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Books on the topic 'Limited dependent variable'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Lee, Myoung-jae. Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2550-6.

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2

Yokoyama, Yoshimitsu. An application of the limited dependent variable model under rational expectations. Kagawa, Japan: Kagawa University, 1996.

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3

Lee, Myoung-jae. Methods of moments and semiparametric econometrics for limited dependent and variable models. New York: Springer, 1996.

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4

Angrist, Joshua David. Estimation of limited-dependent variable models with dummy endogenous regressors: Simple strategies for empirical practice. Cambridge, Mass: Massachusetts Institute of Technology, Dept. of Economics, 1999.

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5

Statistical models for the social and behavioral sciences: Multiple regression and limited-dependent variable models. Westport, Conn: Praeger, 1998.

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6

Keithly, Walter Russell. Socioeconomic determinants of at-home seafood consumption: A limited dependent variable analysis of existingand latent consumers. Ann Arbor, Mich: University Microfilms International, 1988.

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7

Maddala, G. S. Limited-dependent and qualitative variables in econometrics. Cambridge [Cambridgeshire]: Cambridge University Press, 1987.

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8

Regression models for categorical and limited dependent variables. Thousand Oaks: Sage Publications, 1997.

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9

Altonji, Joseph G. Estimating derivatives in nonseparable models with limited dependent variables. Cambridge, MA: National Bureau of Economic Research, 2008.

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10

Panel data econometrics: Methods-of-moments and limited dependent variables. San Diego: Academic Press, 2002.

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11

Lin, Cheng-yen. Limit theory for mixing dependent random variables. New York: Science Press, 1996.

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12

Lin, Zhengyan. Limit theory for mixing dependent random variables. Dordrecht: Kluwer Academic Publishers, 1996.

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13

Lin, Zhengyan. Limit theory for mixing dependent random variables. Dordrecht: Springer, 2011.

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14

1975-, Sims Robert, and Ueltschi Daniel 1969-, eds. Entropy and the quantum II: Arizona School of Analysis with Applications, March 15-19, 2010, University of Arizona. Providence, R.I: American Mathematical Society, 2011.

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15

Chen, Jinyong. Bootstrap methods in limited dependent variable models. 1993.

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16

Analysis of Panels and Limited Dependent Variable Models. University of Cambridge ESOL Examinations, 2010.

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17

Hsiao, Cheng, M. Hashem Pesaran, Kajal Lahiri, and Lung Fei Lee, eds. Analysis of Panels and Limited Dependent Variable Models. Cambridge University Press, 1999. http://dx.doi.org/10.1017/cbo9780511493140.

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18

Richard, Blundell, ed. Specification testing in limited and discrete dependent variable models. Amsterdam: North-Holland, 1987.

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19

Lee, Myoung-j., and Myoung-jae Lee. Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models. 2nd ed. Springer, 2006.

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20

Gatti, Juan Carlos Reberte. A limited dependent variable model of consumers' valuation of dairy product characteristics. 1993.

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21

1943-, Hsiao Cheng, ed. Analysis of panels and limited dependent variable models: In honour of G.S. Maddala. Cambridge, [U.K.]: Cambridge University Press, 1999.

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22

Spatial Econometrics: Qualitative and Limited Dependent Variables. Emerald Publishing Limited, 2016.

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23

Baltagi, Badi H., James P. LeSage, and R. Kelley Pace. Spatial Econometrics: Qualitative and Limited Dependent Variables. Emerald Publishing Limited, 2016.

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24

Baltagi, Badi H., James P. Lesage, and R. Kelley Pace, eds. Spatial Econometrics: Qualitative and Limited Dependent Variables. Emerald Group Publishing Limited, 2016. http://dx.doi.org/10.1108/s0731-9053201737.

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25

Lee, Myoung-jae. Micro-Econometrics: Methods of Moments and Limited Dependent Variables. Springer, 2014.

