Results 61 to 70 of about 133 (130)

Gen‐AI Is Not an Option for Environment Sustainability‐Enabling of Gen‐AI for Responsible and Green Supply Chains Using a Grey Network Map (GNM)

Business Strategy and the Environment, EarlyView.
ABSTRACT Environmental sustainability in supply chains is no longer considered a compliance concern. It has become a strategic capability challenge, as firms seek to use Generative artificial intelligence (Gen‐AI) to improve decision quality, resource efficiency and responsible operations.
Anbesh Jamwal, Anil Kumar, Ashutosh Samadhiya, Sunil Luthra   +3 more
wiley   +1 more source

Finance 5.0, Digital Financial Inclusion, and Carbon Footprint: Evidence From a Multi‐Country Panel Using Threshold Models

Business Strategy and the Environment, EarlyView.
ABSTRACT This study investigates the impact of digital finance and financial inclusion on carbon emissions within a multi‐country panel framework, contributing to the emerging Finance 5.0 literature that links digital transformation with environmental sustainability.
Utku Altunöz
wiley   +1 more source

CEO Managerial Ability and the Strategic Repetition of Climate Disclosures

Business Strategy and the Environment, EarlyView.
ABSTRACT This study examines whether CEO managerial ability shapes the repetition of firms' climate‐related disclosures in mandatory 10‐K filings. Climate reporting is highly judgment based and central to firms' broader climate‐risk management strategies, yet little is known about why some firms repeatedly use similar climate narratives and others ...
Javad Rajabalizadeh
wiley   +1 more source

A‐optimal model‐based design of experiments for processes with uncertain inputs

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract Model‐based design of experiments (MBDoE) techniques are tools for selecting experimental conditions that enable accurate parameter estimation for mechanistic models. Most MBDoE approaches assume that the selected experimental conditions will be implemented perfectly, without uncertainties in the independent variables.
Bright Ofori, Iman Moshiritabrizi, Anh‐Duong Vo, Kimberley B. McAuley   +3 more
wiley   +1 more source

Restricted Tweedie stochastic block models

Canadian Journal of Statistics, EarlyView.
Abstract The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. However, conventional SBMs are not directly applicable to an adjacency matrix that consists of nonnegative zero‐inflated continuous edge weights.
Jie Jian, Mu Zhu, Peijun Sang
wiley   +1 more source

Rank‐based estimation of propensity score weights via subclassification

Canadian Journal of Statistics, EarlyView.
Abstract Propensity score (PS) weighting estimators are widely used for causal effect estimation and enjoy desirable theoretical properties, such as consistency and potential efficiency under correct model specification. However, their performance can degrade in practice due to sensitivity to PS model misspecification.
Linbo Wang, Yuexia Zhang, Thomas S. Richardson, Xiaohua Zhou   +3 more
wiley   +1 more source

On subset least squares estimation and prediction in vector autoregressive models with exogenous variables

Canadian Journal of Statistics, EarlyView.
Abstract We establish the consistency and the asymptotic distribution of the least squares estimators of the coefficients of a subset vector autoregressive process with exogenous variables (VARX). Using a martingale central limit theorem, we derive the asymptotic normal distribution of the estimators. Diagnostic checking is discussed using kernel‐based
Pierre Duchesne, Thanh‐Liem Huynh, Mylène Oscar   +2 more
wiley   +1 more source

Asymptotic properties of cross‐classified sampling designs

Canadian Journal of Statistics, EarlyView.
Abstract We investigate the family of cross‐classified sampling designs across an arbitrary number of dimensions. We introduce a variance decomposition that enables the derivation of general asymptotic properties for these designs and the development of straightforward and asymptotically unbiased variance estimators.
Jean Rubin, Guillaume Chauvet
wiley   +1 more source

Non‐negative Gaussian estimation of variance components in random effects models

Canadian Journal of Statistics, EarlyView.
Abstract When used to estimate variance components (VCs), confidence intervals (CIs) can be truncated at zero, have a point estimate not in the quoted CI, be empty with positive probability, or be all‐inclusive. This is because they have conflicting dual roles, since they are considered to cover the parameter with a specified probability while also ...
André Plante, Michael Plante
wiley   +1 more source

Nonlinear permuted Granger causality

Canadian Journal of Statistics, EarlyView.
Abstract Granger causality is an established, contentious method that seeks causal temporal connections via association and precedence. While not true causal inference, it assists in mapping networks of information flow that may warrant further study.
Noah D. Gade, Jordan Rodu
wiley   +1 more source