pwc-archive/papers-with-abstracts huggingface dataset:
Simple subvector inference on sharp identified set in affine models
Authors: Bulat Gafarov
Date: 2019-03-29 00:00:00
Tasks: valid

This paper studies a regularized support function estimator for bounds on components of the parameter vector in the case in which the identified set is a polygon. The proposed regularized estimator has three important properties: (i) it has a uniform asymptotic Gaussian limit in the presence of flat faces in the absence of redundant (or overidentifying) constraints (or vice versa); (ii) the bias from regularization does not enter the first-order limiting distribution; (iii) the estimator remains consistent for sharp (non-enlarged) identified set for the individual components even in the non-regualar case. These properties are used to construct \emph{uniformly valid} confidence sets for an element $\theta_{1}$ of a parameter vector $\theta\in\mathbb{R}^{d}$ that is partially identified by affine moment equality and inequality conditions. The proposed confidence sets can be computed as a solution to a small number of linear and convex quadratic programs, leading to a substantial decrease in computation time and guarantees a global optimum. As a result, the method provides a uniformly valid inference in applications in which the dimension of the parameter space, $d$, and the number of inequalities, $k$, were previously computationally unfeasible ($d,k=100$). The proposed approach can be extended to construct confidence sets for intersection bounds, to construct joint polygon-shaped confidence sets for multiple components of $\theta$, and to find the set of solutions to a linear program. Inference for coefficients in the linear IV regression model with an interval outcome is used as an illustrative example.
Estimation of cell lineage trees by maximum-likelihood phylogenetics
Date: 2019-03-29 00:00:00

CRISPR technology has enabled large-scale cell lineage tracing for complex
multicellular organisms by mutating synthetic genomic barcodes during
organismal development. However, these sophisticated biological tools currently
use ad-hoc and outmoded computational methods to reconstruct the cell lineage
tree from the mutated barcodes. Because these methods are agnostic to the
biological mechanism, they are unable to take full advantage of the data's
structure. We propose a statistical model for the mutation process and develop
a procedure to estimate the tree topology, branch lengths, and mutation
parameters by iteratively applying penalized maximum likelihood estimation. In
contrast to existing techniques, our method estimates time along each branch,
rather than number of mutation events, thus providing a detailed account of
tissue-type differentiation. Via simulations, we demonstrate that our method is
substantially more accurate than existing approaches. Our reconstructed trees
also better recapitulate known aspects of zebrafish development and reproduce
similar results across fish replicates.
A Thermodynamic Picture of Financial Market and Model Risk
Date: 2019-03-30 00:00:00

By treating the financial market as a thermodynamic system, we establish a
one-to-one correspondence between thermodynamic variables and economic
quantities. Measured by the expected loss under the worst-case scenario,
financial risk caused by model uncertainty is regarded as a result of the
interaction between financial market and external information sources. This
forms a thermodynamic picture in which a closed system interacts with an
external reservoir, reaching its equilibrium at the worst-case scenario. The
severity of the worst-case scenario depends on the rate of heat dissipation,
caused by information sources reducing the entropy of the system. This
thermodynamic picture leads to simple and natural derivation of the
characterization rules of the worst-case risk, and gives its Lagrangian and
Hamiltonian forms. With its help financial practitioners may evaluate risks
utilizing both equilibrium and non-equilibrium thermodynamics.
WRFRFT-based Coherent Detection and Parameter Estimation of Radar Moving Target With Unknown Entry/Departure Time
Date: 2019-03-30 00:00:00
Tasks: ARC, parameter estimation

