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Relevant bibliographies by topics / Minimum distance estimator

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Academic literature on the topic 'Minimum distance estimator'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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Journal articles on the topic "Minimum distance estimator"

1

Moon, Hyungsik Roger, and Frank Schorfheide. "MINIMUM DISTANCE ESTIMATION OF NONSTATIONARY TIME SERIES MODELS." Econometric Theory 18, no. 6 (2002): 1385–407. http://dx.doi.org/10.1017/s0266466602186063.

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This paper analyzes the limit distribution of minimum distance (MD) estimators for nonstationary time series models that involve nonlinear parameter restrictions. A rotation for the restricted parameter space is constructed to separate the components of the MD estimator that converge at different rates. We derive regularity conditions for the restriction function that are easier to verify than the stochastic equicontinuity conditions that arise from direct estimation of the restricted parameters. The sequence of matrices that is used to weigh the discrepancy between the unrestricted estimates

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Fan, Zhaozhi. "Minimum-Distance Estimator for Stable Exponent." Communications in Statistics - Theory and Methods 38, no. 4 (2009): 511–28. http://dx.doi.org/10.1080/03610920802245733.

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Pak, Ro-Jin. "The Minimum Squared Distance Estimator and the Minimum Density Power Divergence Estimator." Communications for Statistical Applications and Methods 16, no. 6 (2009): 989–95. http://dx.doi.org/10.5351/ckss.2009.16.6.989.

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Koenker, Roger, José A. F. Machado, Christopher L. Skeels, and Alan H. Welsh. "Momentary Lapses: Moment Expansions and the Robustness of Minimum Distance Estimation." Econometric Theory 10, no. 1 (1994): 172–97. http://dx.doi.org/10.1017/s0266466600008288.

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This paper explores the robustness of minimum distance (GMM) estimators focusing particularly on the effect of intermediate covariance matrix estimation on final estimator performance. Asymptotic expansions to order Op(n−3/2) are employed to construct O(n−2) expansions for the variance of estimators constructed from preliminary least-squares and general M-estimators. In the former case, there is a rather curious robustifying effect due to estimation of the Eicker-White covariance matrix for error distributions with sufficiently large kurtosis.

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Nagnur, B. N., and M. S. Hegde. "On Second Order Efficiency of Minimum Discrepancy and Minimum Distance Estimators for the Multinomial Distribution." Calcutta Statistical Association Bulletin 37, no. 1-2 (1988): 17–28. http://dx.doi.org/10.1177/0008068319880102.

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Rao (1961, '62, '63) introduced the concept of second order efficiency (s.o.e.) of an asymptotically efficient i.e., best asymptotically normal (BAN) estimator. The main purpose of introducing this concept was to discriminate different asymptotically efficient estimators. Rao considered some wellknown methods of estimation for the multinomial distribution with true cell probabilities depending on a single unknown parameter. Nagnur and Aithal (1986) obtained a general expression for the s.o.e. of minimum distance estimators, obtained from a particular form of a distance function given by Taylor

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Tieslau, Margie A., Peter Schmidt, and Richard T. Baillie. "A minimum distance estimator for long-memory processes." Journal of Econometrics 71, no. 1-2 (1996): 249–64. http://dx.doi.org/10.1016/0304-4076(94)01703-4.

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Hayakawa, Kazuhiko. "Corrected standard errors for optimal minimum distance estimator." Economics Letters 167 (June 2018): 5–9. http://dx.doi.org/10.1016/j.econlet.2018.02.029.

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Kadjo, Roger, Ouagnina Hili, and Aubin N'dri. "Minimum Hellinger Distance Estimation of a Univariate GARCH Process." Journal of Mathematics Research 9, no. 3 (2017): 80. http://dx.doi.org/10.5539/jmr.v9n3p80.

