321,284 research outputs found
Random Logic Programs: Linear Model
This paper proposes a model, the linear model, for randomly generating logic
programs with low density of rules and investigates statistical properties of
such random logic programs. It is mathematically shown that the average number
of answer sets for a random program converges to a constant when the number of
atoms approaches infinity. Several experimental results are also reported,
which justify the suitability of the linear model. It is also experimentally
shown that, under this model, the size distribution of answer sets for random
programs tends to a normal distribution when the number of atoms is
sufficiently large.Comment: 33 pages. To appear in: Theory and Practice of Logic Programmin
Jeffreys-prior penalty, finiteness and shrinkage in binomial-response generalized linear models
Penalization of the likelihood by Jeffreys' invariant prior, or by a positive
power thereof, is shown to produce finite-valued maximum penalized likelihood
estimates in a broad class of binomial generalized linear models. The class of
models includes logistic regression, where the Jeffreys-prior penalty is known
additionally to reduce the asymptotic bias of the maximum likelihood estimator;
and also models with other commonly used link functions such as probit and
log-log. Shrinkage towards equiprobability across observations, relative to the
maximum likelihood estimator, is established theoretically and is studied
through illustrative examples. Some implications of finiteness and shrinkage
for inference are discussed, particularly when inference is based on Wald-type
procedures. A widely applicable procedure is developed for computation of
maximum penalized likelihood estimates, by using repeated maximum likelihood
fits with iteratively adjusted binomial responses and totals. These theoretical
results and methods underpin the increasingly widespread use of reduced-bias
and similarly penalized binomial regression models in many applied fields
Linear Model Predictive Control of Induction Machine
This article presents new control algorithm for induction machine based on linear model predictive control (MPC). Controller works in similar manners as field oriented control (FOC), but control is performed in stator coordinates. This reduces computational demands as Park’s transformation is absent and induction machine mathematical model in stator coordinates contains less nonlinear elements. Another aim of proposed controller was to achieve fast torque response
M-estimation in high-dimensional linear model
We mainly study the M-estimation method for the high-dimensional linear
regression model, and discuss the properties of M-estimator when the penalty
term is the local linear approximation. In fact, M-estimation method is a
framework, which covers the methods of the least absolute deviation, the
quantile regression, least squares regression and Huber regression. We show
that the proposed estimator possesses the good properties by applying certain
assumptions. In the part of numerical simulation, we select the appropriate
algorithm to show the good robustness of this methodComment: 16 pages,3 table
A Bayesian Multivariate Functional Dynamic Linear Model
We present a Bayesian approach for modeling multivariate, dependent
functional data. To account for the three dominant structural features in the
data--functional, time dependent, and multivariate components--we extend
hierarchical dynamic linear models for multivariate time series to the
functional data setting. We also develop Bayesian spline theory in a more
general constrained optimization framework. The proposed methods identify a
time-invariant functional basis for the functional observations, which is
smooth and interpretable, and can be made common across multivariate
observations for additional information sharing. The Bayesian framework permits
joint estimation of the model parameters, provides exact inference (up to MCMC
error) on specific parameters, and allows generalized dependence structures.
Sampling from the posterior distribution is accomplished with an efficient
Gibbs sampling algorithm. We illustrate the proposed framework with two
applications: (1) multi-economy yield curve data from the recent global
recession, and (2) local field potential brain signals in rats, for which we
develop a multivariate functional time series approach for multivariate
time-frequency analysis. Supplementary materials, including R code and the
multi-economy yield curve data, are available online
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