Results 71 to 80 of about 456 (211)
The Decentralization of Liquor Policies in Texas During the Post‐Prohibition Era
ABSTRACT We examine the decentralization of liquor policies in Texas during the Post‐Prohibition era using newly collected historical legislative roll call data. By combining these data with local referendum vote shares, we analyze both legislators' and constituents' preferences on liquor policy.
Andrew Arnold, Holger Sieg
wiley +1 more source
An Uncertainty Based Approach for Dealing With Selection Bias in Non‐Probability Samples
Summary The main issue with non‐probability samples is that the standard design‐based approach cannot be applied as the selection mechanism is unknown. In this paper, the concept of uncertainty on data generating model, resulting from the lack of knowledge of the sampling design acting in the non‐probability sample, is discussed.
Pier Luigi Conti, Daniela Marella
wiley +1 more source
Medical Knowledge Integration Into Reinforcement Learning Algorithms for Dynamic Treatment Regimes
Summary The goal of precision medicine is to provide individualised treatment at each stage of chronic diseases, a concept formalised by dynamic treatment regimes (DTR). These regimes adapt treatment strategies based on decision rules learned from clinical data to enhance therapeutic effectiveness.
Sophia Yazzourh +3 more
wiley +1 more source
A spectral analysis extension to DEMATEL for strategic leverage points identification
Abstract Efforts to intervene in complex systems often emphasize influential factors, yet system behavior is equally shaped by the relationships among them. Methods such as Decision‐Making Trial and Evaluation Laboratory (DEMATEL) map causal structures but remain descriptive and do not identify which relationships provide the greatest leverage for ...
Pavlos Delias, Kerasia Kalkitsa
wiley +1 more source
Evidence Gathering Under Competitive and Noncompetitive Rewards
ABSTRACT Reward schemes may affect not only agents' effort but also their incentives to gather information in order to reduce the riskiness of the productive activity. In a laboratory experiment using a novel task, we find that the relationship between incentives and evidence gathering depends critically on the availability of information about peers ...
Philip Brookins +2 more
wiley +1 more source
Markov Determinantal Point Process for Dynamic Random Sets
ABSTRACT The Law of Determinantal Point Process (LDPP) is a flexible parametric family of distributions over random sets defined on a finite state space, or equivalently over multivariate binary variables. The aim of this paper is to introduce Markov processes of random sets within the LDPP framework. We show that, when the pairwise distribution of two
Christian Gouriéroux, Yang Lu
wiley +1 more source
Adaptive Estimation for Weakly Dependent Functional Times Series
ABSTRACT We propose adaptive mean and autocovariance function estimators for stationary functional time series under 𝕃p−m‐approximability assumptions. These estimators are designed to adapt to the regularity of the curves and to accommodate both sparse and dense data designs.
Hassan Maissoro +2 more
wiley +1 more source
The Accuracy Smoothness Dilemma in Prediction: A Novel Multivariate M‐SSA Forecast Approach
ABSTRACT Forecasting presents a complex estimation challenge, as it involves balancing multiple, often conflicting, priorities and objectives. Conventional forecast optimization methods typically emphasize a single metric, such as minimizing the mean squared error (MSE), which may neglect other crucial aspects of predictive performance. To address this
Marc Wildi
wiley +1 more source
Multiple Chains Markov Switching Vector Autoregression
ABSTRACT Both the U.S. stock and bond returns exhibit distinct Markovian regimes. However, because these regimes display limited coherence, conventional models typically require highly parameterized systems to adequately capture their joint distribution.
Leopoldo Catania
wiley +1 more source
Penalized Convex Estimation in Dynamic Location Models
ABSTRACT This paper studies L1$$ {L}^1 $$‐penalized estimation for location models yt=mt+ϵt$$ {y}_t={m}_t+{\epsilon}_t $$, where mt$$ {m}_t $$ is defined by a possibly non‐Markovian recursion and ϵt$$ {\epsilon}_t $$ is a martingale difference sequence with possibly time‐varying conditional variance.
Reda Alami Chentoufi
wiley +1 more source