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In this paper, we determine the Minimum Hellinger Distance estimator of a stationary GARCH process. We construct an estimator of the parameters based on the minimum Hellinger distance method. Under conditions which ensure the $\phi$-mixing of the GARCH process, we establish the almost sure convergence and the asymptotic normality of the estimator.

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Hodoshima, Jiro. "Effect of Nonnormality on the Estimation of a Single Structural Equation with Structural Change." Econometric Theory 5, no. 1 (1989): 53–62. http://dx.doi.org/10.1017/s0266466600012251.

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Effect of nonnormality on the asymptotic property of three estimators of a single structural equation with structural change is examined. The three estimators are the limited information maximum likelihood estimator, derived under normality and equality of structural variances in different samples, given by Hodoshima, a two-stage least squares type estimator due to Barten and Bronsard, and a minimum distance estimator presented here. Normality is relaxed but the equality assumption of structural variances is retained. Under nonnormality the limited information maximum likelihood estimator is c

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Shin, Vladimir, Georgy Shevlyakov, Woohyun Jeong, and Yoonsoo Kim. "Closed-Form Distance Estimators under Kalman Filtering Framework with Application to Object Tracking." Mathematical Problems in Engineering 2020 (August 20, 2020): 1–16. http://dx.doi.org/10.1155/2020/9141735.

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In this paper, the minimum mean square error (MMSE) estimation problem for calculation of distances between two signals via the Kalman filtering framework is considered. The developed algorithm includes two stages: the Kalman estimate of a state vector computed at the first stage is nonlinearly transformed at the second stage based on a distance function and the MMSE criterion. In general, the most challenging aspect of application of the distance estimator is calculation of the multivariate Gaussian integral. However, it can be successfully overcome for the specific metrics between two points

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Dissertations / Theses on the topic "Minimum distance estimator"

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D'Ambrosio, Philip. "A Differential Geometry-Based Algorithm for Solving the Minimum Hellinger Distance Estimator." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/32228.

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Robust estimation of statistical parameters is traditionally believed to exist in a trade space between robustness and efficiency. This thesis examines the Minimum Hellinger Distance Estimator (MHDE), which is known to have desirable robustness properties as well as desirable efficiency properties. This thesis confirms that the MHDE is simultaneously robust against outliers and asymptotically efficient in the univariate location case. Robustness results are then extended to the case of simple linear regression, where the MHDE is shown empirically to have a breakdown point of 50%. A geomet

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Munasib, Abdul B. A. "Lifecycle of social networks: A dynamic analysis of social capital accumulation." The Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=osu1121441394.

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Warwick, Jane. "Selecting tuning parameters in minimum distance estimators." Thesis, Open University, 2002. http://oro.open.ac.uk/19918/.

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Many minimum distance estimators have the potential to provide parameter estimates which are both robust and efficient and yet, despite these highly desirable theoretical properties, they are rarely used in practice. This is because the performance of these estimators is rarely guaranteed per se but obtained by placing a suitable value on some tuning parameter. Hence there is a risk involved in implementing these methods because if the value chosen for the tuning parameter is inappropriate for the data to which the method is applied, the resulting estimators may not have the desired theoretica

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Warwick, Jane. "Selecting tuning parameters in minimum distance estimators." n.p, 2001. http://ethos.bl.uk/.

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Ben, Abdeddaiem Maroua. "Tests d'ajustement pour des processus stochastiques dans le cas de l'hypothèse nulle paramétrique." Thesis, Le Mans, 2016. http://www.theses.fr/2016LEMA1016/document.

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Ce travail est consacré au problème de construction des tests d'ajustement dans le cas des processus stochastiques observés en temps continu. Comme modèles d'observations, nous considérons les processus de diffusion avec « petit bruit » et ergodique et le processus de Poisson non homogène. Sous l'hypothèse nulle, nous traitons le cas où chaque modèle dépend d'un paramètre inconnu unidimensionnel et nous proposons l'estimateur de distance minimale pour ce paramètre. Notre but est la construction des tests d'ajustement « asymptotically distribution free » (ADF) de niveau asymtotique α ϵ (0,1) da

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Xiang, Sijia. "Minimum Hellinger distance estimation in a semiparametric mixture model." Kansas State University, 2012. http://hdl.handle.net/2097/13762.