A moving target may enter a radar coverage area unannounced and leave after
an unspecified period, which implies that the target's entry time and departure
time are unknown. In the absence of these time information, target detection
and parameter estimation (DAPE) will be severely impacted. In this paper, we
consider the coherent detection and parameters estimation problem for a radar
moving target with unknown entry time and departure time (that is, the time
when the target appears-in/leaves the radar detection field is unknown),
involving across range cell (ARC) and Doppler spread (DS) effects within the
observation period. A new algorithm, known as window Radon Fractional Fourier
transform (WRFRFT) is proposed to detect and estimate the target's time
parameters (i.e., entry time and departure time) and motion parameters (i.e.,
range, velocity and acceleration). The observation values of a maneuvering
target are first intercepted and extracted by the window function and searching
along the motion trajectory. Then these values are fractional Fourier
transformed and well accumulated in the WRFRFT domain, where the DAPE of target
could be accomplished thereafter. Experiments with simulated and real radar
data sets prove its effectiveness.
Post-Selection Inference in Three-Dimensional Panel Data
Date: 2019-04-30 00:00:00
Tasks: Model Selection

Three-dimensional panel models are widely used in empirical analysis.
Researchers use various combinations of fixed effects for three-dimensional
panels. When one imposes a parsimonious model and the true model is rich, then
it incurs mis-specification biases. When one employs a rich model and the true
model is parsimonious, then it incurs larger standard errors than necessary. It
is therefore useful for researchers to know correct models. In this light, Lu,
Miao, and Su (2018) propose methods of model selection. We advance this
literature by proposing a method of post-selection inference for regression
parameters. Despite our use of the lasso technique as means of model selection,
our assumptions allow for many and even all fixed effects to be nonzero.
Simulation studies demonstrate that the proposed method is more precise than
under-fitting fixed effect estimators, is more efficient than over-fitting
fixed effect estimators, and allows for as accurate inference as the oracle
estimator.
Voltage Quality Time Series Classification using Convolutional Neural Network
Date: 2019-03-30 00:00:00
Tasks: Classification, Time Series, Time Series Analysis, Time Series Classification

This paper presents the effectiveness of convolutional neural network (CNN)
to classify power quality problems. These problems arise mainly due to increase
in use of non-linear loads, operation of devices like adjustable speed drives
and power factor correction capacitors, which is a growing concern both for
utilities and customers. This work uses the advances in supervised learning to
classify different power quality time-series waveforms such as voltage sag,
swell, interruption, harmonics, transients and flicker. CNN results in a very
high classification accuracy compared to other traditional and machine learning
methods in presence of noise. This process can be employed by utilities as well
as customers to understand the cause and mitigate voltage quality problems.
Price equations with symmetric supply/demand; implications for fat tails
Date: 2019-03-30 00:00:00

Implementing a set of microeconomic criteria, we develop price dynamics
equations using a function of demand/supply with key symmetry properties. The
function of demand/supply can be linear or nonlinear. The type of function
determines the nature of the tail of the distribution based on the randomness
in the supply and demand. For example, if supply and demand are normally
distributed, and the function is assumed to be linear, then the density of
relative price change has behavior $x^{-2}$ for large $x$ (i.e., large
deviations). The exponent approaches $-1$ if the function of supply and demand
involves a large exponent. The falloff is exponential, i.e., $e^{-x}$, if the
function of supply and demand is logarithmic.
Optimal Number of Measurements in a Linear System with Quadratically Decreasing SNR
Date: 2019-03-31 00:00:00

We consider the design of a linear sensing system with a fixed energy budget
assuming that the sampling noise is the dominant noise source. The energy
constraint implies that the signal energy per measurement decreases linearly
with the number of measurements. When the maximum sampling rate of the sampling
circuit is chosen to match the designed sampling rate, the assumption on the
noise implies that its variance increases approximately linearly with the
sampling rate (number of measurements). Therefore, the overall SNR per
measurement decreases quadratically in the number of measurements. Our analysis
shows that, in this setting there is an optimal number of measurements. This is
in contrast to the standard case, where noise variance remains unchanged with
sampling rate, in which case more measurements imply better performance. Our
results are based on a state evolution technique of the well-known approximate
message passing algorithm. We consider both the sparse (e.g. Bernoulli-Gaussian
and least-favorable distributions) and the non-sparse (e.g. Gaussian
distribution) settings in both the real and complex domains. Numerical results
corroborate our analysis.
Satellite Based IoT Networks for Emerging Applications
Date: 2019-03-29 00:00:00

