Relevant bibliographies by topics / Stochastic errors

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Stochastic errors'

Author: Grafiati
Published: 3 June 2025
Last updated: 22 June 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Stochastic errors.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Stochastic errors"

1

Lv, Pin, Jizhou Lai, Jianye Liu, and Mengxin Nie. "The Compensation Effects of Gyros' Stochastic Errors in a Rotational Inertial Navigation System." Journal of Navigation 67, no. 6 (2014): 1069–88. http://dx.doi.org/10.1017/s0373463314000319.

Full text
Abstract:
The errors of an inertial navigation system (INS) in response to gyros' errors can be effectively reduced by the rotation technique, which is a commonly used method to improve an INS's accuracy. A gyro's error consists of a deterministic contribution and a stochastic contribution. The compensation effects of gyros' deterministic errors are clear now, but the compensation effects of gyros' stochastic errors are as yet unknown. However, the compensation effects are always needed in a rotational inertial navigation system's (RINS) error analysis and optimization study. In this paper, the compensation effects of gyros' stochastic errors, which are modelled as a Gaussian white (GW) noise plus a first-order Markov process, are analysed and the specific formulae are derived. During the research, the responses of an INS's and a RINS's position error equations to gyros' stochastic errors are first analysed. Then the compensation effects of gyros' stochastic errors brought by the rotation technique are discussed by comparing the error propagation characteristics in an INS and a RINS. In order to verify the theory, a large number of simulations are carried out. The simulation results show a good consistency with the derived formulae, which can indicate the correctness of the theory.
APA, Harvard, Vancouver, ISO, and other styles
2

Zheng, Zhichao, Songlai Han, Jin Yue, and Linglong Yuan. "Compensation for Stochastic Error of Gyros in a Dual-axis Rotational Inertial Navigation System." Journal of Navigation 69, no. 1 (2015): 169–82. http://dx.doi.org/10.1017/s037346331500051x.

Full text
Abstract:
A dual-axis rotational Inertial Navigation System (INS) has received wide attention in recent years because of high performance and low cost. However, some errors of inertial sensors such as stochastic errors are not averaged out automatically during navigation. Therefore a Twice Position-fix Reset (TPR) method is provided to enhance accuracy of a dual-axis rotational INS by compensating stochastic errors. According to characteristics of an azimuth error introduced by stochastic errors of an inertial sensor in the dual-axis rotational INS, both an azimuth error and a radial-position error are much better corrected by the TPR method based on an optimised error propagation equation. As a result, accuracy of the dual-axis rotational INS is prominently enhanced by the TPR method, as is verified by simulations and field tests.
APA, Harvard, Vancouver, ISO, and other styles
3

Ozendi, M., D. Akca, and H. Topan. "STOCHASTIC SURFACE MESH RECONSTRUCTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 805–12. http://dx.doi.org/10.5194/isprs-archives-xlii-2-805-2018.

Full text
Abstract:
A generic and practical methodology is presented for 3D surface mesh reconstruction from the terrestrial laser scanner (TLS) derived point clouds. It has two main steps. The first step deals with developing an anisotropic point error model, which is capable of computing the theoretical precisions of 3D coordinates of each individual point in the point cloud. The magnitude and direction of the errors are represented in the form of error ellipsoids. The following second step is focused on the stochastic surface mesh reconstruction. It exploits the previously determined error ellipsoids by computing a point-wise quality measure, which takes into account the semi-diagonal axis length of the error ellipsoid. The points only with the least errors are used in the surface triangulation. The remaining ones are automatically discarded.
APA, Harvard, Vancouver, ISO, and other styles
4

Grooms, I., Y. Lee, and A. J. Majda. "Ensemble Filtering and Low-Resolution Model Error: Covariance Inflation, Stochastic Parameterization, and Model Numerics." Monthly Weather Review 143, no. 10 (2015): 3912–24. http://dx.doi.org/10.1175/mwr-d-15-0032.1.

Full text
Abstract:
Abstract The use of under-resolved models in ensemble data assimilation schemes leads to two kinds of model errors: truncation errors associated with discretization of the large-scale dynamics and errors associated with interactions with subgrid scales. Multiplicative and additive covariance inflation can be used to account for model errors in ensemble Kalman filters, but they do not reduce the model error. Truncation errors can be reduced by increasing the accuracy of the numerical discretization of the large-scale dynamics, and subgrid-scale parameterizations can reduce errors associated with subgrid-scale interactions. Stochastic subgrid-scale parameterizations both reduce the model error and inflate the ensemble spread, so their effectiveness in ensemble assimilation schemes can be gauged by comparing with covariance inflation techniques. The effects of covariance inflation, stochastic parameterizations, and model numerics in two-layer periodic quasigeostrophic turbulence are compared on an f plane and on a β plane. The stochastic backscatter schemes used here model backscatter in the inverse cascade regime of quasigeostrophic turbulence, as appropriate to eddy-permitting ocean models. Covariance inflation improves the performance of a benchmark model with no parameterizations and second-order numerics. Fourth-order spatial discretization and the stochastic parameterizations, alone and in combination, are superior to covariance inflation. In these experiments fourth-order numerics and stochastic parameterizations lead to similar levels of improvement in filter performance even though the climatology of models without stochastic parameterizations is poor.
APA, Harvard, Vancouver, ISO, and other styles
5

