62,420 research outputs found
One Dimensional Kondo Lattice Model Studied by the Density Matrix Renormalization Group Method
Recent developments of the theoretical investigations on the one-dimensional
Kondo lattice model by using the density matrix renormalization group (DMRG)
method are discussed in this review. Short summaries are given for the
zero-temperature DMRG, the finite-temperature DMRG, and also its application to
dynamic quantities. Away from half-filling, the paramagnetic metallic state is
shown to be a Tomonaga-Luttinger liquid with the large Fermi surface. For the
large Fermi surface its size is determined by the sum of the densities of the
conduction electrons and the localized spins. The correlation exponent K_rho of
this metallic phase is smaller than 1/2. At half-filling the ground state is
insulating. Excitation gaps are different depending on channels, the spin gap,
the charge gap and the quasiparticle gap. Temperature dependence of the spin
and charge susceptibilities and specific heat are discussed. Particularly
interesting is the temperature dependence of various excitation spectra, which
show unusual properties of the Kondo insulators.Comment: 18 pages, 23 Postscript figures, REVTe
Dynamic Bayesian Predictive Synthesis in Time Series Forecasting
We discuss model and forecast combination in time series forecasting. A
foundational Bayesian perspective based on agent opinion analysis theory
defines a new framework for density forecast combination, and encompasses
several existing forecast pooling methods. We develop a novel class of dynamic
latent factor models for time series forecast synthesis; simulation-based
computation enables implementation. These models can dynamically adapt to
time-varying biases, miscalibration and inter-dependencies among multiple
models or forecasters. A macroeconomic forecasting study highlights the dynamic
relationships among synthesized forecast densities, as well as the potential
for improved forecast accuracy at multiple horizons
Dynamic dependence networks: Financial time series forecasting and portfolio decisions (with discussion)
We discuss Bayesian forecasting of increasingly high-dimensional time series,
a key area of application of stochastic dynamic models in the financial
industry and allied areas of business. Novel state-space models characterizing
sparse patterns of dependence among multiple time series extend existing
multivariate volatility models to enable scaling to higher numbers of
individual time series. The theory of these "dynamic dependence network" models
shows how the individual series can be "decoupled" for sequential analysis, and
then "recoupled" for applied forecasting and decision analysis. Decoupling
allows fast, efficient analysis of each of the series in individual univariate
models that are linked-- for later recoupling-- through a theoretical
multivariate volatility structure defined by a sparse underlying graphical
model. Computational advances are especially significant in connection with
model uncertainty about the sparsity patterns among series that define this
graphical model; Bayesian model averaging using discounting of historical
information builds substantially on this computational advance. An extensive,
detailed case study showcases the use of these models, and the improvements in
forecasting and financial portfolio investment decisions that are achievable.
Using a long series of daily international currency, stock indices and
commodity prices, the case study includes evaluations of multi-day forecasts
and Bayesian portfolio analysis with a variety of practical utility functions,
as well as comparisons against commodity trading advisor benchmarks.Comment: 31 pages, 9 figures, 3 table
Distance Oracles for Time-Dependent Networks
We present the first approximate distance oracle for sparse directed networks
with time-dependent arc-travel-times determined by continuous, piecewise
linear, positive functions possessing the FIFO property.
Our approach precomputes approximate distance summaries from
selected landmark vertices to all other vertices in the network. Our oracle
uses subquadratic space and time preprocessing, and provides two sublinear-time
query algorithms that deliver constant and approximate
shortest-travel-times, respectively, for arbitrary origin-destination pairs in
the network, for any constant . Our oracle is based only on
the sparsity of the network, along with two quite natural assumptions about
travel-time functions which allow the smooth transition towards asymmetric and
time-dependent distance metrics.Comment: A preliminary version appeared as Technical Report ECOMPASS-TR-025 of
EU funded research project eCOMPASS (http://www.ecompass-project.eu/). An
extended abstract also appeared in the 41st International Colloquium on
Automata, Languages, and Programming (ICALP 2014, track-A
Approximate maximum likelihood estimation using data-cloning ABC
A maximum likelihood methodology for a general class of models is presented,
using an approximate Bayesian computation (ABC) approach. The typical target of
ABC methods are models with intractable likelihoods, and we combine an ABC-MCMC
sampler with so-called "data cloning" for maximum likelihood estimation.
Accuracy of ABC methods relies on the use of a small threshold value for
comparing simulations from the model and observed data. The proposed
methodology shows how to use large threshold values, while the number of
data-clones is increased to ease convergence towards an approximate maximum
likelihood estimate. We show how to exploit the methodology to reduce the
number of iterations of a standard ABC-MCMC algorithm and therefore reduce the
computational effort, while obtaining reasonable point estimates. Simulation
studies show the good performance of our approach on models with intractable
likelihoods such as g-and-k distributions, stochastic differential equations
and state-space models.Comment: 25 pages. Minor revision. It includes a parametric bootstrap for the
exact MLE for the first example; includes mean bias and RMSE calculations for
the third example. Forthcoming in Computational Statistics and Data Analysi
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