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Abstract Optical absorption and scattering properties are often estimated from the diffusive reflection light intensity at only one distance from the material surface, which often encounters accuracy and convergence issues. In this work, a method was proposed to determine optical properties by using diffusive reflection light intensity profiles at multiple distances, which enhanced data richness as a result of the intensity profiles are linearly independent. In this method, five features of light intensity profiles (contrast, correlation, energy, homogeneity, and second moment) were used to reduce the data dimensions. To demonstrate the effectiveness of the proposed method, Monte Carlo (MC) simulations were used to generate diffusive reflection light intensity profiles with noise at different distances for various combinations of four optical properties (absorption coefficientμa, scattering coefficientμs, isotropic coefficientg, and refractive indexn). The five profile feature vectors were used as inputs and the four optical parameters were used as outputs to train and test a backpropagation (BP) neural network. The influences of noise levels and the number of diffusive light intensity profiles on parameter estimation accuracy were investigated. The four optical parameters estimated by the BP network were compared with the results estimated by the traditional least squares method, which shows that the proposed method can estimate the optical properties with higher accuracy and better convergence.
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Speeding up Monte Carlo simulations for the adaptive sum of powered score test with importance sampling
Abstract A central but challenging problem in genetic studies is to test for (usually weak) associations between a complex trait (e.g., a disease status) and sets of multiple genetic variants. Due to the lack of a uniformly most powerful test, data‐adaptive tests, such as the adaptive sum of powered score (aSPU) test, are advantageous in maintaining high power against a wide range of alternatives. However, there is often no closed‐form to accurately and analytically calculate thep‐values of many adaptive tests like aSPU, thus Monte Carlo (MC) simulations are often used, which can be time consuming to achieve a stringent significance level (e.g., 5e‐8) used in genome‐wide association studies (GWAS). To estimate such a smallp‐value, we need a huge number of MC simulations (e.g., 1e+10). As an alternative, we propose using importance sampling to speed up such calculations. We develop some theory to motivate a proposed algorithm for the aSPU test, and show that the proposed method is computationally more efficient than the standard MC simulations. Using both simulated and real data, we demonstrate the superior performance of the new method over the standard MC simulations.
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- PAR ID:
- 10364635
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Biometrics
- Volume:
- 78
- Issue:
- 1
- ISSN:
- 0006-341X
- Format(s):
- Medium: X Size: p. 261-273
- Size(s):
- p. 261-273
- Sponsoring Org:
- National Science Foundation
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