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1

Weinstein, Ehud. Multiple source location estimation using the EM algorithm. Woods Hole, Mass: Woods Hole Oceanographic Institution, 1986.

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2

Feder, Meir. Optimal multiple source location via the EM algorithm. Woods Hole, Mass: Woods Hole Oceanographic Institution, 1986.

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3

Palaszewski, Bo. On multiple test procedures for finding deviating parameters. Göteborg: University of Göteborg, 1993.

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4

Major, Péter. On the Estimation of Multiple Random Integrals and U-Statistics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37617-7.

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5

Blech, Richard A. Parallel Gaussian estimation of a block tridiagonal matrix using multiple microcomputers. Cleveland, Ohio: Lewis Research Center, 1989.

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6

Schreuder, Hans T. Data estimation and prediction for natural resources public data. [Fort Collins, Colo.?]: U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 1998.

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7

M, Reich Robin, e Rocky Mountain Research Station (Fort Collins, Colo.), a cura di. Data estimation and prediction for natural resources public data. [Fort Collins, Colo.?]: U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 1998.

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8

W, Cooper Russell. Estimation and identification of structural parameters in the presence of multiple equilibria. Cambridge, MA: National Bureau of Economic Research, 2002.

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9

Prins, Robert Dean. Effective dose estimation for U.S. Army soldiers undergoing multiple computed tomography scans. [New York, N.Y.?]: [publisher not identified], 2011.

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10

Ohya, Jun. Analyzing video sequences of multiple humans: Tracking, posture estimation and behavior recognition. Boston, MA: Springer, 2002.

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11

Akira, Utsumi, e Yamato Junji, a cura di. Analyzing video sequences of multiple humans: Tracking, posture estimation, and behavior recognition. Boston: Kluwer Academic Publishers, 2002.

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12

1957-, Ferenc Susan A., e Foran Jeffery Allen 1953-, a cura di. Multiple stressors in ecological risk and impact assessments: Approaches to risk estimation. Pensacola, Fla: Society of Environmental Toxicology and Chemistry, 2000.

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13

Nguyen, Tuan Tran. A Reliability-Aware Fusion Concept Toward Robust Ego-Lane Estimation Incorporating Multiple Sources. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-26949-4.

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14

Tanner, Gwen Lettice. Some topics in multiple hypothesis estimation and control using non-quadratic cost functions. [s.l.]: typescript, 1997.

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15

Schreuder, Hans T. Annual design-based estimation for the annualized inventories of forest inventory and analysis: Sample size determination. [Ogden, UT]: U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 2000.

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16

Schreuder, Hans T. Annual design-based estimation for the annualized inventories of forest inventory and analysis: Sample size determination. [Ogden, UT]: U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 2000.

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17

S, Lin Jin-Mann, Teply John e Rocky Mountain Research Station (Fort Collins, Colo.), a cura di. Annual design-based estimation for the annualized inventories of forest inventory and analysis: Sample size determination. [Ogden, UT] (324 25th St., Ogden 84401): U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 2000.

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18

Schreuder, Hans T. Annual design-based estimation for the annualized inventories of forest inventory and analysis: Sample size determination. [Ogden, UT]: U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 2000.

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19

Rahiala, Markku. On the identification and estimation of multiple input transfer function models with autocorrelated errors. Helsinki: Research Institute of the Finnish Economy, 1985.

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20

Breckenridge, Richard P. Localization of multiple broadband targets in spherical coordinates via adaptive beamforming and non-linear estimation. Monterey, Calif: Naval Postgraduate School, 1989.

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21

Toro, Javier G. Direct and indirect parametric multiple motion estimation in image sequences and their application in layer-based angiographic image compression. [s.l: The Author], 2000.

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22

Armknecht, Paul A. Price imputation and other techniques for dealing with missing observations, seasonality and quality change in price indices. [Washington, D.C.]: International Monetary Fund, Statistics Department, 1999.

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23

Pihlajavaara, S. E. Contributions for the development of the estimation of long-term performance and service life of concrete: A statistical multiple comparison system, a permeability apparatus, permeability studies, and a surface layer deterioration model ; an overview of a collection of the author's theoretical, experimental, and applied research on the subjects. Espoo: Helsinki University of Technology, 1994.

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24

Babeshko, Lyudmila, Mihail Bich e Irina Orlova. Econometrics and econometric modeling. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1141216.