Internet of things (IoT) has been proven to be a ubiquitous technology for
several existing and new applications. It can provide both accuracy and
sustainability in the emerging services and applications. IoT has several
advantages and it can provide different levels of coverage in different
geographical locations and contexts. There are several applications in which
the wide coverage, low power and reliability of the communication have very
high priorities. Conglomeration of IoT and satellite networks can make it very
attractive for these applications. In this article, we present the general
features, motivation and deployment issues of satellite based IoT networks. We
present the applications where these satellite based IoT networks can play
important roles. We also present the hybridization of narrowband IoT and
satellite networks which can provide a long term sustainable network solution
for several applications.
Deep Learning in Asset Pricing
Authors: Luyang Chen, Markus Pelger, Jason Zhu
Date: 2019-03-11 00:00:00
Tasks: Deep Learning, Time Series, Time Series Analysis

We use deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. The key innovations are to use the fundamental no-arbitrage condition as criterion function, to construct the most informative test assets with an adversarial approach and to extract the states of the economy from many macroeconomic time series. Our asset pricing model outperforms out-of-sample all benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors and identifies the key factors that drive asset prices.
Performance Analysis of Channel Extrapolation in FDD Massive MIMO Systems
Date: 2020-01-22 00:00:00

Channel estimation for the downlink of frequency division duplex (FDD)
massive MIMO systems is well known to generate a large overhead as the amount
of training generally scales with the number of transmit antennas in a MIMO
system. In this paper, we consider the solution of extrapolating the channel
frequency response from uplink pilot estimates to the downlink frequency band,
which completely removes the training overhead. We first show that conventional
estimators fail to achieve reasonable accuracy. We propose instead to use
high-resolution channel estimation. We derive theoretical lower bounds (LB) for
the mean squared error (MSE) of the extrapolated channel. Assuming that the
paths are well separated, the LB is simplified in an expression that gives
considerable physical insight. It is then shown that the MSE is inversely
proportional to the number of receive antennas while the extrapolation
performance penalty scales with the square of the ratio of the frequency offset
and the training bandwidth. The channel extrapolation performance is validated
through numeric simulations and experimental measurements taken in an anechoic
chamber. Our main conclusion is that channel extrapolation is a viable solution
for FDD massive MIMO systems if accurate system calibration is performed and
favorable propagation conditions are present.
Room Geometry Estimation from Room Impulse Responses using Convolutional Neural Networks
Date: 2019-05-15 00:00:00
Tasks: Position, Room Impulse Response (RIR)

We describe a new method to estimate the geometry of a room given room
impulse responses. The method utilises convolutional neural networks to
estimate the room geometry and uses the mean square error as the loss function.
In contrast to existing methods, we do not require the position or distance of
sources or receivers in the room. The method can be used with only a single
room impulse response between one source and one receiver for room geometry
estimation. The proposed estimation method can achieve an average of six
centimetre accuracy. In addition, the proposed method is shown to be
computationally efficient compared to state-of-the-art methods.
A simple method for detecting chaos in nature
Date: 2020-01-10 00:00:00
Tasks: Heart Rate Variability

Chaos, or exponential sensitivity to small perturbations, appears everywhere
in nature. Moreover, chaos is predicted to play diverse functional roles in
living systems. A method for detecting chaos from empirical measurements should
therefore be a key component of the biologist's toolkit. But, classic
chaos-detection tools are highly sensitive to measurement noise and break down
for common edge cases, making it difficult to detect chaos in domains, like
biology, where measurements are noisy. However, newer tools promise to overcome
these limitations. Here, we combine several such tools into an automated
processing pipeline, and show that our pipeline can detect the presence (or
absence) of chaos in noisy recordings, even for difficult edge cases. As a
first-pass application of our pipeline, we show that heart rate variability is
not chaotic as some have proposed, and instead reflects a stochastic process in
both health and disease. Our tool is easy-to-use and freely available.
Counterfactual Sensitivity and Robustness
Authors: Timothy Christensen, Benjamin Connault
Date: 2019-04-01 00:00:00
Tasks: counterfactual, Discrete Choice Models, Sensitivity