Ijaz, Muhammad, Syed Azhar Ali Zaidi, and Aamir Rashid. "Uniform patterns based area-efficient and accurate stochastic computing finite impulse response filter." PLOS ONE 16, no. 1 (2021): e0245943. http://dx.doi.org/10.1371/journal.pone.0245943.

Full text
Abstract:
Stochastic computing has recently gained attention due to its low hardware complexity and better fault tolerance against soft errors. However, stochastic computing based circuits suffer from different errors which affect the output accuracy of these circuits. In this paper, an accurate and area-efficient stochastic computing based digital finite impulse response filter is designed. In the proposed work, constant uniform patterns are used as stochastic numbers for the select lines of different MUXes in the filter and the error performance of filter is analysed. Based on the error performance, the combinations of these patterns are proposed for reducing the output error of stochastic computing based filters. The architectures for generating these uniform patterns are also proposed. Results show that the proposed design methodology has better error performance and comparable hardware complexity as compared to the state-of-the-art implementations.
APA, Harvard, Vancouver, ISO, and other styles
6

Cheng, Qiang, Can Wu, Peihua Gu, Wenfen Chang, and Dongsheng Xuan. "An Analysis Methodology for Stochastic Characteristic of Volumetric Error in Multiaxis CNC Machine Tool." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/863283.

Full text
Abstract:
Traditional approaches about error modeling and analysis of machine tool few consider the probability characteristics of the geometric error and volumetric error systematically. However, the individual geometric error measured at different points is variational and stochastic, and therefore the resultant volumetric error is aslo stochastic and uncertain. In order to address the stochastic characteristic of the volumetric error for multiaxis machine tool, a new probability analysis mathematical model of volumetric error is proposed in this paper. According to multibody system theory, a mean value analysis model for volumetric error is established with consideration of geometric errors. The probability characteristics of geometric errors are obtained by statistical analysis to the measured sample data. Based on probability statistics and stochastic process theory, the variance analysis model of volumetric error is established in matrix, which can avoid the complex mathematics operations during the direct differential. A four-axis horizontal machining center is selected as an illustration example. The analysis results can reveal the stochastic characteristic of volumetric error and are also helpful to make full use of the best workspace to reduce the random uncertainty of the volumetric error and improve the machining accuracy.
APA, Harvard, Vancouver, ISO, and other styles
7

Zhu, Luhua, and Erlei Yao. "An Improved Hilbert Spectral Representation Method for Synthesizing Spatially Correlated Earthquake Ground Motions and Its Error Assessment." Mathematical Problems in Engineering 2020 (May 16, 2020): 1–21. http://dx.doi.org/10.1155/2020/2127374.

Full text
Abstract:
This paper is an extension of the random amplitude-based improved Hilbert spectral representation method (IHSRM) that the authors developed previously for the simulation of spatially correlated earthquake ground motions (SCEGMs) possessing the nonstationary characteristics of the natural earthquake record. In fact, depending on the fundamental types (random phase method and random amplitude method) and matrix decomposition methods (Cholesky decomposition, root decomposition, and eigendecomposition), the IHSRM possesses various types. To evaluate the influence of different types of this method on the statistic errors, i.e., bias errors and stochastic errors, an error assessment for this method was conducted. First, the random phase-based IHSRM was derived, and its reliability was proven by theoretical deduction. Unified formulas were given for random phase- and random amplitude-based IHSRMs, respectively. Then, the closed-form solutions of statistic errors of simulated seismic motions were derived. The validness of the proposed closed-form solutions was proven by comparing the closed-form solutions with estimated values. At last, the stochastic errors of covariance (i.e., variance and cross-covariance) for different types of IHSRMs were compared, and the results showed that (1) the proposed IHSRM is not ergodic; (2) the random amplitude-based IHSRMs possessed higher stochastic errors of covariance than the random phase-based IHSRMs; and (3) the value of the stochastic error of covariance for the random phase-based IHSRM is dependent on the matrix decomposition method, while that for the random amplitude-based one is not.
APA, Harvard, Vancouver, ISO, and other styles
8

Anant, Venkat, and Roland Priemer. "Adjacent errors of stochastic gradient algorithms under general error criteria." Computer Standards & Interfaces 20, no. 6-7 (1999): 475. http://dx.doi.org/10.1016/s0920-5489(99)91053-x.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Doerr, Daniel, Ilan Gronau, Shlomo Moran, and Irad Yavneh. "Stochastic errors vs. modeling errors in distance based phylogenetic reconstructions." Algorithms for Molecular Biology 7, no. 1 (2012): 22. http://dx.doi.org/10.1186/1748-7188-7-22.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Alderov, Zaur A., Evgeny V. Rozengauz, and Denis Nesterov. "How CT reconstruction parameters effect measurement error of pulmonary nodules volume." HERALD of North-Western State Medical University named after I.I. Mechnikov 12, no. 3 (2020): 73–77. http://dx.doi.org/10.17816/mechnikov44920.

