Results 71 to 80 of about 15,773 (265)

Errors in Statistical Inference Under Model Misspecification: Evidence, Hypothesis Testing, and AIC

Frontiers in Ecology and Evolution, 2019
The methods for making statistical inferences in scientific analysis have diversified even within the frequentist branch of statistics, but comparison has been elusive.
Brian Dennis, José Miguel Ponciano, Mark L. Taper, Mark L. Taper, Subhash R. Lele   +4 more
doaj   +1 more source

Renewable Energy Use, Agricultural Productivity, and Economic Expansion Impact on Environmental Sustainability in India

Energy Science &Engineering, EarlyView.
ABSTRACT This research aims to explore the effects of agricultural productivity and the use of renewable energy sources on India's CO2 emissions while taking economic expansion into concern based on data during 1985–2022. This study applies the autoregressive distributed lag (ARDL) technique, dynamic‐ordinary least square (DOLS), fully‐modified ...
Palanisamy Manigandan, Radulescu Magdalena, Duraisamy Pachiyappan, Nadia Sha, Marian Cristescu   +4 more
wiley   +1 more source

Applying the M2 Statistic to Evaluate the Fit of Diagnostic Classification Models in the Presence of Attribute Hierarchies

Frontiers in Psychology, 2018
The performance of the limited-information statistic M2 for diagnostic classification models (DCMs) is under-investigated in the current literature. Specifically, the investigations of M2 for specific DCMs rather than general modeling frameworks are ...
Fu Chen, Yanlou Liu, Tao Xin, Ying Cui
doaj   +1 more source

Coherent Forecasting of Realized Volatility

Journal of Forecasting, EarlyView.
ABSTRACT The QLIKE loss function is the stylized favorite of the literature on volatility forecasting when it comes to out‐of‐sample evaluation and the state of the art model for realized volatility (RV) forecasting is the HAR model, which minimizes the squared error loss for in‐sample estimation of the parameters.
Marius Puke, Karsten Schweikert
wiley   +1 more source

Minimum Penalized ϕ-Divergence Estimation under Model Misspecification

Entropy, 2018
This paper focuses on the consequences of assuming a wrong model for multinomial data when using minimum penalized ϕ -divergence, also known as minimum penalized disparity estimators, to estimate the model parameters.
M. Virtudes Alba-Fernández, M. Dolores Jiménez-Gamero, F. Javier Ariza-López   +2 more
doaj   +1 more source

Forecasting Count Data With Varying Dispersion: A Latent‐Variable Approach

Journal of Forecasting, EarlyView.
ABSTRACT Count data, such as product sales and disease case counts, are common in business forecasting and many areas of science. Although the Poisson distribution is the best known model for such data, its use is severely limited by its assumption that the dispersion is a fixed function of the mean, which rarely holds in real‐world scenarios.
Easton Huch, Candace Berrett, Mason Ferlic, Kimberly F. Sellers   +3 more
wiley   +1 more source

Simulating realistic patient profiles from pharmacokinetic models by a machine learning postprocessing correction of residual variability

CPT: Pharmacometrics & Systems Pharmacology
We address the problem of model misspecification in population pharmacokinetics (PopPK), by modeling residual unexplained variability (RUV) by machine learning (ML) methods in a postprocessing step after conventional model building. The practical purpose
Christos Kaikousidis, Robert R. Bies, Aristides Dokoumetzidis   +2 more
doaj   +1 more source

Robust Portfolio Optimization with Environmental, Social, and Corporate Governance Preference

Risks
This study addresses the crucial but under-explored topic of ambiguity aversion, i.e., model misspecification, in the area of environmental, social, and corporate governance (ESG) within portfolio decisions.
Marcos Escobar-Anel, Yiyao Jiao
doaj   +1 more source

DSGE Model Forecasting: Rational Expectations Versus Adaptive Learning

Journal of Forecasting, EarlyView.
ABSTRACT This paper compares within‐sample and out‐of‐sample fit of a DSGE model with rational expectations to a model with adaptive learning. The Galí, Smets, and Wouters model is the chosen laboratory using quarterly real‐time euro area data vintages, covering 2001Q1–2019Q4.
Anders Warne
wiley   +1 more source

Improving Implied Volatility Forecasts for American Options Using Neural Networks

Journal of Futures Markets, EarlyView.
ABSTRACT This paper explores the application of neural networks to improve pricing of American options. Focusing on both American and European options on the S&P 100 index from January 2016 to August 2023, we integrate neural networks to model the difference between market‐implied and model‐implied volatilities derived from the Black‐Scholes and Heston
Haitong Jiang, Emese Lazar, Miriam Marra
wiley   +1 more source