Results 11 to 20 of about 4,713,645 (328)

Profile Likelihood Biclustering

Electronic Journal of Statistics, 2020
Biclustering, the process of simultaneously clustering the rows and columns of a data matrix, is a popular and effective tool for finding structure in a high-dimensional dataset.
Flynn, Cheryl J., Perry, Patrick O.
core   +4 more sources

Maximum likelihood, profile likelihood, and penalized likelihood: a primer. [PDF]

Am J Epidemiol, 2014
The method of maximum likelihood is widely used in epidemiology, yet many epidemiologists receive little or no education in the conceptual underpinnings of the approach. Here we provide a primer on maximum likelihood and some important extensions which have proven useful in epidemiologic research, and which reveal connections between maximum likelihood
Cole SR, Chu H, Greenland S.
europepmc   +7 more sources

Profile likelihood-based parameter and predictive interval analysis guides model choice for ecological population dynamics [PDF]

bioRxiv, 2022
Calibrating mathematical models to describe ecological data provides important insight via parameter estimation that is not possible from analysing data alone.
Matthew J. Simpson, Shannon A. Walker, Emma N. Studerus, Scott W. McCue, Ryan J. Murphy, Oliver J. Maclaren   +5 more
openalex   +2 more sources

Search for low-mass dark matter with CDMSlite using a profile likelihood fit [PDF]

Physical Review D, 2019
Author(s): Agnese, R; Aralis, T; Aramaki, T; Arnquist, IJ; Azadbakht, E; Baker, W; Banik, S; Barker, D; Bauer, DA; Binder, T; Bowles, MA; Brink, PL; Bunker, R; Cabrera, B; Calkins, R; Cameron, RA; Cartaro, C; Cerdeno, DG; Chang, YY; Cooley, J; Cornell, B;
R. Agnese, T. Aralis, T. Aramaki, I. J. Arnquist, E. Azadbakht, W. Baker, S. Banik, D. Barker, D. A. Bauer, Tobias Binder, M. A. Bowles, P. L. Brink, R. Bunker, B. Cabrera, R. Calkins, R. A. Cameron, C. Cartaro, D. G. Cerdeño, Y.-Y. Chang, J. Cooley, Bruce Cornell, P. Cushman, F. De Brienne, T. Doughty, E. Fascione, E. Figueroa‐Feliciano, C. W. Fink, M. Fritts, G. Gerbier, R. Germond, M. Ghaith, S. R. Golwala, H. R. Harris, Nick Herbert, Z. Hong, E. W. Hoppe, L. Hsu, M. E. Huber, V. Iyer, D. Jardin, A. Jastram, C. Jena, M. H. Kelsey, A. Kennedy, A. Kubik, Noah Kurinsky, Richard Lawrence, B. Loer, E. Lopez Asamar, P. Lukens, Danika Marina Macdonell, R. Mahapatra, V. Mandic, N. Mast, Eric Haynes Miller, N. Mirabolfathi, B. Mohanty, J. D. Morales Mendoza, J. K. Nelson, H. Neog, J. L. Orrell, S. M. Oser, W. A. Page, R. Partridge, M. Pepin, F. Ponce, S. S. Poudel, M. Pyle, H. Qiu, W. Rau, A. Reisetter, R. Ren, T. Reynolds, A. Roberts, Alan Robinson, H. E. Rogers, T. Saab, B. Sadoulet, J. Sander, A. Scarff, R. W. Schnee, S. Scorza, Kartik Senapati, B. Serfass, D. Speller, C. Stanford, M. Stein, John Street, H. A. Tanaka, D. Toback, R. Underwood, A. N. Villano, B. von Krosigk, S. L. Watkins, J. S. Wilson, M. J. Wilson, J. Winchell, D. H. Wright, S. Yellin, Betty Young   +99 more
openalex   +3 more sources

Constraints on Lorentz Invariance Violation from Gamma-Ray Burst Rest-Frame Spectral Lags Using Profile Likelihood [PDF]

Universe
We reanalyze the spectral lag data for 56 Gamma-Ray Bursts (GRBs) in the cosmological rest frame to search for Lorentz Invariance Violation (LIV) using frequentist inference.
Vyaas Ramakrishnan, Shantanu Desai
doaj   +2 more sources

Profile Maximum Likelihood Estimation of Single-Index Spatial Dynamic Panel Data Model [PDF]

Mathematics, 2023
In this paper, the spatial dynamic panel data (SDPD) model is extended to the single-index spatial dynamic panel data (Si-SDPD) model by introducing a nonlinear connection function to reflect the interaction between explanatory variables.
Mengqi Zhang, Boping Tian
doaj   +2 more sources

Constraint on Lorentz invariance violation for spectral lag transition in GRB 160625B using profile likelihood [PDF]

European Physical Journal C: Particles and Fields
We reanalyze the spectral lag data for GRB 160625B using frequentist inference in order to constrain the energy scale $$(E_{QG})$$ ( E QG ) of Lorentz Invariance Violation (LIV).
Shantanu Desai, Shalini Ganguly
doaj   +4 more sources

Monte Carlo modified profile likelihood in models for clustered data [PDF]

, 2019
The main focus of the analysts who deal with clustered data is usually not on the clustering variables, and hence the group-specific parameters are treated as nuisance. If a fixed effects formulation is preferred and the total number of clusters is large
Claudia Di Caterina, Giuliana Cortese, Nicola Sartori   +2 more
openalex   +4 more sources

Bias Correction and Modified Profile Likelihood under the Wishart Complex Distribution [PDF]

IEEE Transactions on Geoscience and Remote Sensing, 2014
This paper proposes improved methods for the maximum likelihood (ML) estimation of the equivalent number of looks $L$. This parameter has a meaningful interpretation in the context of polarimetric synthetic aperture radar (PolSAR) images.
Cintra, Renato J., Frery, Alejandro C., Nascimento, Abraão D. C.   +2 more
core   +2 more sources

Maximum-likelihood model averaging to profile clustering of site types across discrete linear sequences. [PDF]

PLoS Computational Biology, 2009
A major analytical challenge in computational biology is the detection and description of clusters of specified site types, such as polymorphic or substituted sites within DNA or protein sequences.
Zhang Zhang, Jeffrey P Townsend
doaj   +2 more sources