Full text
Abstract:
One of the the widely used way to follow up oncological disease is estimation of lesion size differences. Volumetry is one of the most accurate approaches of lesion size estimation. However, being highly sensitive, volumetric errors can reach 60%, which significantly limits the applicability of the method.
 Purpose was to estimate the effect of reconstruction parameters on volumetry error.
 Materials and methods. 32 patients with pulmonary metastases underwent a CT scanning with 326 foci detected. 326 pulmonary were segmented. Volumetry error was estimated for every lesion with each combination of slice thickness and reconstruction kernel. The effect was measured with linear regression analysis
 Results. Systematic and stochastic errors are impacted by slice thickness, reconstruction kernel, lesion position and its diameter. FC07 kernel and larger slice thickness is associated with high systematic error. Both systematic and stochastic errors decrease with lesion enlargment. intrapulmonary lesions have the lowest error regardless the reconstruction parameters.
 Lineal regression model was created to prognose error rate. Model standart error was 6.7%. There was corelation between model remnants deviation and slice thickness, reconstruction kernel, lesion position and its diameter.
 Conclusion. The systematic error depends on the focal diameter, slice thickness and reconstruction kernel. It can be estimated using the proposed model with a 6% error. Stochastic error mainly depends on lesion size.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Stochastic errors"

1

El, Arar El-Mehdi. "Stochastic models for the evaluation of numerical errors." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG104.

Full text
Abstract:
L'idée de considérer les erreurs d'arrondi comme des variables aléatoires n'est pas nouvelle. Basées sur des outils tels que l'indépendance des variables aléatoires ou le théorème central limite, plusieurs propositions ont démontré des bornes d'erreur en O(√n). Cette thèse est dédiée à l'étude de l'arrondi stochastique (SR) en tant que remplaçant du mode d'arrondi déterministe par défaut. Tout d'abord, nous introduisons une nouvelle approche pour dériver une borne probabiliste de l'erreur en O(√n), basée sur le calcul de la variance et l'inégalité de Bienaymé-Chebyshev. Ensuite, nous développons un cadre général permettant l'analyse probabiliste des erreurs des algorithmes sous SR. Dans ce contexte, nous décomposons l'erreur en une martingale plus un biais. Nous montrons que le biais est nul pour les algorithmes présentant des erreurs multilinéaires, tandis que l'analyse probabiliste de la martingale conduit à des bornes probabilistes de l'erreur en O(√n). Pour le calcul de la variance, nous montrons que le biais est négligeable au premier ordre par rapport à la martingale, et nous prouvons des bornes probabilistes de l'erreur en O(√n)<br>The idea of assuming rounding errors as random variables is not new. Based on tools such as independent random variables or the Central Limit Theorem, various propositions have demonstrated error bounds in O(√n). This thesis is dedicated to studying stochastic rounding (SR) as a replacement for the default deterministic rounding mode. First, we introduce a new approach to derive a probabilistic error bound in O(√n) based on variance calculation and Bienaymé-Chebyshev inequality. Second, we demonstrate a general framework that allows the probabilistic error analysis of algorithms under SR. In this context, we decompose the error into a martingale plus a drift. We show that the drift is zero for algorithms with multi-linear errors, while the probabilistic analysis of the martingale term leads to probabilistic error bounds in O(√n). We show that the drift is negligible at the first order compared to the martingale term for the variance computation, and we prove probabilistic error bounds in O(√n)
APA, Harvard, Vancouver, ISO, and other styles
2

Mårtensson, Jonas. "Geometric analysis of stochastic model errors in system identification." Doctoral thesis, KTH, Reglerteknik, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4506.

