Results 121 to 130 of about 4,373,274 (284)

Composite marginal likelihood estimation of higher‐order diagnostic classification models under high dimensionality

British Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract Although full‐information maximum likelihood (FIML) estimation is widely used for diagnostic classification models (DCMs), its computational efficiency deteriorates sharply in high‐dimensional settings. This scalability challenge is increasingly critical as DCMs are applied to large‐scale assessments, psychological testing and longitudinal ...
Minho Lee, Yon Soo Suh
wiley   +1 more source

Track-before-detect procedures for detection of extended object

EURASIP Journal on Advances in Signal Processing, 2011
In this article, we present a particle filter (PF)-based track-before-detect (PF TBD) procedure for detection of extended objects whose shape is modeled by an ellipse.
Fan Ling, Zhang Xiaoling, Shi Jun
doaj  

Cosmology with the angular cross-correlation of gravitational-wave and galaxy catalogs: Forecasts for next-generation interferometers and the Euclid survey

Astronomy & Astrophysics
Context. The spatial clustering of galaxies has long been a key probe of cosmology. Gravitational–wave (GW) sources, which provide direct luminosity–distance measurements, have recently emerged as a complementary tracer of large–scale structures.
Pedrotti Alessandro, Mancarella Michele, Bel Julien, Santoni Michele, Gerosa Davide   +4 more
doaj   +1 more source

Adjusted Empirical Likelihood Method in the Presence of Nuisance Parameters with Application to the Sharpe Ratio. [PDF]

Entropy (Basel), 2018
Fu Y, Wang H, Wong A.
europepmc   +1 more source

Motion Parameter Estimation from Optical Flow without Nuisance Parameters

, 2003
Many kinds of computer vision problems can be formalized as statistical estimation problems with nuisance parameters. In the past, such problems have been solved without making any distinction between the nuisance parameters and structural ones. However,
Naoya Ohta
core  

Calibrating Bayesian inference

British Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract Bayesian statistics has gained popularity in psychological research due to its intuitive uncertainty quantification and convenient information‐updating rules. In many applications, however, prior distributions are introduced merely as instruments to facilitate computation, rather than as representations of genuine subjective belief ...
Yang Liu, Jonathan P. Williams, Jan Hannig   +2 more
wiley   +1 more source

Estimation and Removal of Residual Motion Artifact in Retrospectively Motion-Corrected fMRI Data: A Comparison of Intervolume and Intravolume Motion Using Gold Standard Simulated Motion Data

Aperture Neuro
Residual head motion artifact in motion-corrected resting-state (rs-) functional MRI (fMRI) and fMRI datasets reduces the temporal signal-to-noise ratio and leaves non-neuronal signal components in the data, which can induce false findings in these ...
Wanyong Shin, Paul Taylor, Mark J. Lowe
doaj   +1 more source

Multidimensional unipolar IRT and applications to the measurement of print exposure

British Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract Item response theory (IRT) has been a prominent modelling framework in educational and psychological measurement. Traditional IRT models are bipolar, commonly assuming symmetric measurement link functions and symmetric trait distributions over a latent continuum unbounded at both ends. However, many measured constructs, such as print exposure,
Qi (Helen) Huang, Daniel M. Bolt
wiley   +1 more source

Sensitivity analysis for unmeasured pretreatment confounders in causal mediation analysis with debiased machine learning

British Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract Sensitivity analysis for unmeasured confounders is essential for assessing the robustness of causal mediation conclusions. Most existing methods rely on parametric assumptions, which are ill‐suited for machine learning‐based estimators that are not tied to specific parametric models.
Xiao Liu, Cameron McCann
wiley   +1 more source

Arti4D: Statistical Analysis and Modelling of the Spatio‐temporal Variability in Articulated 4D Shapes

Computer Graphics Forum, EarlyView.
Abstract We propose a novel framework for the statistical modeling and analysis of the spatio‐temporal shape variability in articulated 4D (i.e., 3D + time) shapes such as human bodies and animals. We treat articulated 3D shapes, represented using parametric models such as SMPL or its variants, as elements of the product space of shape and pose ...
Z. Li, A. Amrani, S. Rai, H. Laga
wiley   +1 more source