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We present a new method to estimate shear measurement bias in image simulations that significantly improves the precision with respect to current techniques. Our method is based on measuring the shear response for individual images. We…

Cosmology and Nongalactic Astrophysics · Physics 2018-12-19 Arnau Pujol , Martin Kilbinger , Florent Sureau , Jerome Bobin

Due to measurement noise, a common problem in in various fields is how to estimate the ratio of two functions. We consider this problem of estimating the ratio of two functions in a nonparametric regression model. Assuming the noise is…

Methodology · Statistics 2013-11-28 Jelena Markovic , Lie Wang

This paper proposes an automatic procedure, based on ROOT data Analysis Framework, for the analysis of Silicon Photomultipliers (SiPM) characteristics. In particular, it can be used to analyze experimental waveforms, from oscilloscope,…

Instrumentation and Detectors · Physics 2019-07-10 Andrii Nagai , Nicoleta Dinu-Jaeger , Adam Para

This paper develops a Bayesian approach for assessing equivalence and non-inferiority hypotheses in two-arm trials using relative belief ratios. A relative belief ratio is a measure of statistical evidence and can indicate evidence either…

Applications · Statistics 2014-01-20 Saman Muthukumarana , Michael Evans

Scanning electron microscopy (SEM) is a versatile technique used to image samples at the nanoscale. Conventional imaging by this technique relies on finding the average intensity of the signal generated on a detector by secondary electrons…

Instrumentation and Detectors · Physics 2021-11-04 Akshay Agarwal , John Simonaitis , Vivek K. Goyal , Karl K. Berggren

Determining the adsorption isotherms is an issue of significant importance in preparative chromatography. A modern technique for estimating adsorption isotherms is to solve an inverse problem so that the simulated batch separation coincides…

Applications · Statistics 2022-11-24 Jiaji Su , Zhigang Yao , Cheng Li , Ye Zhang

Nonlinear combinations of direct observables are often used to estimate quantities of theoretical interest. Without sufficient caution, this could lead to biased estimations. An example of great interest is the skewness $S_3$ of the galaxy…

Astrophysics · Physics 2009-10-31 Lam Hui , Enrique Gaztanaga

Despite the growing demand for accurate surface normal estimation models, existing methods use general-purpose dense prediction models, adopting the same inductive biases as other tasks. In this paper, we discuss the inductive biases needed…

Computer Vision and Pattern Recognition · Computer Science 2024-03-04 Gwangbin Bae , Andrew J. Davison

Bilayer intelligent omni-surface (BIOS) has recently attracted increasing attention due to its capability of independent beamforming on both reflection and refraction sides. However, its specific bilayer structure makes the channel…

Information Theory · Computer Science 2023-02-17 Qiucen Wu , Tian Lin , Yu Zhu

The remarkable sensitivity achieved by the planned Laser Interferometer Space Antenna (LISA) will allow us to observe gravitational-wave signals from the mergers of massive black hole binaries (MBHBs) with signal-to-noise ratio (SNR) in the…

General Relativity and Quantum Cosmology · Physics 2025-12-22 Sophia Yi , Francesco Iacovelli , Sylvain Marsat , Digvijay Wadekar , Emanuele Berti

Secondary electron (SE) imaging techniques, such as scanning electron microscopy and helium ion microscopy (HIM), use electrons emitted by a sample in response to a focused beam of charged particles incident at a grid of raster scan…

Signal Processing · Electrical Eng. & Systems 2026-03-12 Vaibhav Choudhary , Akshay Agarwal , Vivek K Goyal

Hardness ratios are commonly used in X-ray photometry to indicate spectral properties roughly. It is usually defined as the ratio of counts in two different wavebands. This definition, however, is problematic when the counts are very…

Astrophysics · Physics 2008-11-26 Y. K. Jin , S. N. Zhang , J. F. Wu

Several problems in statistics involve the combination of high-variance unbiased estimators with low-variance estimators that are only unbiased under strong assumptions. A notable example is the estimation of causal effects while combining…

Methodology · Statistics 2023-05-25 Michael Oberst , Alexander D'Amour , Minmin Chen , Yuyan Wang , David Sontag , Steve Yadlowsky

The manuscript discusses how to incorporate random effects for quantile regression models for clustered data with focus on settings with many but small clusters. The paper has three contributions: (i) documenting that existing methods may…

Methodology · Statistics 2022-02-24 Maria Laura Battagliola , Helle Sørensen , Anders Tolver , Ana-Maria Staicu

The distributed Hill estimator is a divide-and-conquer algorithm for estimating the extreme value index when data are stored in multiple machines. In applications, estimates based on the distributed Hill estimator can be sensitive to the…

Methodology · Statistics 2021-12-21 Liujun Chen , Deyuan Li , Chen Zhou

A nonparametric kernel density estimator for directional-linear data is introduced. The proposal is based on a product kernel accounting for the different nature of both (directional and linear) components of the random vector. Expressions…

Microdosimetry investigates the energy deposition of ionizing radiation at microscopic scales, beyond the assessment capabilities of macroscopic dosimetry. This contributes to an understanding of the biological response in radiobiology,…

Empirical researchers often trim observations with small denominator A when they estimate moments of the form E[B/A]. Large trimming is a common practice to mitigate variance, but it incurs large trimming bias. This paper provides a novel…

Methodology · Statistics 2021-01-12 Yuya Sasaki , Takuya Ura

Spectral estimation (SE) aims to identify how the energy of a signal (e.g., a time series) is distributed across different frequencies. This can become particularly challenging when only partial and noisy observations of the signal are…

Machine Learning · Statistics 2019-01-15 Felipe Tobar

Welch's method provides an estimator of the power spectral density that is statistically consistent. This is achieved by averaging over periodograms calculated from overlapping segments of a time series. For a finite length time series,…

Methodology · Statistics 2024-04-12 Lachlan C. Astfalck , Adam M. Sykulski , Edward J. Cripps