Full text
Abstract:
Models of dynamical systems are important in many disciplines of science, ranging from physics and traditional mechanical and electrical engineering to life sciences, computer science and economics. Engineers, for example, use models for development, analysis and control of complex technical systems. Dynamical models can be derived from physical insights, for example some known laws of nature, (which are models themselves), or, as considered here, by fitting unknown model parameters to measurements from an experiment. The latter approach is what we call system identification. A model is always (at best) an approximation of the true system, and for a model to be useful, we need some characterization of how large the model error is. In this thesis we consider model errors originating from stochastic (random) disturbances that the system was subject to during the experiment. Stochastic model errors, known as variance-errors, are usually analyzed under the assumption of an infinite number of data. In this context the variance-error can be expressed as a (complicated) function of the spectra (and cross-spectra) of the disturbances and the excitation signals, a description of the true system, and the model structure (i.e., the parametrization of the model). The primary contribution of this thesis is an alternative geometric interpretation of this expression. This geometric approach consists in viewing the asymptotic variance as an orthogonal projection on a vector space that to a large extent is defined from the model structure. This approach is useful in several ways. Primarily, it facilitates structural analysis of how, for example, model structure and model order, and possible feedback mechanisms, affect the variance-error. Moreover, simple upper bounds on the variance-error can be obtained, which are independent of the employed model structure. The accuracy of estimated poles and zeros of linear time-invariant systems can also be analyzed using results closely related to the approach described above. One fundamental conclusion is that the accuracy of estimates of unstable poles and zeros is little affected by the model order, while the accuracy deteriorates fast with the model order for stable poles and zeros. The geometric approach has also shown potential in input design, which treats how the excitation signal (input signal) should be chosen to yield informative experiments. For example, we show cases when the input signal can be chosen so that the variance-error does not depend on the model order or the model structure. Perhaps the most important contribution of this thesis, and of the geometric approach, is the analysis method as such. Hopefully the methodology presented in this work will be useful in future research on the accuracy of identified models; in particular non-linear models and models with multiple inputs and outputs, for which there are relatively few results at present.<br>QC 20100810
APA, Harvard, Vancouver, ISO, and other styles
3

Mårtensson, Jonas. "Geometric analysis of stochastic model errors in system identification /." Stockholm : Elektro- och systemteknik, Kungliga Tekniska högskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4506.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Rama, Vishal. "Estimating stochastic volatility models with student-t distributed errors." Master's thesis, Faculty of Science, 2020. http://hdl.handle.net/11427/32390.

Full text
Abstract:
This dissertation aims to extend on the idea of Bollerslev (1987), estimating ARCH models with Student-t distributed errors, to estimating Stochastic Volatility (SV) models with Student-t distributed errors. It is unclear whether Gaussian distributed errors sufficiently account for the observed leptokurtosis in financial time series and hence the extension to examine Student-t distributed errors for these models. The quasi-maximum likelihood estimation approach introduced by Harvey (1989) and the conventional Kalman filter technique are described so that the SV model with Gaussian distributed errors and SV model with Student-t distributed errors can be estimated. Estimation of GARCH (1,1) models is also described using the method maximum likelihood. The empirical study estimated four models using data on four different share return series and one index return, namely: Anglo American, BHP, FirstRand, Standard Bank Group and JSE Top 40 index. The GARCH and SV model with Student-t distributed errors both perform best on the series examined in this dissertation. The metric used to determine the best performing model was the Akaike information criterion (AIC).
APA, Harvard, Vancouver, ISO, and other styles
5

Ketata, Chefi. "Knowledge-assisted stochastic evaluation of sampling errors in mineral processing streams." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq39321.pdf.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Van, Langenhove Jan Willem. "Adaptive control of deterministic and stochastic approximation errors in simulations of compressible flow." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066357/document.

Full text
Abstract:
La simulation de systèmes d'ingénierie non linéaire complexes tels que les écoulements de fluide compressibles peut être ciblée pour rendre plus efficace et précise l'approximation d'une quantité spécifique (scalaire) d'intérêt du système. En mettant de côté l'erreur de modélisation et l'incertitude paramétrique, on peut y parvenir en combinant des estimations d'erreurs axées sur des objectifs et des raffinements adaptatifs de maillage spatial anisotrope. A cette fin, un cadre élégant et efficace est celui de l'adaptation dite basé-métrique où une estimation d'erreur a priori est utilisée comme indicateur d’adaptation de maillage. Dans cette thèse on propose une nouvelle extension de cette approche au cas des approximations de système portant une composante stochastique. Dans ce cas, un problème d'optimisation est formulé et résolu pour un meilleur contrôle des sources d'erreurs. Ce problème est posé dans le cadre continu de l'espace de métrique riemannien. Des développements algorithmiques sont également proposés afin de déterminer les sources dominates d’erreur et effectuer l’adaptation dans les espaces physique ou des paramètres incertains. L’approche proposé est testée sur divers problèmes comprenant une entrée de scramjet supersonique soumise à des incertitudes paramétriques géométriques et opérationnelles. Il est démontré que cette approche est capable de bien capturé les singularités dans l’escape stochastique, tout en équilibrant le budget de calcul et les raffinements de maillage dans les deux espaces<br>The simulation of complex nonlinear engineering systems such as compressible fluid flows may be targeted to make more efficient and accurate the approximation of a specific (scalar) quantity of interest of the system. Putting aside modeling error and parametric uncertainty, this may be achieved by combining goal-oriented error estimates and adaptive anisotropic spatial mesh refinements. To this end, an elegant and efficient framework is the one of (Riemannian) metric-based adaptation where a goal-based a priori error estimation is used as indicator for adaptivity. This thesis proposes a novel extension of this approach to the case of aforementioned system approximations bearing a stochastic component. In this case, an optimisation problem leading to the best control of the distinct sources of errors is formulated in the continuous framework of the Riemannian metric space. Algorithmic developments are also presented in order to quantify and adaptively adjust the error components in the deterministic and stochastic approximation spaces. The capability of the proposed method is tested on various problems including a supersonic scramjet inlet subject to geometrical and operational parametric uncertainties. It is demonstrated to accurately capture discontinuous features of stochastic compressible flows impacting pressure-related quantities of interest, while balancing computational budget and refinements in both spaces
APA, Harvard, Vancouver, ISO, and other styles
7