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Abstract (sommario):
The textbook covers a wide range of issues related to econometric modeling. Regression models are the core of econometric modeling, so the issues of their evaluation, testing of assumptions, adjustment and verification are given a significant place. Various aspects of multiple regression models are included: multicollinearity, dummy variables, and lag structure of variables. Methods of linearization and estimation of nonlinear models are considered. An apparatus for evaluating systems of simultaneous and apparently unrelated equations is presented. Attention is paid to time series models. Detailed solutions of the examples in Excel and the R software environment are included. Meets the requirements of the federal state educational standards of higher education of the latest generation. For undergraduate and graduate students studying in the field of "Economics", the curriculum of which includes the disciplines "Econometrics"," Econometric Modeling","Econometric research".
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25

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

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Abstract (sommario):
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. 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."
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26

Park, Beomjin. Channel estimation in multiple-input multiple-output systems. 2004.

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27

Multiple Model Adaptive Estimation Using Filter Spawning. Storming Media, 1998.

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28

Modified Multiple Model Adaptive Estimation (M3AE) for Simultaneous Parameter and State Estimation. Storming Media, 1998.

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29

Yang, Ming-Chung. Estimation of multiple poisson means under entropy loss. 1986.

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30

Multiple Model Adaptive Estimation for Time Series Analysis. Storming Media, 2001.

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31

Hasenkamp, G. Specification and Estimation of Multiple-Output Production Functions. Springer London, Limited, 2012.

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32

New Algorithms for Moving-Bank Multiple Model Adaptive Estimation. Storming Media, 1998.

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33

Kapoor, Samir. Estimation of motion parameters for single and multiple mobile objects. 1994.

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34

Major, Péter. On the Estimation of Multiple Random Integrals and U-Statistics. Springer, 2013.

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35

Schenker, Nathaniel. Multiple imputation for interval estimation from surveys with ignorable nonresponse. 1985.

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36

Generalized Residual Multiple Model Adaptive Estimation of Parameters and States. Storming Media, 2003.

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37

Practical Implementation of Multiple Model Adaptive Estimation Using Neyman-Pearson Based Hypothesis Testing and Spectral Estimation Tools. Storming Media, 1996.

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38

Asako, Kazumi, Jun-ichi Nakamura e Konomi Tonogi. Multiple q and Investment in Japan, Volume 1: Theory and Estimation. Springer, 2020.

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39

Ström, Erik G. Direct-sequence code-division multiple access systems: Near-far resistant parameter estimation and detection. 1994.

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40

Multiple Model Adaptive Estimation and Control Redistribution for the Vista F-16. Storming Media, 1996.

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41

Ohya, Jun, Akira Utsumi e Junji Yamato. Analyzing Video Sequences of Multiple Humans: Tracking, Posture Estimation and Behavior Recognition. Springer, 2012.

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42

A Reliability-Aware Fusion Concept Toward Robust Ego-Lane Estimation Incorporating Multiple Sources. Springer, 2019.

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43

Annual design-based estimation for the annualized inventories of forest inventory and analysis: Sample size determination. [Ogden, UT] (324 25th St., Ogden 84401): U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 2000.

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44

Annual design-based estimation for the annualized inventories of forest inventory and analysis: Sample size determination. [Ogden, UT] (324 25th St., Ogden 84401): U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 2000.

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45

Matsui, Shigeyuki, e Hisashi Noma. Estimation and Selection in High-Dimensional Genomic Studies: Multiple Testing, Gene Ranking, and Classification. Springer, 2020.

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46

Xie, Xianchao. Two Tales of Frequentist Properties of Bayesianly Motivated Methods: Multiple Imputation and Shrinkage Estimation. 2011.

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47

Rendall, Michael, Bonnie Ghosh-Dastidar, Margaret Weden e Zafar Nazarov. Multiple Imputation for Combined-Survey Estimation With Incomplete Regressors In One But Not Both Surveys. RAND Corporation, 2011. http://dx.doi.org/10.7249/wr887-1.

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48

Flight Control Failure Detection and Control Redistribution Using Multiple Model Adaptive Estimation with Filter Spawning. Storming Media, 2002.

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49

Buhr, Dianne C. Variability in the estimation of item option characteristic curves for the multiple-category scoring model. 1989.

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50

Lederer, Michael J. Cross-validation, formula estimation, and a bootstrap approach to estimating the population cross-validity of multiple regression equations. 1996.

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