Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 10,000 papers for “Estimation”

Mean estimation through proportion estimation

Juan Carlos Rodrı́guez · Journal of nonparametric statistics · 2013

In this work a device which changes the problem of mean estimation into that of proportion estimation is proposed. The device consists of perturbing the observations. The goal of the work is the construction of conservative confidence intervals for means. Three applications are given: (1) proportion estimation in the context of cluster random sampling, (2) differences of proportions of a multinomial population and (3) variance estimation.

Cited by 0

Estimation of variance estimation error.

David Augustin (15811009), Ben Lambert (5060288), Ken Wang (4517578) et al. · Figshare · 2023

<div><p>Variability is an intrinsic property of biological systems and is often at the heart of their complex behaviour. Examples range from cell-to-cell variability in cell signalling pathways to variability in the response to treatment across patients. A popular approach to model and understand this variability is nonlinear mixed effects (NLME) modelling. However, estimating the parameters of NLME models from measurements quickly becomes computationally expensive as the number of measured individuals grows, making NLME inference intractable for datasets with thousands of measured individuals. This shortcoming is particularly limiting for snapshot datasets, common e.g. in cell biology, where high-throughput measurement techniques provide large numbers of single cell measurements. We introduce a novel approach for the estimation of NLME model parameters from snapshot measurements, which we call filter inference. Filter inference uses measurements of simulated individuals to define an approximate likelihood for the model parameters, avoiding the computational limitations of traditional NLME inference approaches and making efficient inferences from snapshot measurements possible. Filter inference also scales well with the number of model parameters, using state-of-the-art gradient-based MCMC algorithms such as the No-U-Turn Sampler (NUTS). We demonstrate the properties of filter inference using examples from early cancer growth modelling and from epidermal growth factor signalling pathway modelling.</p></div>

Cited by 0Open access

Estimation

Hans‐Michael Kaltenbach · SpringerBriefs in statistics · 2011

Cited by 0

Estimation

Rok Istenic, Ales Holobar, Marco Gazzoni et al. · 2015

Abstract: The aim of this paper is to introduce an extension to a force estimation technique based on activity index and to compare it to two other muscle force estimation techniques that also use the motor control information on the same set of surface EMG signals. Our new method is called motor unit twitch force technique and the compared methods include motor unit action potential rate and activity index. The main difference of the three compared methods lies in the extraction of the motor control information from multi-channel surface EMG. Motor unit action potential rate and activity index measure global muscle activity as they represent the summation of innervation pulse trains of all active motor units, while twitch force technique decomposes the surface EMG and obtains the activity of all active individual motor units separately. This means a great improvement over activity index and motor unit action potential rate methods as both force regulation principles, i.e. motor unit recruitment and firing rate modulation can be observed. Surface EMG signals used in the experiment were recorded from biceps brachii muscle during elbow flexion on five subjects. Two-dimensional matrix of surface electrodes (13 rows by 5 columns) was applied. Isometric constant force contractions at three different force levels were performed, i.e. at 5, 10 and 30 % of maximal voluntary contraction. Torque produced at the elbow joint was measured simultaneously with surface EMG. The force estimation error of the methods was measured by root mean square error between the recorded and estimated force. Our new motor unit twitch force technique reduced the muscle force estimation error significantly, for 13 % when compared to the motor unit

Cited by 0

Estimation

Marc Nerlove, Francis X. Diebold · The New Palgrave Dictionary of Economics · 2018

Point estimation concerns making inferences about a quantity that is unknown but about which some information is available, e.g., a fixed quantity θ for which we have n imperfect measurements x1,…,xn) The theory of estimation deals with how best to use the information (combine the values x1,…,xn) to obtain a single number, estimate, for θ, say $$ \widehat{\theta} $$. Interval estimation does not reduce the available information to a single number and is a special case of hypothesis testing. This entry deals only with point estimation.

Cited by 0

Estimation

Pierre Moulin, Venugopal V. Veeravalli · Cambridge University Press eBooks · 2018

This book is a mathematically accessible and up-to-date introduction to the tools needed to address modern inference problems in engineering and data science, ideal for graduate students taking courses on statistical inference and detection and estimation, and an invaluable reference for researchers and professionals. With a wealth of illustrations and examples to explain the key features of the theory and to connect with real-world applications, additional material to explore more advanced concepts, and numerous end-of-chapter problems to test the reader's knowledge, this textbook is the 'go-to' guide for learning about the core principles of statistical inference and its application in engineering and data science. The password-protected solutions manual and the image gallery from the book are available online.

Cited by 0

Estimation

Sabee Molloi, David Chalyan, Huy Le et al. · 2013

Cited by 0

Estimation

Abhinav Gupta, Trista Chen, Francine Chen et al. · 2013

Current approaches to pose estimation and tracking can be classified into two categories: generative and discriminative. While generative approaches can accurately determine human pose from image observations, they are computationally expensive due to search in the high dimensional human pose space. On the other hand, discriminative approaches do not generalize well, but are computationally efficient. We present a hybrid model that combines the strengths of the two in an integrated learning and inference framework. We extend the Gaussian process latent variable model (GPLVM) to include an embedding from observation space (the space of image features) to the latent space. GPLVM is a generative model, but the inclusion of this mapping provides a discriminative component,

Cited by 0

Estimation

Dharmaraja Selvamuthu, Dipayan Das · University texts in the mathematical sciences. · 2024

Cited by 0

Estimation

J. Jack Lee, Cheng-Few Lee · 2022

Cited by 0

Estimation

Rice University, Hongxiao Zhu, Jong Soo Lee et al. · 2008

Cited by 0

estimation

James Saunderson, Pablo A. Parrilo, Alan S. Willsky · 2014

Cited by 0

estimation

G Mauro D’ariano, Lorenzo Maccone, Matteo Gaparis · 2000

Quantum tomography is the process of reconstructing the ensemble average of an arbitrary operator (observable or not, including the density matrix), which may not be directly accessible by feasible detection schemes, starting from the measurement of a complete set of observables i.e. a quorum. The measurement of a quorum thus represents a complete characterization of the quantum state. The operator expression in terms of a quorum corresponds to an expansion on an irreducible set of operators in the Liouville space. We give two general characterizations of these sets, and show that all the known quantum tomographies can be described in this framework. New operatorial resolutions are also given that may be used in novel reconstruction schemes. PACS numbers: 0530C, 0220, 0367 1.

Cited by 0