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Quantum Semiparametric Estimation [PDF]

Physical Review X, 2020
In the study of quantum limits to parameter estimation, the high dimensionality of the density operator and that of the unknown parameters have long been two of the most difficult challenges. Here, we propose a theory of quantum semiparametric estimation
Mankei Tsang, Francesco Albarelli, Animesh Datta   +2 more
doaj   +4 more sources

Semiparametric maximum likelihood probability density estimation.

PLoS ONE, 2021
A comprehensive methodology for semiparametric probability density estimation is introduced and explored. The probability density is modelled by sequences of mostly regular or steep exponential families generated by flexible sets of basis functions ...
Frank Kwasniok
doaj   +6 more sources

Prenatal paracetamol exposure and wheezing in infancy: a targeted maximum likelihood estimation application [PDF]

BMJ Open Respiratory Research
Introduction Targeted maximum likelihood estimation (TMLE) is a semiparametric doubly‐robust estimator that integrates the SuperLearner in the estimation process, an ensemble method that allows us to model the exposure–outcome relationship combining ...
Maja Popovic, Lorenzo Richiardi, Daniela Zugna, Costanza Pizzi, Anne-Marie Nybo Andersen, Franca Rusconi, Enrica Migliore, Milena Maule, Piero Fariselli, Tiziana Sanavia, Chiara Moccia, Giovenale Moirano   +11 more
doaj   +2 more sources

Navigating challenges in pediatric trial conduct: integrating bayesian sequential design with semiparametric elicitation for handling primary and secondary endpoints [PDF]

BMC Medical Research Methodology
Background This study presents a Bayesian Adaptive Semiparametric approach designed to address the challenges of pediatric randomized controlled trials (RCTs).
Danila Azzolina, Ileana Baldi, Silvia Bressan, Mohd Rashid Khan, Liviana Da Dalt, Dario Gregori, Paola Berchialla   +6 more
doaj   +2 more sources

Optimal Estimation of Quantum Coherence by Bell State Measurement: A Case Study

Entropy, 2023
Quantum coherence is the most distinguished feature of quantum mechanics. As an important resource, it is widely applied to quantum information technologies, including quantum algorithms, quantum computation, quantum key distribution, and quantum ...
Yuan Yuan, Xufeng Huang, Yueping Niu, Shangqing Gong   +3 more
doaj   +1 more source

Penalty and Shrinkage Strategies Based on Local Polynomials for Right-Censored Partially Linear Regression

Entropy, 2022
This study aims to propose modified semiparametric estimators based on six different penalty and shrinkage strategies for the estimation of a right-censored semiparametric regression model.
Syed Ejaz Ahmed, Dursun Aydın, Ersin Yılmaz   +2 more
doaj   +1 more source

Stressed portfolio optimization with semiparametric method

Financial Innovation, 2022
Tail risk is a classic topic in stressed portfolio optimization to treat unprecedented risks, while the traditional mean–variance approach may fail to perform well.
Chuan-Hsiang Han, Kun Wang
doaj   +1 more source

Comparing parametric and semiparametric error correction models for estimation of long run equilibrium between exports and imports

Apstract: Applied Studies in Agribusiness and Commerce, 2017
This paper introduces the semiparametric error correction model for estimation of export-import relationship as an alternative to the least squares approach.
Henry De-Graft Acquah, Lawrence Acheampong   +1 more
doaj   +1 more source

bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors

Journal of Statistical Software, 2019
The Bayesian spectral analysis model (BSAM) is a powerful tool to deal with semiparametric methods in regression and density estimation based on the spectral representation of Gaussian process priors. The bsamGP package for R provides a comprehensive set
Seongil Jo, Taeryon Choi, Beomjo Park, Peter Lenk   +3 more
doaj   +1 more source

ESTIMASI SELANG KEPERCAYAAN NILAI UJIAN NASIONAL BERBASIS KOMPETENSI BERDASARKAN MODEL REGRESI SEMIPARAMETRIK MULTIRESPON TRUNCATED SPLINE

Media Statistika, 2020
Confidence interval estimation is important in statistical inference for the parameters of the regression model, but the theory of confidence interval estimation for multi-response semiparametric regression model parameters based on the truncated spline ...
Lilik Hidayati, Nur Chamidah, I Nyoman Budiantara   +2 more
doaj   +1 more source