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Books on the topic 'Modèle de covariance'

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Published: 11 May 2022

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1

Anderson, Gordon. Alternative error covariance assumptions in dynamic panel data models. Toronto: Dept. of Economics and Institute for Policy Analysis, University of Toronto, 1988.

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2

Michael, Baker. Growth rate heterogeneity and the covariance structure of life cycle earnings. Toronto: Dept. of Economics and Institute for Policy Analysis, University of Toronto, 1992.

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3

Brandt, Michael W. A no-arbitrage approach to range-based estimation of return covariances and correlations. Cambridge, Mass: National Bureau of Economic Research, 2003.

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4

Woodruff, David J. Linear models for item scores: Reliability, covariance structure, and psychometric inference. Iowa City, Iowa: American College Testing Program, 1993.

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5

Gaver, Donald Paul. Bayesian prediction of mean square errors with covariates. Monterey, Calif: Naval Postgraduate School, 1992.

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6

Polites, Michael E. The estimation error covariance matrix for the ideal state reconstructor with measurement noise. [Washington, D.C.]: National Aeronautics and Space Administration, Scientific and Technical Information Division, 1988.

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7

Daniel, Kent. Covariance risk, mispricing, and the cross section of security returns. Cambridge, MA: National Bureau of Economic Research, 2000.

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8

Khalaf, Lynda. Structural change in covariance and exchange rate pass-through: The case of Canada. Ottawa: Bank of Canada, 2006.

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9

Lewis, Karen K. Should the holding period matter for the intertemporal consumption-based CAPM? Cambridge, MA: National Bureau of Economic Research, 1991.

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10

Sengupta, Debasis. Linear models: An integrated approach. River Edge, N.J: World Scientific, 2003.

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11

Durlauf, Steven N. Bounds on the variances of specification errors in models with expectations. Cambridge, MA: National Bureau of Economic Research, 1989.

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12

Chan, Louis K. C. On portfolio optimization: Forecasting covariances and choosing the risk model. Cambridge, MA: National Bureau of Economic Research, 1999.

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13

Black, Fischer. Equilibrium exchange rate hedging. Cambridge, MA: National Bureau of Economic Research, 1989.

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14

Rutherford, Andrew, and Andrew Rutherford. ANOVA and ANCOVA: A GLM approach. 2nd ed. Hoboken, N.J: Wiley, 2011.

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15

Fuss, Melvyn A. Heteroskedasticity-consistent estimation of the variance-covariance matrix for the almost ideal demand system. Toronto: University of Toronto, 1989.

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16

Rutherford, Andrew. ANOVA and ANCOVA: A GLM approach. 2nd ed. Hoboken, N.J: Wiley, 2011.

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17

Mueller, Ralph O., and Gregory R. Hancock. Structural equation modeling: A second course. 2nd ed. Charlotte, NC: Information Age Publishing, Inc., 2013.

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18

Chechelnitsky, Michael Y. Adaptive error estimation in linearized ocean general circulation models. Cambridge, Mass: Massachusetts Institute of Technology, 1999.

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19

Baldacci, Emanuele. More on the effectiveness of public spending on health care and education: A covariance structure model. [Washington, D.C.]: International Monetary Fund, Fiscal Affairs Department, 2002.

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20

Berger, Vance. Selection bias and covariate imbalances in randomzied clinical trials. Hoboken, NJ: John Wiley & Sons, 2005.

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21

Gilleskie, Donna B. Estimating the effects of covariates on health expenditures. Cambridge, MA: National Bureau of Economic Research, 2000.

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22

Stevens, Kurt Benedict. Remote measurement of the atmospheric isoplanatic angle and determination of refractive turbulence profiles by direct inversion of the scintillation amplitude covariance function with Tikhonov regularization. Monterey, Calif: Naval Postgraduate School, 1985.

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23

Khavaran, Abbas. A parametric study of fine-scale turbulence mixing noise. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2002.

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24

Hox, J. J. Applied multilevel analysis. 2nd ed. Amsterdam: TT-Publikaties, 1995.

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25

Brodsky, Slava. An Introduction To The Factorial Design Of Experiments: (Mathematical Foundations). USA: Manhattan Academia, 2014.

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26

Winer, B. J. Statistical principles in experimental design. 3rd ed. New York: McGraw-Hill, 1991.

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27

Great Lakes Environmental Research Laboratory, ed. Covariance properties of annual net basin supplies to the Great Lakes. Ann Arbor, Mich: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, Great Lakes Environmental Research Laboratory, 1994.