We propose a framework for analyzing the sensitivity of counterfactuals to parametric assumptions about the distribution of latent variables in structural models. In particular, we derive bounds on counterfactuals as the distribution of latent variables spans nonparametric neighborhoods of a given parametric specification while other "structural" features of the model are maintained. Our approach recasts the infinite-dimensional problem of optimizing the counterfactual with respect to the distribution of latent variables (subject to model constraints) as a finite-dimensional convex program. We also develop an MPEC version of our method to further simplify computation in models with endogenous parameters (e.g., value functions) defined by equilibrium constraints. We propose plug-in estimators of the bounds and two methods for inference. We also show that our bounds converge to the sharp nonparametric bounds on counterfactuals as the neighborhood size becomes large. To illustrate the broad applicability of our procedure, we present empirical applications to matching models with transferable utility and dynamic discrete choice models.
Bayesian Trading Cost Analysis and Ranking of Broker Algorithms
Date: 2019-04-22 00:00:00

We present a formulation of the transaction cost analysis (TCA) in the
Bayesian framework for the primary purpose of comparing broker algorithms using
standardized benchmarks. Our formulation allows effective calculation of the
expected value of trading benchmarks with only a finite sample of data relevant
to practical applications. We discuss the nature of distribution of
implementation shortfall, volume-weighted average price, participation-weighted
price and short-term reversion benchmarks. Our model takes into account fat
tails, skewness of the distributions and heteroscedasticity of benchmarks. The
proposed framework allows the use of hierarchical models to transfer
approximate knowledge from a large aggregated sample of observations to a
smaller sample of a particular algorithm.
Channel Static Antennas
Date: 2019-03-29 00:00:00
Tasks: Position

The possibility to keep wireless communication channels static is
investigated. When an antenna is moved away from its position, this will in
general cause the channel to change. Considerations suggest that wireless
communication channels can be kept static by performing a counter-movement of
the antenna to keep it in its original position relative to outside observers.
Feasibility is shown for a platform moving in straight motion over a finite
distance. The channel is kept static by countering the platform's movement with
physical movement of the antenna in the opposite direction. The experiment is
conducted with a quarter-wavelength monopole antenna in the gigahertz range.
Low-Complexity Methods for Estimation After Parameter Selection
Date: 2020-01-28 00:00:00
Tasks: Selection bias

Statistical inference of multiple parameters often involves a preliminary
parameter selection stage. The selection stage has an impact on subsequent
estimation, for example by introducing a selection bias. The post-selection
maximum likelihood (PSML) estimator is shown to reduce the selection bias and
the post-selection mean-squared-error (PSMSE) compared with conventional
estimators, such as the maximum likelihood (ML) estimator.
  However, the computational complexity of the PSML is usually high due to the
multi-dimensional exhaustive search for a global maximum of the post-selection
log-likelihood (PSLL) function. Moreover, the PSLL involves the probability of
selection that, in general, does not have an analytical form. In this paper, we
develop new low-complexity post-selection estimation methods for a two-stage
estimation after parameter selection architecture.
  The methods are based on implementing the iterative maximization by parts
(MBP) approach, which is based on the decomposition of the PSLL function into
"easily-optimized" and complicated parts.
  The proposed second-best PSML method applies the MBP-PSML algorithm with a
pairwise probability of selection between the two highest-ranked parameters
w.r.t. the selection rule.
  The proposed SA-PSML method is based on using stochastic approximation (SA)
and Monte Carlo integrations to obtain a non-parametric estimation of the
gradient of the probability of selection and then applying the MBP-PSML
algorithm on this approximation. For low-complexity performance analysis, we
develop the empirical post-selection Cramer-Rao-type lower bound.
  Simulations demonstrate that the proposed post-selection estimation methods
are tractable and reduce both the bias and the PSMSE, compared with the ML
estimator, while only requiring moderate computational complexity.
Software Based Pole-Zero Extraction Technique for nth Order Analog Filters
Date: 2019-03-31 00:00:00
Tasks: Position