Wall, John H. "A study of the effects of stochastic inertial sensor errors in dead-reckoning navigation." Auburn, Ala., 2007. http://repo.lib.auburn.edu/07M%20Theses/WALL_JOHN_59.pdf.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Everitt, Niklas. "Identification of Modules in Acyclic Dynamic Networks A Geometric Analysis of Stochastic Model Errors." Licentiate thesis, KTH, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-159698.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Nguyen, Ngoc B. "Estimation of Technical Efficiency in Stochastic Frontier Analysis." Bowling Green State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1275444079.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Bottegal, Giulio. "Modeling, estimation and identification of stochastic systems with latent variables." Doctoral thesis, Università degli studi di Padova, 2013. http://hdl.handle.net/11577/3423358.

Full text
Abstract:
The main topic of this thesis is the analysis of static and dynamic models in which some variables, although directly influencing the behavior of certain observables, are not accessible to measurements. These models find applications in many branches of science and engineering, such as control systems, communications, natural and biological sciences and econometrics. It is well-known that models with unaccessible - or latent - variables, usually suffer from a lack of uniqueness of representation. In other words, there are in general many models of the same type describing a given set of observables say, the measurable input-output variables. This is well-known and has been well-studied for a special class of linear models, called state-space models. In this thesis we shall focus on two particular classes of stochastic systems with latent variables: the generalized factor analysis models and errors-in-variables models. For these classes of models there are still some unresolved issues related to non-uniqueness of the representation and clarifying these issues is of paramount importance for their identification. Since mathematical models usually need to be estimated from experimental data, solving the non-uniqueness problem is essential for their use in statistical inference (system identification) from measured data.<br>L’argomento principale di questa tesi è l’analisi di modelli statici e dinamici in cui alcune variabili non sono accessibili a misurazioni, nonostante esse influenzino l’evoluzione di certe osservazioni. Questi modelli trovano applicazione in molte discipline delle scienze e dell’ingegneria, come ad esempio l’automatica, le telecomunicazioni, le scienze naturali, la biologia e l’econometria e sono stati studiati approfonditamente nel campo dell’identificazione dei modelli. E' ben noto che sistemi con variabili inaccessibili - o latenti, spesso soffrono di una mancanza di unicità nella rappresentazione. In altre parole, in generale ci sono molti modelli dello stesso tipo che possono descrivere un dato insieme di osservazioni, come ad esempio variabili misurabili di ingresso-uscita. Questo è ben noto, ed è stato studiato a fondo per una classe speciale di modelli lineari, chiamata modelli a spazio di stato. In questa tesi ci si focalizza su due classi particolari di sistemi stocastici a variabili latenti: i modelli generalized factor analysis e i modelli errors-in-variables. Per queste classi di modelli ci sono ancora alcuni problemi irrisolti legati alla non unicità della rappresentazione e chiarificare questi problemi è di importanza fondamentale per la loro identificazione. Poiché solitamente i modelli matematici necessitano ti essere stimati da dati sperimentali, è essenziale risolvere il problema della non unicità per il loro utilizzo nell’inferenza statistica (identificazione di modelli) da dati misurati.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Stochastic errors"

1

Feenstra, Robert C. Should exact index numbers have standard errors?: Theory and application to Asian growth. National Bureau of Economic Research, 2003.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Feenstra, Robert C. Should exact index numbers have standard errors?: Theory and application to Asian growth. National Bureau of Economic Research, 2004.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Phan, Minh Q. Stochastic prediction of vibration levels in the presence of modeling uncertainties: Final report, NASA grant NAG1-1698. Princeton University, Dept. of Mechanical and Aerospace Engineering, 1997.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Novak, Erich. Deterministic and Stochastic Error Bounds in Numerical Analysis. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/bfb0079792.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Leontaritis, I. J. A prediction error estimator for nonlinear stochastic systems. University, Dept. of Control Engineering, 1986.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Novak, Erich. Deterministic and stochastic error bounds in numerical analysis. Springer-Verlag, 1988.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Michael, Evans. An algorithm for the approximation of integrals with exact error bounds. University of Toronto, Dept. of Statistics, 1997.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Hodge, Bri-Mathias. Wind power forecasting error distributions over multiple timescales. National Renewable Energy Laboratory, 2011.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Hodge, Bri-Mathias. Wind power forecasting error distributions over multiple timescales: Preprint. National Renewable Energy Laboratory, 2011.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Babeshko, Lyudmila, and Irina Orlova. Econometrics and econometric modeling in Excel and R. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1079837.