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26

Lee, Myoung-jae. Micro-Econometrics: Methods of Moments and Limited Dependent Variables. Springer, 2008.

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27

Limited-Dependent and Qualitative Variables in Econometrics (Econometric Society Monographs). Cambridge University Press, 1986.

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28

Generalized Linear Models For Categorical And Continuous Limited Dependent Variables. Taylor & Francis Inc, 2013.

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29

Smithson, Michael, and Edgar C. Merkle. Generalized Linear Models for Categorical and Continuous Limited Dependent Variables. Chapman and Hall/CRC, 2013. http://dx.doi.org/10.1201/b15694.

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30

Jeliazkov, Ivan, and Justin Tobias. Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling. Emerald Publishing Limited, 2019.

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31

Jeliazkov, Ivan, and Justin Tobias. Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling. Emerald Publishing Limited, 2019.

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32

Jeliazkov, Ivan, and Justin Tobias. Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling. Emerald Publishing Limited, 2019.

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33

Jeliazkov, Ivan, and Justin Tobias. Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling. Emerald Publishing Limited, 2019.

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34

Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A. Emerald Publishing Limited, 2019. http://dx.doi.org/10.1108/s0731-9053201940a.

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35

Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B. Emerald Publishing Limited, 2019. http://dx.doi.org/10.1108/s0731-9053201940b.

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36

Colaresi, Michael, and Jude C. Hays. Spatial and Temporal Interdependence. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190846626.013.301.

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Time and space are two dimensions that are likely to provide the paths—either singly or in tandem—by which international policy decisions are interdependent. There are several reasons to expect international relations processes to be interdependent across space, time, or both dimensions. Theoretical approaches such as rational expectations models, bureaucratic models of decision-making, and psychological explanations of international phenomena at least implicitly assume—and in many cases explicitly predict—dependence structures within data. One approach that researchers can use to test whether their international processes of interest are marked by dependence across time, space, or both time and space, is to explicitly model and interpret the hypothesized underlying dependence structures. There are two areas of spatial modeling at the research frontier: spatial models with qualitative and limited dependent variables, an co-evolution models of structure and behavior. These models have theoretical implications that are likely to be useful for international relations research. However, a gap remains between the kinds of empirical models demanded by international relations data and theory and the supply of time series and spatial econometric models that are available to those doing applied research. There is a need to develop appropriate models of temporal and spatial interdependence for qualitative and limited dependent variables, and for better models in which outcomes and structures of interdependence are jointly endogenous.

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37

Merlevède, Florence, Magda Peligrad, and Sergey Utev. Functional Gaussian Approximation for Dependent Structures. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198826941.001.0001.

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This book has its origin in the need for developing and analyzing mathematical models for phenomena that evolve in time and influence each another, and aims at a better understanding of the structure and asymptotic behavior of stochastic processes. This monograph has double scope. First, to present tools for dealing with dependent structures directed toward obtaining normal approximations. Second, to apply the normal approximations presented in the book to various examples. The main tools consist of inequalities for dependent sequences of random variables, leading to limit theorems, including the functional central limit theorem (CLT) and functional moderate deviation principle (MDP). The results will point out large classes of dependent random variables which satisfy invariance principles, making possible the statistical study of data coming from stochastic processes both with short and long memory. Over the course of the book different types of dependence structures are considered, ranging from the traditional mixing structures to martingale-like structures and to weakly negatively dependent structures, which link the notion of mixing to the notions of association and negative dependence. Several applications have been carefully selected to exhibit the importance of the theoretical results. They include random walks in random scenery and determinantal processes. In addition, due to their importance in analyzing new data in economics, linear processes with dependent innovations will also be considered and analyzed.

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38

Zhengyan, Lin, and Lu Chuanrong. Limit Theory for Mixing Dependent Random Variables (Mathematics and Its Applications). Springer, 1997.

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39

Functional Gaussian Approximation For Dependent Structures. Oxford, UK: Oxford University Press, 2019.

