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Books on the topic 'Mean-variance Analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Sentana, Enrique. Least squares predictions and mean-variance analysis. London School of Economics, Financial Markets Group, 1999.

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2

Johnson, Laura D. Smoothing spatial data by estimating mean local variance. Naval Postgraduate School, 1988.

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3

Markowitz, H. Mean-variance analysis in portfolio choice and capital markets. Basil Blackwell, 1987.

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4

Markowitz, H. Mean-variance analysis in portfolio choice and capital markets. Blackwell, 1990.

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5

Markowitz, H. Mean-variance analysis in portfolio choice and capital markets. B. Blackwell, 1987.

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6

Markowitz, H. Mean-variance analysis in portfolio choice and capital markets. Frank J. Fabozzi Associates, 1987.

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7

Johnson, D. G. The robustness of mean and variance approximations in pert and risk analysis. Loughborough University Business School, 1997.

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8

O'Gorman, Aongus J. Mean-risk analysis: An examination of semivariance as an alternative to the traditional risk measure of variance. University College Dublin, 1994.

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9

Back, Kerry E. Mean-Variance Analysis. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0005.

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The mean‐variance frontier is characterized with and without a risk‐free asset. The global minimum variance portfolio and tangency portfolio are defined, and two‐fund spanning is explained. The frontier is characterized in terms of the return defined from the SDF that is in the span of the assets. This is related to the Hansen‐Jagannathan bound. There is an SDF that is an affine function of a return if and only if the return is on the mean‐variance frontier. Separating distributions are defined and shown to imply two‐fund separation and mean‐variance efficiency of the market portfolio.

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10

Markowitz, H. Mean-Variance Analysis in Portfolio Choice and Capital Markets. Wiley & Sons, Incorporated, John, 2008.

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11

Markowitz, H. Mean-Variance Analysis in Portfolio Choice and Capital Markets. Blackwell Pub, 1991.

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12

Markowitz, H. Mean-Variance Analysis in Portfolio Choice and Capital Markets. Wiley, 2000.

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13

Johnson, D. The robustness of mean and variance approximations in pert and risk analysis. Loughborough University Business School, 1997.

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14

Miles, Jeremy. General and generalised linear models. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0017.

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This chapter discusses general and generalised linear models (GLM and GLZ respectively). It outlines GLMs (mean, properties of GLMs and the mean), samples and populations, comparison of two groups of data, multiple regression and the GLM, analysis of variance (ANOVA) and the GLM, GLM in SPSS, and the GLZ).

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15

NANDE-VÁZQUEZ, Edgard Alfredo, Teodoro REYES-FONG, and Omar Alejandro PÉREZ-CRUZ. The Generalized Least Squares Method (GMM) as a tool for causal analysis of spending, budget management and electoral results. ECORFAN, 2021. http://dx.doi.org/10.35429/b.2021.8.1.130.

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In the different fields of science, many times, there is a need to estimate the associations between variables, as an approach to understanding the interaction of one as a function of the others. It is usually done by applying restrictive models, such as analysis of variance and linear regression. This type of analysis requires that the dependent variable be continuous, have a normal and constant distribution of the mean and variance. However, when the dependent variable is discrete or categorical, the linear model is not viable. Faced with this impediment, the theory of linear models arises a

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