Full text
Abstract:
The textbook includes topics of modern econometrics, often used in economic research. Some aspects of multiple regression models related to the problem of multicollinearity and models with a discrete dependent variable are considered, including methods for their estimation, analysis, and application. A significant place is given to the analysis of models of one-dimensional and multidimensional time series. Modern ideas about the deterministic and stochastic nature of the trend are considered. Methods of statistical identification of the trend type are studied. Attention is paid to the evaluation, analysis, and practical implementation of Box — Jenkins stationary time series models, as well as multidimensional time series models: vector autoregressive models and vector error correction models. It includes basic econometric models for panel data that have been widely used in recent decades, as well as formal tests for selecting models based on their hierarchical structure. Each section provides examples of evaluating, analyzing, and testing models in the R software environment. Meets the requirements of the Federal state educational standards of higher education of the latest generation.&#x0D; &#x0D; It is addressed to master's students studying in the Field of Economics, the curriculum of which includes the disciplines Econometrics (advanced course)", "Econometric modeling", "Econometric research", and graduate students."
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Stochastic errors"

1

Bouleau, Nicolas. "The Instantaneous Error Structure of a Stochastic Process." In The Mathematics of Errors. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88575-5_13.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Doerr, Daniel, Ilan Gronau, Shlomo Moran, and Irad Yavneh. "Stochastic Errors vs. Modeling Errors in Distance Based Phylogenetic Reconstructions." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23038-7_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Golyandina, N., and V. Nekrutkin. "Estimation Errors for Functionals on Measure Spaces." In Advances in Stochastic Simulation Methods. Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-1318-5_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Azencott, Robert, Yutheeka Gadhyan, and Roland Glowinski. "Option price sensitivity to errors in stochastic dynamics modeling." In Proceedings of the 2009 SIAM Conference on “Mathematics for Industry”. Society for Industrial and Applied Mathematics, 2010. http://dx.doi.org/10.1137/1.9781611973303.18.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Lobbe, Alexander, Dan Crisan, and Oana Lang. "Generative Modelling of Stochastic Rotating Shallow Water Noise." In Mathematics of Planet Earth. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70660-8_1.

Full text
Abstract:
AbstractIn recent work Crisan and co-authors (Foundations of Data Science, 2023), have developed a generic methodology for calibrating the noise in fluid dynamics stochastic partial differential equations where the stochasticity was introduced to parametrize subgrid-scale processes. The stochastic parameterization of sub-grid scale processes is required in the estimation of uncertainty in weather and climate predictions, to represent systematic model errors arising from subgrid-scale fluctuations. The methodology in Crisan (Foundations on Data Science, 2023) used a principal component analysis (PCA) technique based on the ansatz that the increments of the stochastic parametrization are normally distributed. In this chapter, the PCA technique is replaced by a generative model technique. This enables us to avoid imposing additional constraints on the increments. The methodology is tested on a stochastic rotating shallow water model with the elevation variable of the model used as input data. The numerical simulations show that the noise is indeed non-Gaussian. The generative modelling technology gives good RMSE, CRPS score and forecast rank histogram results.
APA, Harvard, Vancouver, ISO, and other styles
6

Müller-Gronbach, Thomas, and Klaus Ritter. "Minimal Errors for Strong and Weak Approximation of Stochastic Differential Equations." In Monte Carlo and Quasi-Monte Carlo Methods 2006. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-74496-2_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Jacobe de Naurois, Ladislas, Arnulf Jentzen, and Timo Welti. "Lower Bounds for Weak Approximation Errors for Spatial Spectral Galerkin Approximations of Stochastic Wave Equations." In Stochastic Partial Differential Equations and Related Fields. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74929-7_13.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Ho, Hwai-Chung. "Estimation errors of the Sharpe ratio for long-memory stochastic volatility models." In Institute of Mathematical Statistics Lecture Notes - Monograph Series. Institute of Mathematical Statistics, 2006. http://dx.doi.org/10.1214/074921706000001021.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Dimov, Ivan, Rayna Georgieva, and Venelin Todorov. "Balancing of Systematic and Stochastic Errors in Monte Carlo Algorithms for Integral Equations." In Numerical Methods and Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15585-2_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Hilgers, Michael G., and Aaron Burke. "Exploring Errors in Reading a Visualization via Eye Tracking Models Using Stochastic Geometry." In HCI in Business, Government and Organizations. Information Systems and Analytics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22338-0_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Stochastic errors"

1

Orazem, Mark E. "A Tutorial on Impedance Spectroscopy." In CORROSION 1998. NACE International, 1998. https://doi.org/10.5006/c1998-98302.