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40

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.

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41

Elwood, Mark. Confounding. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682898.003.0007.

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This chapter gives the definition of confounding, a central issue in epidemiology and its dependence on two associations, with exposure and with outcome. It explains confounding in trials, cohort and case-control studies, and Simpson’s paradox. It explains the five methods of controlling confounding: restriction, randomisation, stratification, matching and multivariate methods. For randomised trials, the limits of randomisation, residual confounding, pre-stratification, intention-to-treat, management and explanatory trials, pragmatic trials are explained. It shows the Mantel–Haenszel risk ratio or odds ratio, direct and indirect standardisation, and effect modification. Frequency and individual matching, their value and limitations, over matching, confounding by indication, and calculation of matched odds ratio are shown. It explains multivariate methods, including linear, logistic, Poisson, and Cox’s proportionate hazards models, including the relationship between coefficients and odds ratios, dummy variables, conditional methods, and propensity scores.

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42

Sillis, Margaret, and David Longbottom. Chlamydiosis. Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780198570028.003.0017.

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Chlamydial pathogens cause a wide-range of infections and disease, known as chlamydioses, in humans, other mammals and birds. The causative organisms are Gram-negative obligate intracellular bacteria that undergo a unique biphasic developmental cycle involving the infectious elementary body and the metabolically-active, non-infectious reticulate body. At least two species, Chlamydophila psittaci and Chlamydophila abortus, are recognized as causes of zoonotic infections in humans worldwide, mainly affecting persons exposed to infected psittacine and other birds, especially ducks, turkeys, and pigeons, and less commonly to animals, particularly sheep. Outbreaks occur amongst aviary workers, poultry processing workers, and veterinarians. Infection is transmitted through inhalation of infected aerosols contaminated by avian droppings, nasal discharges, or products of ovine gestation or abortion. Person to person transmission is rare. Control strategies have met with variable success depending on the degree of compliance or enforcement of legislation. In the United Kingdom control is secondary, resulting from protection of national poultry flocks by preventing the importation of Newcastle disease virus using quarantine measures. Improved standards of husbandry, transport conditions, and chemoprophylaxis are useful for controlling reactivation of latent avian chlamydial infection. Vaccination has had limited effect in controlling ovine infection. Improved education of persons in occupational risk groups and the requirement for notification may encourage a more energetic approach to its control.

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43

Mone, Thomas. Organ donation. Edited by Jeremy R. Chapman. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199592548.003.0277.

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Kidney transplantation has been and continues to be dependent on the apparently unscientific and decidedly personal act of organ donation. In the best-performing regions of the world, 75–95% of those who are medically suitable actually become donors upon their deaths, but because of increasing rates of organ failure, even in these high-performing areas, waiting lists continue to grow. Deceased organ donation performance is highly variable even among medically developed countries, and it is especially challenged in countries with cultural, legal, ethical or religious, economic, clinical, or organizational practices that limit donation. Recognizing these challenges, the transplantation community has collaborated to identify and promulgate international best practices and to foster innovation in the management of deceased donation. The goal of this effort is to clarify the organizational structures, social change interventions, and medical practices necessary to maximize both living and deceased donation. Although donation practice differs significantly across countries, successful organ donation programmes share certain traits and practices that can be modified to fit varied medical delivery reimbursement and social systems and structures. The world’s best-performing donation programmes have focused on increasing the public’s and healthcare professionals’ trust in the donation process, ensuring equitable access to transplantation, and they have built donation organizations that borrow from the theory and practice of business and healthcare management systems. The critical processes, essential functions, job roles, and foundational principles of successful donation programmes require the use of the tools that have been shown to improve donation and increase transplantation, thereby reducing (or, ideally, ending) deaths on the waiting lists. The wider adoption of these tools by countries with fledgling or struggling organ donation would increase organ availability and its exploitation of the poor who in many countries become organ ‘vendors’ rather than donors.

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