3,595 research outputs found
A new index of financial conditions
We use factor augmented vector autoregressive models with time-varying coefficients and stochastic volatility to construct a financial conditions index that can accurately track expectations about growth in key US macroeconomic variables. Time-variation in the model’s parameters allows for the weights attached to each financial variable in the index to evolve over time. Furthermore, we develop methods for dynamic model averaging or selection which allow the financial variables entering into the financial conditions index to change over time. We discuss why such extensions of the existing literature are important and show them to be so in an empirical application involving a wide range of financial variables
A Self-Organization Framework for Wireless Ad Hoc Networks as Small Worlds
Motivated by the benefits of small world networks, we propose a
self-organization framework for wireless ad hoc networks. We investigate the
use of directional beamforming for creating long-range short cuts between
nodes. Using simulation results for randomized beamforming as a guideline, we
identify crucial design issues for algorithm design. Our results show that,
while significant path length reduction is achievable, this is accompanied by
the problem of asymmetric paths between nodes. Subsequently, we propose a
distributed algorithm for small world creation that achieves path length
reduction while maintaining connectivity. We define a new centrality measure
that estimates the structural importance of nodes based on traffic flow in the
network, which is used to identify the optimum nodes for beamforming. We show,
using simulations, that this leads to significant reduction in path length
while maintaining connectivity.Comment: Submitted to IEEE Transactions on Vehicular Technolog
Modeling non-linear Effects with Neural Networks in Relational Event Models
Dynamic networks offer an insight of how relational systems evolve. However,
modeling these networks efficiently remains a challenge, primarily due to
computational constraints, especially as the number of observed events grows.
This paper addresses this issue by introducing the Deep Relational Event
Additive Model (DREAM) as a solution to the computational challenges presented
by modeling non-linear effects in Relational Event Models (REMs). DREAM relies
on Neural Additive Models to model non-linear effects, allowing each effect to
be captured by an independent neural network. By strategically trading
computational complexity for improved memory management and leveraging the
computational capabilities of Graphic Processor Units (GPUs), DREAM efficiently
captures complex non-linear relationships within data. This approach
demonstrates the capability of DREAM in modeling dynamic networks and scaling
to larger networks. Comparisons with traditional REM approaches showcase DREAM
superior computational efficiency. The model potential is further demonstrated
by an examination of the patent citation network, which contains nearly 8
million nodes and 100 million events
The contribution of structural break models to forecasting macroeconomic series
This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving 60 macroeconomic quarterly and monthly time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. We find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance.
However, there are also many cases where simple, rolling window based forecasts perform well
Development of a versatile laser light scattering instrument
A versatile laser light scattering (LLS) instrument is developed for use in microgravity to measure microscopic particles of 30 A to above 3 microns. Since it is an optical technique, LLS does not affect the sample being studied. A LLS instrument built from modules allows several configurations, each optimized for a particular experiment. The multiangle LLS instrument can be mounted in the rack in the Space Shuttle and on Space Station Freedom. It is possible that a Space Shuttle glove-box and a lap-top computer containing a correlator card can be used to perform a number of experiments and to demonstrate the technology needed for more elaborate investigations. This offers simple means of flying a great number of experiments without the additional requirements of full-scale flight hardware experiments
Modern Statistical Models and Methods for Estimating Fatigue-Life and Fatigue-Strength Distributions from Experimental Data
Engineers and scientists have been collecting and analyzing fatigue data
since the 1800s to ensure the reliability of life-critical structures.
Applications include (but are not limited to) bridges, building structures,
aircraft and spacecraft components, ships, ground-based vehicles, and medical
devices. Engineers need to estimate S-N relationships (Stress or Strain versus
Number of cycles to failure), typically with a focus on estimating small
quantiles of the fatigue-life distribution. Estimates from this kind of model
are used as input to models (e.g., cumulative damage models) that predict
failure-time distributions under varying stress patterns. Also, design
engineers need to estimate lower-tail quantiles of the closely related
fatigue-strength distribution. The history of applying incorrect statistical
methods is nearly as long and such practices continue to the present. Examples
include treating the applied stress (or strain) as the response and the number
of cycles to failure as the explanatory variable in regression analyses
(because of the need to estimate strength distributions) and ignoring or
otherwise mishandling censored observations (known as runouts in the fatigue
literature). The first part of the paper reviews the traditional modeling
approach where a fatigue-life model is specified. We then show how this
specification induces a corresponding fatigue-strength model. The second part
of the paper presents a novel alternative modeling approach where a
fatigue-strength model is specified and a corresponding fatigue-life model is
induced. We explain and illustrate the important advantages of this new
modeling approach.Comment: 93 pages, 27 page
Sampling Sparse Signals on the Sphere: Algorithms and Applications
We propose a sampling scheme that can perfectly reconstruct a collection of
spikes on the sphere from samples of their lowpass-filtered observations.
Central to our algorithm is a generalization of the annihilating filter method,
a tool widely used in array signal processing and finite-rate-of-innovation
(FRI) sampling. The proposed algorithm can reconstruct spikes from
spatial samples. This sampling requirement improves over
previously known FRI sampling schemes on the sphere by a factor of four for
large . We showcase the versatility of the proposed algorithm by applying it
to three different problems: 1) sampling diffusion processes induced by
localized sources on the sphere, 2) shot noise removal, and 3) sound source
localization (SSL) by a spherical microphone array. In particular, we show how
SSL can be reformulated as a spherical sparse sampling problem.Comment: 14 pages, 8 figures, submitted to IEEE Transactions on Signal
Processin
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