Full text
Abstract:
Abstract Electrochemical impedance spectroscopy is a powerful and general technique suitable for in-situ characterization of electrochemical systems. The fundamental concepts of impedance spectroscopy are presented in the context of a hierarchy of electrochemical methods. As, in theory, time-domain and frequency-domain transient techniques provide the same information concerning the system under study, the selection between the two must be based on the frequency range that can be sampled and on the associated error structure. In the context of error structure, the advantages of impedance spectroscopy are that the integration at a single frequency over many cycles greatly reduces stochastic errors and that the measurement of a complex impedance allows use of the Kramers-Kronig relations to identify experimental bias errors.
APA, Harvard, Vancouver, ISO, and other styles
2

Shimada, Toshiro. "Precaution against errors in using stochastic software." In Training Researchers in the Use if Statistics. International Association for Statistical Education, 2000. http://dx.doi.org/10.52041/srap.00202.

Full text
Abstract:
There are many statistics packages available that make it easy to perform stochastic procedures. Therefore, today's students may think they can handle their data processing needs, and obtain stochastic results simply by clicking a PC button. However, without being aware of it, they can make many mistakes, and treat their data incorrectly. In this paper we compare generalised logistic curves with simple logistic curves and explain their characteristics to help students and researchers avoid mistakes.
APA, Harvard, Vancouver, ISO, and other styles
3

Sturm, A. J., and M. Y. Lee. "Robot Accuracy Qualification: A Stochastic Differential Kinematic Monte Carlo Error Combination Approach." In ASME 1988 Design Technology Conferences. American Society of Mechanical Engineers, 1988. http://dx.doi.org/10.1115/detc1988-0055.

Full text
Abstract:
Abstract This paper presents a stochastic model and a Monte Carlo computer simulation algorithm to combine all measurable non-linear kinematic and misalignment error components to predict the overall tooltip quasi-static robot spatial position accuracy for a gantry robot. These errors include joint position accuracies, joint misalignments, joint zero position offsets, axis directional straightness, squareness errors and kinematic coupling errors. All of these errors can be independently measured using a laser interferometer and/or other precision measuring instruments. The interation between robot joints and coupling between these error components are very complex making the determination of the overall robot spatial position accuracy difficult. In this paper a Monte Carlo computer simulation program for predicting overall robot spatial position accuracy based on a stochastic error model was developed. Finally, simulation results are compared with direct spatial accuracy test results using a computerized Theodolite system. This robot spatial accuracy qualification methodology has been accepted and recommended by RIA as part of American National Standard for robot accuracy evaluation.
APA, Harvard, Vancouver, ISO, and other styles
4

Lewandowski, Arkadiusz, and Dylan Williams. "Stochastic modeling of coaxial-connector repeatability errors." In 2009 74th ARFTG Microwave Measurement Conference. IEEE, 2009. http://dx.doi.org/10.1109/arftg74.2009.5439104.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

CHAUBEY, YOGENDRA P., MANSI KHURANA, and SHALINI CHANDRA. "JACKKNIFING STOCHASTIC RESTRICTED RIDGE ESTIMATOR WITH HETEROSCEDASTIC ERRORS." In Proceedings of Statistics 2011 Canada/IMST 2011-FIM XX. WORLD SCIENTIFIC, 2013. http://dx.doi.org/10.1142/9789814417983_0004.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Radhakrishnan, C., and A. C. Singer. "Recursive least squares filtering under stochastic computational errors." In 2013 Asilomar Conference on Signals, Systems and Computers. IEEE, 2013. http://dx.doi.org/10.1109/acssc.2013.6810552.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Iakovidou, Charikleia, and Ermin Wei. "Nested Distributed Gradient Methods with Stochastic Computation Errors." In 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2019. http://dx.doi.org/10.1109/allerton.2019.8919853.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Zhiyong, Miao, Shi Hongyang, and Yi Zhang. "Real-time analysis for Stochastic errors of MEMS gyro." In Space Optics and Earth Imaging and Space Navigation, edited by Carl Nardell, Suijian Xue, and Huaidong Yang. SPIE, 2017. http://dx.doi.org/10.1117/12.2282195.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Ichikawa, Katsuhiro, and Shigeru Yamashita. "A Multiply Accumulator for Stochastic Numbers Without Scaling Errors." In 2021 34th International Conference on VLSI Design and 2021 20th International Conference on Embedded Systems (VLSID). IEEE, 2021. http://dx.doi.org/10.1109/vlsid51830.2021.00020.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Evensen, G. "Introducing Stochastic Model Errors In Ensemble-Based History Matching." In ECMOR XVI - 16th European Conference on the Mathematics of Oil Recovery. EAGE Publications BV, 2018. http://dx.doi.org/10.3997/2214-4609.201802280.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Stochastic errors"

1

Hansen, Lars Peter, and Ravi Jagannathan. Assessing Specification Errors in Stochastic Discount Factor Models. National Bureau of Economic Research, 1994. http://dx.doi.org/10.3386/t0153.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Chen, X., J. M. Connors, and C. H. Tong. A flexible method to calculate the distributions of discretization errors in operator-split codes with stochastic noise in problem data. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1119920.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Clark, Todd E., Gergely Ganics, and Elmar Mertens. Constructing fan charts from the ragged edge of SPF forecasts. Federal Reserve Bank of Cleveland, 2022. http://dx.doi.org/10.26509/frbc-wp-202236.

