Results 131 to 140 of about 1,032,480 (246)

Logistic-Normal Likelihoods for Heteroscedastic Label Noise [Elektronisk resurs]

, 2023
A natural way of estimating heteroscedastic label noise in regression is to model the observed (potentially noisy) target as a sample from a normal distribution, whose parameters can be learned by minimizing the negative log-likelihood.
Azizpour, Hossein,, Englesson, Erik,, Mehrpanah, Amir,   +2 more
core  

Do jumps matter in discrete-time portfolio optimization?

Operations Research Perspectives
This paper studies a discrete-time portfolio optimization problem, wherein the underlying risky asset follows a Lévy GARCH model. Besides a Gaussian noise, the framework allows for various jump increments, including infinite-activity jumps.
Marcos Escobar-Anel, Ben Spies, Rudi Zagst   +2 more
doaj   +1 more source

The Effects of Financial Liberalization on Country‐Level Emissions

Journal of Money, Credit and Banking, EarlyView.
Abstract We use country‐level shocks and a difference‐in‐differences framework to study how financial market liberalization is related to country‐level carbon dioxide, overall greenhouse gas, and sulfur dioxide emissions. Liberalization increases the number of foreign institutional investors, which could lead to a decrease in firm pollution ...
Kim Ceulemans, Sarfraz Khan, John K. Wald   +2 more
wiley   +1 more source

Belief-Adaptive MAP Detection for Molecular ISI Channels with Heteroscedastic Noise

CoRR
Inter-symbol interference (ISI) with heteroscedastic (state-dependent) noise is a defining feature of molecular communication via diffusion (MCvD). However, such noise variance dependency across ISI states has not been systematically considered in prior detector designs.
Erencem Ozbey, H. Birkan Yilmaz, Chan-Byoung Chae   +2 more
openaire   +2 more sources

Responsible Innovation: The Impact of Major Industrial Disasters and Gender in a Global South Context

Journal of Product Innovation Management, EarlyView.
ABSTRACT Responsible innovation (RI) dynamics remain underexplored in Global South contexts, which have a high prevalence of micro‐ and small enterprises and are vulnerable to the devastating effects of industrial disasters. Only a few studies examine RI within such settings, where it is arguably needed most.
Afreen Choudhury, Magda Hassan, Jarrod P. Vassallo, Chinmay Pattnaik   +3 more
wiley   +1 more source

Robust CDF‐Filtering of a Location Parameter

Journal of Time Series Analysis, EarlyView.
ABSTRACT This paper introduces a novel framework for designing robust filters associated with signal plus noise models having symmetric observation density. The filters are obtained by a recursion where the innovation term is a transform of the cumulative distribution function of the residuals.
Leopoldo Catania, Andrew C. Harvey, Alessandra Luati   +2 more
wiley   +1 more source

Sparse Causal Dynamic Linear Regression

Journal of Time Series Analysis, EarlyView.
ABSTRACT We develop a sparse causal dynamic regression framework for long multivariate time series. With very long time series, the potentially large number of lags and leads in a dynamic regression model often makes time‐domain estimation numerically unstable or intractable.
Rui Huang, Kung‐Sik Chan
wiley   +1 more source

MULTIOBJECTIVE RANKING AND SELECTION WITH CORRELATION AND HETEROSCEDASTIC NOISE

, 2020
We consider multi-objective ranking and selection problems with heteroscedastic noise and correlation between the mean values of alternatives. From a Bayesian perspective, we propose a sequential sampling technique that uses a combination of screening ...
Rojas-Gonzalez, Sebastian, VAN NIEUWENHUYSE, Inneke, Branke, Juergen   +2 more
core  

Detecting Multiple Change Points in Linear Models With Heteroscedasticity

Journal of Time Series Analysis, EarlyView.
ABSTRACT The problem of detecting change points in the parameters of a linear regression model with errors and covariates exhibiting heteroscedasticity is considered. Asymptotic results for weighted functionals of the cumulative sum (CUSUM) processes of model residuals are established when the model errors are weakly dependent and non‐stationary ...
Lajos Horváth, Gregory Rice, Yuqian Zhao   +2 more
wiley   +1 more source

Anchored or Adrift? A Note on Measuring Inflation Expectations Anchoring

Oxford Bulletin of Economics and Statistics, EarlyView.
ABSTRACT We develop a behavioural model of inflation which contains an indicator that quantifies the expectations unanchoring risk over the business cycle. We estimate this model using US and Canadian inflation and output gap data. We find that during the post‐pandemic inflation surge, the macroeconomic data are compatible with non mean‐reverting ...
Olena Kostyshyna, Isabelle Salle, Hung Truong   +2 more
wiley   +1 more source