How estimating nuisance parameters can reduce the variance (with consistent variance estimation). [PDF]
Stat Med, 2021 We often estimate a parameter of interest ψ$$ \psi $$ when the identifying conditions involve a finite‐dimensional nuisance parameter θ∈ℝd$$ \theta \in {\mathbb{R}}^d $$ .
Lok JJ.
europepmc +3 more sources
Minimal model dependent constraints on cosmological nuisance parameters and cosmic curvature from combinations of cosmological data [PDF]
International Journal of Modern Physics D, 2022 The study of cosmic expansion history and the late time cosmic acceleration from observational data depends on the nuisance parameters associated with the data.
Bikash Ranjan Dinda
semanticscholar +1 more source
The Costs and Benefits of Uniformly Valid Causal Inference with High-Dimensional Nuisance Parameters [PDF]
Statistical Science, 2021 Important advances have recently been achieved in developing procedures yielding uniformly valid inference for a low dimensional causal parameter when high-dimensional nuisance models must be estimated.
Niloofar Moosavi +2 more
semanticscholar +1 more source
Specification Tests for GARCH Processes with Nuisance Parameters on the Boundary [PDF]
Social Science Research Network, 2021 This article develops tests for the correct specification of the conditional variance function in GARCH models when the true parameter may lie on the boundary of the parameter space. The test statistics considered are of Kolmogorov-Smirnov and Cramér-von
Giuseppe Cavaliere +2 more
semanticscholar +1 more source
Unified Bayesian theory of sparse linear regression with nuisance parameters
Electronic Journal of Statistics, 2021 We study frequentist asymptotic properties of Bayesian procedures for high-dimensional Gaussian sparse regression when unknown nuisance parameters are involved. Nuisance parameters can be finite-, high-, or infinite-dimensional. A mixture of point masses
Seonghyun Jeong, S. Ghosal
semanticscholar +1 more source
Optimal Statistical Inference in the Presence of Systematic Uncertainties Using Neural Network Optimization Based on Binned Poisson Likelihoods with Nuisance Parameters [PDF]
Computing and Software for Big Science, 2020 Data analysis in science, e.g., high-energy particle physics, is often subject to an intractable likelihood if the observables and observations span a high-dimensional input space.
Stefan Wunsch +3 more
semanticscholar +1 more source
Profile Likelihood for Hierarchical Models Using Data Doubling
Entropy, 2023 In scientific problems, an appropriate statistical model often involves a large number of canonical parameters. Often times, the quantities of scientific interest are real-valued functions of these canonical parameters.
Subhash R. Lele
doaj +1 more source
Constraining single-field inflation with MegaMapper
Physics Letters B, 2023 We forecast the constraints on single-field inflation from the bispectrum of future high-redshift surveys such as MegaMapper. Considering non-local primordial non-Gaussianity (NLPNG), we find that current methods will yield constraints of order σ(fNLeq ...
Giovanni Cabass +4 more
doaj +1 more source
Quantum state estimation with nuisance parameters [PDF]
Journal of Physics A: Mathematical and Theoretical, 2019 In parameter estimation, nuisance parameters refer to parameters that are not of interest but nevertheless affect the precision of estimating other parameters of interest.
J. Suzuki +2 more
semanticscholar +1 more source
Atmosphere, 2021 Prolonged exposure to odour emissions causes annoyance which leads to nuisance and consequently to complaints. Different methodologies exist in the literature to evaluate odour impacts, but not all are suitable to assess environmental odour nuisance ...
Tiziano Zarra +2 more
doaj +1 more source