The positions of poles and zeros of a filter circuit indicates how the system
behaves with variation in frequency as well as it results a tool to utilize the
system gain by setting the frequency range in a complex process. The existing
solutions to extract the poles and zeros often include too complex calculations
to use it frequently. In this paper we propose a compact tool to detect the
position of poles and zeros of any practically designed analog filter of any
order without going through the working principle of the circuit as well as by
using this technique calculation of transfer function is does not need to do
derive which often becomes hectic.
Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1
Date: 2019-11-18 00:00:00
Tasks: Saliency Prediction

Previous studies suggested that lateral interactions of V1 cells are
responsible, among other visual effects, of bottom-up visual attention
(alternatively named visual salience or saliency). Our objective is to mimic
these connections with a neurodynamic network of firing-rate neurons in order
to predict visual attention. Early visual subcortical processes (i.e. retinal
and thalamic) are functionally simulated. An implementation of the cortical
magnification function is included to define the retinotopical projections
towards V1, processing neuronal activity for each distinct view during scene
observation. Novel computational definitions of top-down inhibition (in terms
of inhibition of return and selection mechanisms), are also proposed to predict
attention in Free-Viewing and Visual Search tasks. Results show that our model
outpeforms other biologically-inpired models of saliency prediction while
predicting visual saccade sequences with the same model. We also show how
temporal and spatial characteristics of inhibition of return can improve
prediction of saccades, as well as how distinct search strategies (in terms of
feature-selective or category-specific inhibition) can predict attention at
distinct image contexts.
Infrared uplink design for visible light communication (VLC) systems with beam steering
Date: 2019-10-15 00:00:00
Tasks: Diversity

Providing uplink high data rate is one of the big concerns in visible light
communication (VLC) systems. This paper introduces an uplink VLC system based
on an infrared transmitter with beam steering to provide high data rates. In
this work, a 4 branches angle diversity receiver (ADR) is used and the
resultant delay spread and SNR are examined. The proposed system achieved data
rates up to 3.57 Gb/s using simple on-off-keying (OOK).
A Machine Learning Based Classification Approach for Power Quality Disturbances Exploiting Higher Order Statistics in the EMD Domain
Date: 2019-08-14 00:00:00
Tasks: Computational Efficiency

The aim of this paper is to propose a new approach for the pattern
recognition of power quality (PQ) disturbances based on Empirical mode
decomposition (EMD) and $k$ Nearest Neighbor ($k$-NN) classifier. Since EMD
decomposes a signal into intrinsic mode functions (IMF) in time-domain with
same length of the original signal, it preserves the information that is hidden
in Fourier domain or in wavelet coefficients. In this proposed method, power
signals are decomposed into IMFs in EMD domain. Due to the presence of
non-linearity and noise on the original signal, it is hard to analyze them by
second order statistics. Thus, an effective feature set is developed
considering higher order statistics (HOS) like variance, skewness, and kurtosis
from the decomposed first three IMFs. This feature vector is fed into different
classifiers like $k$-NN, probabilistic neural network (PNN), and radial basis
function (RBF). Among all the classifiers, $k$-NN showed higher classification
accuracy and robustness both in training and testing to detect the PQ
disturbance events. Simulation results evaluated that the proposed HOS-EMD
based method along with $k$-NN classifier outperformed in terms of
classification accuracy and computational efficiency in comparison to the other
state-of-art methods both in clean and noisy environment.