Full text
Abstract:
We develop a model that permits the estimation of a term structure of both expectations and forecast uncertainty for application to professional forecasts such as the Survey of Professional Forecasters (SPF). Our approach exactly replicates a given data set of predictions from the SPF (or a similar forecast source) without measurement error. Our model captures fixed horizon and fixed-event forecasts, and can accommodate changes in the maximal forecast horizon available from the SPF. The model casts a decomposition of multi-period forecast errors into a sequence of forecast updates that may be partially unobserved, resulting in a multivariate unobserved components model. In our empirical analysis, we provide quarterly term structures of expectations and uncertainty bands. Our preferred specification features stochastic volatility in forecast updates, which improves forecast performance and yields model estimates of forecast uncertainty that vary over time. We conclude by constructing SPF-based fan charts for calendar-year forecasts like those published by the Federal Reserve. Replication files are available at https://github.com/elmarmertens/ClarkGanicsMertensSPFfancharts.
APA, Harvard, Vancouver, ISO, and other styles
4

Hart, Carl, and Gregory Lyons. A tutorial on the rapid distortion theory model for unidirectional, plane shearing of homogeneous turbulence. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/44766.

Full text
Abstract:
The theory of near-surface atmospheric wind noise is largely predicated on assuming turbulence is homogeneous and isotropic. For high turbulent wavenumbers, this is a fairly reasonable approximation, though it can introduce non-negligible errors in shear flows. Recent near-surface measurements of atmospheric turbulence suggest that anisotropic turbulence can be adequately modeled by rapid-distortion theory (RDT), which can serve as a natural extension of wind noise theory. Here, a solution for the RDT equations of unidirectional plane shearing of homogeneous turbulence is reproduced. It is assumed that the time-varying velocity spectral tensor can be made stationary by substituting an eddy-lifetime parameter in place of time. General and particular RDT evolution equations for stochastic increments are derived in detail. Analytical solutions for the RDT evolution equation, with and without an effective eddy viscosity, are given. An alternative expression for the eddy-lifetime parameter is shown. The turbulence kinetic energy budget is examined for RDT. Predictions by RDT are shown for velocity (co)variances, one-dimensional streamwise spectra, length scales, and the second invariant of the anisotropy tensor of the moments of velocity. The RDT prediction of the second invariant for the velocity anisotropy tensor is shown to agree better with direct numerical simulations than previously reported.
APA, Harvard, Vancouver, ISO, and other styles
5

Diebold, Francis, and Minchul Shin. Assessing Point Forecast Accuracy by Stochastic Error Distance. National Bureau of Economic Research, 2016. http://dx.doi.org/10.3386/w22516.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Radtke, Gregg Arthur, Keith L. Cartwright, and Lawrence C. Musson. Stochastic Richardson Extrapolation Based Numerical Error Estimation for Kinetic Plasma Simulations. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1504853.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Wettergren, Thomas A. Stochastic Error Modeling of Beamformer Output for Arrays with Directive Elements. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada390305.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Holzenthal, Elizabeth, and Bradley Johnson. Comparison of run-up models with field data. Engineer Research and Development Center (U.S.), 2024. https://doi.org/10.21079/11681/49470.

Full text
Abstract:
Run-up predictions are inherently uncertain, owing to ambiguities in phase-averaged models and inherent complexities of surf and swash-zone hydrodynamics. As a result, different approaches, ranging from simple algebraic expressions to computationally intensive phase-resolving models, have been used in attempt to capture the most relevant run-up processes. Studies quantifiably comparing these methods in terms of physical accuracy and computational speed are needed as new observation technologies and models become available. The current study tests the capability of the new swash formulation of the Coastal Modeling System (CMS) to predict 1D run-up statistics (R2%) collected during an energetic 3-week period on sandy dune-backed beach in Duck, North Carolina. The accuracy and speed of the debut CMS swash formulation is compared with one algebraic model and three other numerical models. Of the four tested numerical models, the CSHORE model computed the results fastest, and the CMS model results had the greatest accuracy. All four numerical models, including XBeach in surfbeat and nonhydrostatic modes, yielded half the error of the algebraic model tested. These findings present an encouraging advancement for phase-averaged coastal models, a critical step towards rapid prediction for near-time deterministic or long-term stochastic guidance.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Stochastic errors' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography