2,982 research outputs found
Ambiguity, Information Quality and Asset Pricing
When ambiguity averse investors process news of uncertain quality, they act as if they take a worst-case assessment of quality. As a result, they react more strongly to bad news than to good news. They also dislike assets for which information quality is poor, especially when the underlying fundamentals are volatile. These effects induce skewness in asset returns and induce ambiguity premia that depend on idiosyncratic risk in fundamentals. Moreover, shocks to information quality can have persistent negative effects on prices even if fundamentals do not change. This helps to explain the reaction of markets to events like 9/11/2001.ambiguity, information quality, asset pricing, idiosyncratic risk, negatively skewed returns
Learning Under Ambiguity
This paper considers learning when the distinction between risk and ambiguity matters. It first describes thought experiments, dynamic variants of those provided by Ellsberg, that highlight a sense in which the Bayesian learning model is extreme - it models agents who are implausibly ambitious about what they can learn in complicated environments. The paper then provides a generalization of the Bayesian model that accommodates the intuitive choices in the thought experiments. In particular, the model allows decision-makersā confidence about the environment to change ā along with beliefs ā as they learn. A calibrated portfolio choice application shows how this property induces a trend towards more stock market participation and investment.ambiguity, learning, noisy signals, ambiguous signals, quality information, portfolio choice, portfolio diversification, Ellsberg Paradox
Learning Under Ambiguity
This paper considers learning when the distinction between risk and ambiguity (Knightian uncertainty) matters. Working within the framework of recursive multiple-priors utility, the paper formulates a counterpart of the Bayesian model of learning about an uncertain parameter from conditionally i.i.d. signals. Ambiguous signals capture responses to information that cannot be captured by noisy signals. They induce nonmonotonic changes in agent confidence and prevent ambiguity from vanishing in the limit. In a dynamic portfolio choice model, learning about ambiguous returns leads to endogenous stock market participation costs that depend on past market performance. Hedging of ambiguity provides a new reason why the investment horizon matters for portfolio choice.ambiguity, learning, noisy signals, ambiguous signals, quality information, portfolio choice, portfolio diversification, Ellsberg Paradox
Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation
While several papers have investigated computationally and statistically
efficient methods for learning Gaussian mixtures, precise minimax bounds for
their statistical performance as well as fundamental limits in high-dimensional
settings are not well-understood. In this paper, we provide precise information
theoretic bounds on the clustering accuracy and sample complexity of learning a
mixture of two isotropic Gaussians in high dimensions under small mean
separation. If there is a sparse subset of relevant dimensions that determine
the mean separation, then the sample complexity only depends on the number of
relevant dimensions and mean separation, and can be achieved by a simple
computationally efficient procedure. Our results provide the first step of a
theoretical basis for recent methods that combine feature selection and
clustering
Density-sensitive semisupervised inference
Semisupervised methods are techniques for using labeled data
together with unlabeled data
to make predictions. These methods invoke some assumptions that link the
marginal distribution of X to the regression function f(x). For example,
it is common to assume that f is very smooth over high density regions of
. Many of the methods are ad-hoc and have been shown to work in specific
examples but are lacking a theoretical foundation. We provide a minimax
framework for analyzing semisupervised methods. In particular, we study methods
based on metrics that are sensitive to the distribution . Our model
includes a parameter that controls the strength of the semisupervised
assumption. We then use the data to adapt to .Comment: Published in at http://dx.doi.org/10.1214/13-AOS1092 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Feature Selection For High-Dimensional Clustering
We present a nonparametric method for selecting informative features in
high-dimensional clustering problems. We start with a screening step that uses
a test for multimodality. Then we apply kernel density estimation and mode
clustering to the selected features. The output of the method consists of a
list of relevant features, and cluster assignments. We provide explicit bounds
on the error rate of the resulting clustering. In addition, we provide the
first error bounds on mode based clustering.Comment: 11 pages, 2 figure
New Spontaneous Model of Fibrodysplasia Ossificans Progressiva
We report the first known example of spontaneous, naturally occurring fibrodysplasia ossificans progressiva (FOP) in a mammal. The Southeast Asian mouse deer of the genus _Tragulus_ (Artiodactyla: Tragulidae) have an osseous sheath covering the lower back and upper thigh region consistent with the clinical definition of FOP. This heterotophic bone deposition is sex related apparently with a genetic basis - it only occurs in males and is lacking in females; it is present in all adults males, including both wild obtained and zoo bred animals. _Tragulus_ may offer the opportunity to examine many of the disease's most significant attributes experimentally
Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures
We consider the problem of clustering data points in high dimensions, i.e.
when the number of data points may be much smaller than the number of
dimensions. Specifically, we consider a Gaussian mixture model (GMM) with
non-spherical Gaussian components, where the clusters are distinguished by only
a few relevant dimensions. The method we propose is a combination of a recent
approach for learning parameters of a Gaussian mixture model and sparse linear
discriminant analysis (LDA). In addition to cluster assignments, the method
returns an estimate of the set of features relevant for clustering. Our results
indicate that the sample complexity of clustering depends on the sparsity of
the relevant feature set, while only scaling logarithmically with the ambient
dimension. Additionally, we require much milder assumptions than existing work
on clustering in high dimensions. In particular, we do not require spherical
clusters nor necessitate mean separation along relevant dimensions.Comment: 11 pages, 1 figur
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