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Master of Science<br>Department of Statistics<br>Weixin Yao<br>In this report, we introduce the minimum Hellinger distance (MHD) estimation method and review its history. We examine the use of Hellinger distance to obtain a new efficient and robust estimator for a class of semiparametric mixture models where one component has known distribution while the other component and the mixing proportion are unknown. Such semiparametric mixture models have been used in biology and the sequential clustering algorithm. Our new estimate is based on the MHD, which has been shown to have good efficiency and

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Alexandridis, Roxana Antoanela. "Minimum disparity inference for discrete ranked set sampling data." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1126033164.

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Thesis (Ph. D.)--Ohio State University, 2005.<br>Title from first page of PDF file. Document formatted into pages; contains xi, 124 p.; also includes graphics. Includes bibliographical references (p. 121-124). Available online via OhioLINK's ETD Center

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Yan, Huey. "Generalized Minimum Penalized Hellinger Distance Estimation and Generalized Penalized Hellinger Deviance Testing for Generalized Linear Models: The Discrete Case." DigitalCommons@USU, 2001. https://digitalcommons.usu.edu/etd/7066.

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In this dissertation, robust and efficient alternatives to quasi-likelihood estimation and likelihood ratio tests are developed for discrete generalized linear models. The estimation method considered is a penalized minimum Hellinger distance procedure that generalizes a procedure developed by Harris and Basu for estimating parameters of a single discrete probability distribution from a random sample. A bootstrap algorithm is proposed to select the weight of the penalty term. Simulations are carried out to compare the new estimators with quasi-likelihood estimation. The robustness of the estim

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Ström, Simon, and Ali Qhorbani. "Automation of the design process of printed circuit boards : Determining minimum distance required by auto-routing software." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-251925.

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This thesis project aims to create an overview of new technologies in printed circuit board manufacturing which when automated could become part of an Industry 4.0 production flow. Potential design limits imposed by new technologies are then applied in the creation process of a minimum distance estimation function. The intended purpose of this function is to correctly estimate the minimum distance required for the auto-routing software FreeRouting to be able to successfully route between two components. This is achieved by using a brute-force attack to progressively decrease the distance betwe

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Yngman, Gunnar. "Individualization of fixed-dose combination regimens : Methodology and application to pediatric tuberculosis." Thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-242059.

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Introduction: No Fixed-Dose Combination (FDC) formulations currently exist for pediatric tuberculosis (TB) treatment. Earlier work implemented, in the software NONMEM, a rational method for optimizing design and individualization of pediatric anti-TB FDC formulations based on patient body weight, but issues with parameter estimation, dosage strata heterogeneity and representative pharmacokinetics remained. Aim: To further develop the rational model-based methodology aiding the selection of appropriate FDC formulation designs and dosage regimens, in pediatric TB treatment. Materials and Methods

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Books on the topic "Minimum distance estimator"

1

Hiroyuki, Shioya, and Park Chanseok, eds. Statistical inference: The minimum distance approach. Taylor & Francis, 2011.

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Koenker, Roger. Momentary lapses: Moment expansions and the robustness of minimum distance estimation. College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1991.

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Wesselman, A. M. The population-sample decomposition method: A distribution-free estimation technique for minimum distance parameters. M. Nijhoff, 1987.

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Basu, Ayanendranath, Hiroyuki Shioya, and Chanseok Park. Statistical Inference: The Minimum Distance Approach. Taylor & Francis Group, 2011.

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5

Minimum Distance Estimation on Time Series Analysis With Little Data. Storming Media, 2001.

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Parameter Estimation of the Mixed Generalized Gamma Distribution Using Maximum Likelihood Estimation and Minimum Distance Estimation. Storming Media, 1998.

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Robust Parameter Estimation for the Mixed Weibull (Seven Parameter) Including the Method of Minimum Likelihood and the Method of Minimum Distance. Storming Media, 1997.

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Book chapters on the topic "Minimum distance estimator"

1

Ferland, René, Sorana Froda, and Jean Lavigne. "A simulation study of a minimum distance estimator for finite mixtures under censoring." In Institute of Mathematical Statistics Lecture Notes - Monograph Series. Institute of Mathematical Statistics, 2003. http://dx.doi.org/10.1214/lnms/1215091954.

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Ferland, René, Sorana Froda, and Jean Lavigne. "A simulation study of a minimum distance estimator for finite mixtures under censoring." In Institute of Mathematical Statistics Lecture Notes - Monograph Series. Institute of Mathematical Statistics, 2003. http://dx.doi.org/10.1214/lnms/1215091954.

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Kant Rai, Piyush, G. C. Tikkiwal, and Alka. "A Joint Calibration Estimator of Population Total Under Minimum Entropy Distance Function Based on Dual Frame Surveys." In Forum for Interdisciplinary Mathematics. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1476-0_8.

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Koul, Hira L. "Minimum Distance Estimators." In Weighted Empirical Processes in Dynamic Nonlinear Models. Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0055-7_5.

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Kutoyants, Yu. "Minimum Distance Estimation." In Identification of Dynamical Systems with Small Noise. Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1020-4_8.

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Devroye, Luc, and Gábor Lugosi. "The Minimum Distance Estimate: Examples." In Combinatorial Methods in Density Estimation. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0125-7_8.

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Lombardi, Gabriele, Alessandro Rozza, Claudio Ceruti, Elena Casiraghi, and Paola Campadelli. "Minimum Neighbor Distance Estimators of Intrinsic Dimension." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23783-6_24.

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Roussas, George G., and Yannis G. Yatracos. "Minimum Distance Estimates with Rates under ø-mixing." In Festschrift for Lucien Le Cam. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1880-7_22.

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Rudrapatna, Mamatha, and Arcot Sowmya. "Feature Weighted Minimum Distance Classifier with Multi-class Confidence Estimation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11941439_29.

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Koul, Hira L., and Pei Geng. "Weighted Empirical Minimum Distance Estimators in Berkson Measurement Error Regression Models." In Analytical Methods in Statistics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48814-7_3.

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Conference papers on the topic "Minimum distance estimator"

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Patil, Nishad, Sandeep Menon, Diganta Das, and Michael Pecht. "Evaluation of Robust Covariance Estimation Techniques for Anomaly Detection of Insulated Gate Bipolar Transistors (IGBT)." In ASME 2010 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2010. http://dx.doi.org/10.1115/smasis2010-3861.

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An approach to detect anomalies in IGBTs is to monitor the collector-emitter current and voltage in application. These current and voltage parameters can then be reduced to a univariate distance measure called the Mahalanobis Distance (MD). The MD values with the use of an appropriate threshold enable anomaly detection of these devices. Mahalanobis distances (MD) are weighted Euclidean distances; the distance of each point from the center of the distribution is weighted by the inverse of the sample variance-covariance matrix. The presence of outliers in the monitored data can lead to the overe

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Otchere, Daniel Asante, David Hodgetts, Tarek Arbi Omar Ganat, Najeeb Ullah, and Alidu Rashid. "Static Reservoir Modeling Comparing Inverse Distance Weighting to Kriging Interpolation Algorithm in Volumetric Estimation. Case Study: Gullfaks Field." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/30919-ms.

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Abstract Understanding and characterizing the behaviour of the subsurface by combining it with a suitable statistical method gives a higher level of confidence in the reservoir model produced. Interpolation of porosity and permeability data with minimum error and high accuracy is, therefore, essential in reservoir modeling. The most widely used interpolation algorithm, kriging, with enough well data is the best linear unbiased estimator. This research sought to compare the applicability and competitiveness of inverse distance weighting (IDW) method using power index of 1, 2 and 4 to kriging wh

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Sharon, Yoav, John Wright, and Yi Ma. "Minimum sum of distances estimator: Robustness and stability." In 2009 American Control Conference. IEEE, 2009. http://dx.doi.org/10.1109/acc.2009.5160571.

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"LOCAL MINIMUM DISTANCE FOR THE DENSE DISPARITY ESTIMATION." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2006. http://dx.doi.org/10.5220/0001369803410348.

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Jiao, Jiantao, Yanjun Han, and Tsachy Weissman. "Minimax estimation of the L1 distance." In 2016 IEEE International Symposium on Information Theory (ISIT). IEEE, 2016. http://dx.doi.org/10.1109/isit.2016.7541399.

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Park, Jungseo, Seunghwan Mun, Chungmin Hyun, Byungkwon Kang, and Kwanghee Ko. "Similarity Assessment Method for Automated Curved Plate Forming." In SNAME 5th World Maritime Technology Conference. SNAME, 2015. http://dx.doi.org/10.5957/wmtc-2015-240.

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In this paper, a novel similarity estimation method for two shapes in the automated thermal forming is proposed. One shape is given as a CAD surface, and the other is given as a set of points measured points. These two shapes are registered with respect to a reference coordinate system so that they are aligned as closely as possible using the ICP based method. Three geometric properties are considered in the method. The first property is the distance between them. At each measured point, the closest distance to the CAD surface is computed, and the defined tolerance for the distances is used as

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Sanatkar, Mohammad Reza, Balasubramaniam Natarajan, and Abbas Mohammadi. "Route BER estimation in wireless ad hoc networks exploiting minimum distance routing." In 2012 IEEE Consumer Communications and Networking Conference (CCNC). IEEE, 2012. http://dx.doi.org/10.1109/ccnc.2012.6180961.

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D'Angelo, John James, Pradeepkumar Ashok, and Eric van Oort. "Improved Geosteering Information and Data Transfer using an Automated Computational Framework." In SPE/IADC International Drilling Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/204122-ms.

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Abstract Geosteering presently requires physical or virtual interaction between the geologist and the directional driller to arrive at steering decisions. This interaction often results in the loss of valuable information because all context and data may not be shared between them. A framework that is able to automatically integrate the data and information which the geologist and directional driller independently possess can result in improved decision making, and is the topic of this paper. The geosteering framework described herein combines estimation and optimization methods to solve geost

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Lukin, Vladimir V., Sergey K. Abramov, Alexander A. Zelensky, and Jaakko T. Astola. "Use of minimal inter-quantile distance estimation in image processing." In SPIE Optics + Photonics, edited by Gerhard X. Ritter, Mark S. Schmalz, Junior Barrera, and Jaakko T. Astola. SPIE, 2006. http://dx.doi.org/10.1117/12.678764.

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Fu, Ping, Bernt J. Leira, and Dag Myrhaug. "Parametric Study Related to the Collision Probability Between Two Risers." In ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/omae2016-54637.

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Efficient and accurate methods for assessment of riser collision become increasingly important as offshore oil and gas exploration moves into deeper water. Collisions may occur as a result of the environmental loading (current and waves), flow interaction and surface floater motions, or combinations of these effects. This paper provides a parametric study on the influence of these factors, especially the influence of the fluid interference on the minimum distance statistics. The conditional collision probability for two flexible risers in a tandem arrangement is also estimated for sea-states w

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Reports on the topic "Minimum distance estimator"

1

Eslinger, Paul W., and Wayne A. Woodward. Minimum Hellinger Distance Estimation for Normal Models. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada228714.

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