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28

Back, Kerry E. Factor Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0006.

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The CAPM and factor models in general are explained. Factors can be replaced by the returns or excess returns that are maximally correlated (the projections of the factors). A factor model is equivalent to an affine representation of an SDF and to spanning a return on the mean‐variance frontier. The use of alphas for performance evaluation is explained. Statistical factor models are defined as models in which factors explain the covariance matrix of returns. A proof is given of the Arbitrage Pricing Theory, which states that statistical factors are approximate pricing factors. The CAPM and the Fama‐French‐Carhart model are evaluated relative to portfolios based on sorts on size, book‐to‐market, and momentum.
29

Schmidt, Alexandra, Jennifer Hoeting, João Batista M. Pereira, and Pedro Paulo Vieira. Mapping malaria in the Amazon rain forest: A spatio-temporal mixture model. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.5.

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This article focuses on the use of a spatio-temporal mixture model for mapping malaria in the Amazon rain forest. The spatio-temporal model was developed to study malaria outbreaks over a four year period in the state of Amazonas, Brazil. The goal is to predict malaria counts for unobserved municipalities and future time periods with the aid of a free-form spatial covariance structure and a methodology that allows temporal prediction and spatial interpolation for outbreaks of malaria over time. The proposed structure is unique in that it is not a distance- or neighbourhood-based covariance model. Instead, spatial correlation is allowed among all locations to be estimated freely. To model the temporal correlation between observations, a Bayesian dynamic linear model is incorporated into one level of the spatio-temporal mixture model. The model also provides sensible ways of malaria mapping for municipalities which were not observed.
30

Fieger, Andreas. Fehlende Kovariablenwerte Bei Linearen Regressionsmodellen (Texte Und Untersuchungen Zur Germanistik Und Skandinavistik). Peter Lang Publishing, 2001.

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31

W, Green David, and Forest Products Laboratory (U.S.), eds. Predictor sort sampling, tight t's, and the analysis of covariance: Theory, tables, and examples. Madison, WI (One Gifford Pinchot Dr., Madison 53705-2398): U.S. Dept. of Agriculture, Forest Service, Forest Products Laboratory, 1996.

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32

W, Green David, and Forest Products Laboratory (U.S.), eds. Predictor sort sampling, tight t's, and the analysis of covariance: Theory, tables, and examples. Madison, WI (One Gifford Pinchot Dr., Madison 53705-2398): U.S. Dept. of Agriculture, Forest Service, Forest Products Laboratory, 1996.

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33

McCleary, Richard, David McDowall, and Bradley J. Bartos. ARIMA Algebra. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0002.

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The goal of Chapter 2 is to derive the properties of common processes and, based on these properties, to develop a general scheme for classifying processes. Stationary processes includes white noise, moving average (MA), and autoregressive (AR) processes. MA and AR models can approximate mixed ARMA models. A lag or backshift operator is used to solve ARIMA models for time series observations or random shocks. Covariance functions are derived for each of the common processes.Maximum likelihood estimates are introduced for the purposes of estimating autoregressive and moving average parameters.
34

Mann, Peter. Lagrangian Field Theory. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198822370.003.0025.

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In this chapter, Hamiltonian field theory is derived classically via a Hamiltonian density, using the zeroth component of a 4-momentum density. In field theory, space and time are considered to be on equal footing but, in the canonical formalism, time is treated as being special and therefore, by definition, it is not covariant. Consequently, most field theoretic models are built on Lagrangian formulations. A covariant canonical formalism is the subject of the de Donder–Weyl formalism, which is briefly discussed as a covariant Hamiltonian field theory. In addition, the chapter examines the case of a generalised Poisson bracket in the continuous form for two local smooth functionals of phase space.
35

Structural Equation Modeling: A Second Course (Quantitative Methods in Education and the Behavioral Sciences) (Quantitative Methods in Education and the Behavioral Sciences). IAP - Information Age Publishing Inc., 2006.

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36

Structural equation modeling: A second course. Greenwich, CT: IAP, 2006.

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37

Tully, Erin C., and William Iacono. An Integrative Common Liabilities Model for the Comorbidity of Substance Use Disorders with Externalizing and Internalizing Disorders. Edited by Kenneth J. Sher. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199381708.013.20.

