrictive form required by most solvers. The user
specifies an objective and set of constraints by combining constants,
variables, and parameters using a library of functions with known mathematical
properties. CVXR then applies signed disciplined convex programming (DCP) to
verify the problem's convexity. Once verified, the problem is converted into
standard conic form using graph implementations and passed to a cone solver
such as ECOS or SCS. We demonstrate CVXR's modeling framework with several
applications.Comment: 34 pages, 9 figure
Efficient posterior sampling for high-dimensional imbalanced logistic regression
High-dimensional data are routinely collected in many areas. We are
particularly interested in Bayesian classification models in which one or more
variables are imbalanced. Current Markov chain Monte Carlo algorithms for
posterior computation are inefficient as and/or increase due to
worsening time per step and mixing rates. One strategy is to use a
gradient-based sampler to improve mixing while using data sub-samples to reduce
per-step computational complexity. However, usual sub-sampling breaks down when
applied to imbalanced data. Instead, we generalize piece-wise deterministic
Markov chain Monte Carlo algorithms to include importance-weighted and
mini-batch sub-sampling. These approaches maintain the correct stationary
distribution with arbitrarily small sub-samples, and substantially outperform
current competitors. We provide theoretical support and illustrate gains in
simulated and real data applications.Comment: 4 figure
Communication Lower Bounds for Statistical Estimation Problems via a Distributed Data Processing Inequality
We study the tradeoff between the statistical error and communication cost of
distributed statistical estimation problems in high dimensions. In the
distributed sparse Gaussian mean estimation problem, each of the machines
receives data points from a -dimensional Gaussian distribution with
unknown mean which is promised to be -sparse. The machines
communicate by message passing and aim to estimate the mean . We
provide a tight (up to logarithmic factors) tradeoff between the estimation
error and the number of bits communicated between the machines. This directly
leads to a lower bound for the distributed \textit{sparse linear regression}
problem: to achieve the statistical minimax error, the total communication is
at least , where is the number of observations that
each machine receives and is the ambient dimension. These lower results
improve upon [Sha14,SD'14] by allowing multi-round iterative communication
model. We also give the first optimal simultaneous protocol in the dense case
for mean estimation.
As our main technique, we prove a \textit{distributed data processing
inequality}, as a generalization of usual data processing inequalities, which
might be of independent interest and useful for other problems.Comment: To appear at STOC 2016. Fixed typos in theorem 4.5 and incorporated
reviewers' suggestion
Implicit Langevin Algorithms for Sampling From Log-concave Densities
For sampling from a log-concave density, we study implicit integrators
resulting from -method discretization of the overdamped Langevin
diffusion stochastic differential equation. Theoretical and algorithmic
properties of the resulting sampling methods for and a
range of step sizes are established. Our results generalize and extend prior
works in several directions. In particular, for , we prove
geometric ergodicity and stability of the resulting methods for all step sizes.
We show that obtaining subsequent samples amounts to solving a strongly-convex
optimization problem, which is readily achievable using one of numerous
existing methods. Numerical examples supporting our theoretical analysis are
also presented
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