160 research outputs found
Generalized extreme value regression for binary response data: An application to B2B electronic payments system adoption
In the information system research, a question of particular interest is to
interpret and to predict the probability of a firm to adopt a new technology
such that market promotions are targeted to only those firms that were more
likely to adopt the technology. Typically, there exists significant difference
between the observed number of ``adopters'' and ``nonadopters,'' which is
usually coded as binary response. A critical issue involved in modeling such
binary response data is the appropriate choice of link functions in a
regression model. In this paper we introduce a new flexible skewed link
function for modeling binary response data based on the generalized extreme
value (GEV) distribution. We show how the proposed GEV links provide more
flexible and improved skewed link regression models than the existing skewed
links, especially when dealing with imbalance between the observed number of
0's and 1's in a data. The flexibility of the proposed model is illustrated
through simulated data sets and a billing data set of the electronic payments
system adoption from a Fortune 100 company in 2005.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS354 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Asymptotics of a Clustering Criterion for Smooth Distributions
We develop a clustering framework for observations from a population with a
smooth probability distribution function and derive its asymptotic properties.
A clustering criterion based on a linear combination of order statistics is
proposed. The asymptotic behavior of the point at which the observations are
split into two clusters is examined. The results obtained can then be utilized
to construct an interval estimate of the point which splits the data and
develop tests for bimodality and presence of clusters
Model-Based Method for Social Network Clustering
We propose a simple mixed membership model for social network clustering in
this note. A flexible function is adopted to measure affinities among a set of
entities in a social network. The model not only allows each entity in the
network to possess more than one membership, but also provides accurate
statistical inference about network structure. We estimate the membership
parameters by using an MCMC algorithm. We evaluate the performance of the
proposed algorithm by applying our model to two empirical social network data,
the Zachary club data and the bottlenose dolphin network data. We also conduct
some numerical studies for different types of simulated networks for assessing
the effectiveness of our algorithm. In the end, some concluding remarks and
future work are addressed briefly
Wavelet modeling of priors on triangles
AbstractParameters in statistical problems often live in a geometry of certain shape. For example, count probabilities in a multinomial distribution belong to a simplex. For these problems, Bayesian analysis needs to model priors satisfying certain constraints imposed by the geometry. This paper investigates modeling of priors on triangles by use of wavelets constructed specifically for triangles. Theoretical analysis and numerical simulations show that our modeling is flexible and is superior to the commonly used Dirichlet prior
Bayesian isotonic changepoint analysis
A general approach to Bayesian isotonic changepoint problems is developed. Such isotonic changepoint analysis includes trends and other constraint problems and it captures linear, non-smooth as well as abrupt changes. Desired marginal posterior densities are obtained using a Markov chain Monte Carlo method. The methodology is exemplified using one simulated and two real data examples, where it is shown that our proposed Bayesian approach captures the qualitative conclusion about the shape of the trend change.Departamento de Matemátic
Bayesian modeling with spatial curvature processes
Spatial process models are widely used for modeling point-referenced
variables arising from diverse scientific domains. Analyzing the resulting
random surface provides deeper insights into the nature of latent dependence
within the studied response. We develop Bayesian modeling and inference for
rapid changes on the response surface to assess directional curvature along a
given trajectory. Such trajectories or curves of rapid change, often referred
to as \emph{wombling} boundaries, occur in geographic space in the form of
rivers in a flood plain, roads, mountains or plateaus or other topographic
features leading to high gradients on the response surface. We demonstrate
fully model based Bayesian inference on directional curvature processes to
analyze differential behavior in responses along wombling boundaries. We
illustrate our methodology with a number of simulated experiments followed by
multiple applications featuring the Boston Housing data; Meuse river data; and
temperature data from the Northeastern United States
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