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This chapter presents an integrative research-derived model to explain comorbidity among substance use disorders (SUDs), externalizing disorders, and internalizing disorders. This hierarchical model is based on phenotypic covariance among the disorders and latent common genetic liability. At the highest level of the hierarchy, general genetically influenced biological dispositions to negative emotionality and behavioral disinhibition each give rise to spectra of related personality traits, cognitive processes, behavioral tendencies, and psychopathology that account for the pattern of co-occurrence among mental disorders. At the lowest level of the hierarchy, disorder-specific genetic and environmental effects explain the presence of some and not other disorders associated with a given general liability. Interplay between the general liabilities and both other genes and environmental factors throughout development affect the likelihood of developing specific mental disorders.
38

Structural Equation Modeling: A Second Course. Information Age Publishing, 2006.

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39

McCleary, Richard, David McDowall, and Bradley J. Bartos. Statistical Conclusion Validity. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0006.

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Chapter 6 addresses the sub-category of internal validity defined by Shadish et al., as statistical conclusion validity, or “validity of inferences about the correlation (covariance) between treatment and outcome.” The common threats to statistical conclusion validity can arise, or become plausible through either model misspecification or through hypothesis testing. The risk of a serious model misspecification is inversely proportional to the length of the time series, for example, and so is the risk of mistating the Type I and Type II error rates. Threats to statistical conclusion validity arise from the classical and modern hybrid significance testing structures, the serious threats that weigh heavily in p-value tests are shown to be undefined in Beyesian tests. While the particularly vexing threats raised by modern null hypothesis testing are resolved through the elimination of the modern null hypothesis test, threats to statistical conclusion validity would inevitably persist and new threats would arise.
40

Ortaçgil, Ercüment H. An Alternative Approach to Lie Groups and Geometric Structures. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198821656.001.0001.

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This book is about the foundations of geometric symmetry, namely, Lie groups and differential geometry. Although this is a classical subject about which hundreds of books have been written, this book takes a new and innovative approach. The main idea is to replace the Maurer–Cartan form with absolute parallelism and its curvature. Unlike the classical approach, where the model is fixed beforehand by the Maurer–Cartan form, this new approach is model-free, and also revisits the foundational concepts of differential geometry, such as covariant differentiation, from a different perspective.
41

James, Bridges, Freund Jonathan B, and NASA Glenn Research Center, eds. A parametric study of fine-scale turbulence mixing noise. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2002.

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42

Delsol, Laurent. Nonparametric Methods for α-Mixing Functional Random Variables. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.5.

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This article considers how functional kernel methods can be used to study α-mixing datasets. It first provides an overview of how prediction problems involving dependent functional datasets may arise from the study of time series, focusing on the standard discretized model and modelization that takes into account the functional nature of the evolution of the quantity to be studied over time. It then considers strong mixing conditions, with emphasis on the notion of α-mixing coefficients and α-mixing variables introduced by Rosenblatt (1956). It also describes some conditions for a Markov chain to be α-mixing; some useful tools that provide covariance inequalities, exponential inequalities, and Central Limit Theorem (CLT) for α-mixing sequences; the asymptotic properties of functional kernel estimators; the use of kernel smoothing methods with α-mixing datasets; and various functional kernel estimators corresponding to different prediction methods. Finally, the article highlights some interesting prospects for further research.
43

Li, Quan. Using R for Data Analysis in Social Sciences. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656218.001.0001.

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This book seeks to teach undergraduate and graduate students in social sciences how to use R to manage, visualize, and analyze data in order to answer substantive questions and replicate published findings. This book distinguishes itself from other introductory R or statistics books in three ways. First, targeting an audience rarely exposed to statistical programming, it adopts a minimalist approach and covers only the most important functions and skills in R that one will need for conducting reproducible research projects. Second, it emphasizes meeting the practical needs of students using R in research projects. Specifically, it teaches students how to import, inspect, and manage data; understand the logic of statistical inference; visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots; and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. Third, it teaches students how to replicate the findings in published journal articles and diagnose model assumption violations. The principle behind this book is to teach students to learn as little R as possible but to do as much reproducible, substance-driven data analysis at the beginner or intermediate level as possible. The minimalist approach dramatically reduces the learning cost but still proves adequate information for meeting the practical research needs of senior undergraduate and beginning graduate students. Having completed this book, students can use R and statistical analysis to answer questions regarding some substantively interesting continuous outcome variable in a cross-sectional design.
44

Advances And Challenges In Spacetime Modelling Of Natural Events. Springer, 2012.

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