Results 151 to 160 of about 2,535,217 (348)

When in Doubt, Tax More Progressively? Uncertainty and Progressive Income Taxation

International Economic Review, EarlyView.
ABSTRACT We study the optimal income tax problem under parameter uncertainty about household preferences and wage dynamics. We derive conditions characterizing how such uncertainty affects optimal tax policy. To quantify the effect, we estimate a life‐cycle model using US data and a Bayesian approach.
Minsu Chang, Chunzan Wu
wiley   +1 more source

On reduced rank nonnegative matrix factorization for symmetric nonnegative matrices

, 2004
Let V∈Rm,n be a nonnegative matrix. The nonnegative matrix factorization (NNMF) problem consists of finding nonnegative matrix factors W∈Rm,r and H∈Rr,n such that V≈WH.
Neumann, Michael, Plemmons, R.J., Han, Lixing, Catral, M.   +3 more
core   +1 more source

Maximal Nonnegative Perturbation of a Nonnegative Matrix [PDF]

, 2006
Bertrand Haut, G. Bastin, Paul Van Dooren   +2 more
openaire   +1 more source

The Legacy of Policy Inaction in Climate‐Growth Models

International Economic Review, EarlyView.
ABSTRACT To better understand the structure and core mechanisms of a broad class of climate‐growth models, we study a simplified version of the dynamic integrated model of climate and the economy (DICE) through the lens of growth theory. We analytically show that this model features a continuum of saddle‐point stable steady states.
Thomas Steger, Timo Trimborn
wiley   +1 more source

Global and Local Similarity Learning in Multi-Kernel Space for Nonnegative Matrix Factorization. [PDF]

Knowl Based Syst, 2023
Peng C, Hou X, Chen Y, Kang Z, Chen C, Cheng Q.   +5 more
europepmc   +1 more source

On the Matrix (I + X)-1

In this note we consider the question under which conditions all entries of the matrix I-(I+X)-1 are nonnegative in case matrix X is a real positive definite matrix.Sufficient conditions are presented as well as some necessary conditions.One sufficient ...
Engwerda, J.C.
core  

Advances in independent component analysis and nonnegative matrix factorization [PDF]

, 2009
A fundamental problem in machine learning research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors.
Yuan, Zhijian
core  

Estimating Velocities of Infectious Disease Spread Through Spatio‐Temporal Log‐Gaussian Cox Point Processes

International Statistical Review, EarlyView.
Summary Understanding the spread of infectious diseases such as COVID‐19 is crucial for informed decision‐making and resource allocation. A critical component of disease behaviour is the velocity with which disease spreads, defined as the rate of change between time and space.
Fernando Rodriguez Avellaneda, Jorge Mateu, Paula Moraga   +2 more
wiley   +1 more source

Improved Graph-Regularized Discriminative Nonnegative Matrix Factorization for Semi-Supervised Clustering

IEEE Access
Nonnegative matrix factorization (NMF) is an effective dimensionality reduction and representation learning technique that captures the intrinsic structure of nonnegative data by learning low-dimensional, parts-based representations.
Xuzhu Shen, Jie Li
doaj   +1 more source

A Non‐Parametric Framework for Correlation Functions on Product Metric Spaces

International Statistical Review, EarlyView.
Summary We propose a non‐parametric framework for analysing data defined over products of metric spaces, a versatile class encountered in various fields. This framework accommodates non‐stationarity and seasonality and is applicable to both local and global domains, such as the Earth's surface, as well as domains evolving over linear time or time ...
Pier Giovanni Bissiri, Bernardo Nipoti, Emilio Porcu, Philip White   +3 more
wiley   +1 more source