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Books on the topic 'Non-parametric and semiparametric model'

Author: Grafiati

Published: 4 May 2024

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1

Schafgans, Marcia M. A. Gender wage differences in Malaysia: Parametric and semiparametric estimation. London: Suntory and Toyota International Centres for Economics and Related Disciplines, 1997.

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2

Aman, Ullah, ed. Semiparametric and nonparametric econometrics. Heidelberg: Physica-Verlag, 1989.

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3

Balakrishnan, N., M. S. Nikulin, M. Mesbah, and N. Limnios, eds. Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life. Boston, MA: Birkhäuser Boston, 2004. http://dx.doi.org/10.1007/978-0-8176-8206-4.

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4

Knight, John L. Pricing interest rate derivatives in a non-parametric two-factor term-structure model. [Ottawa]: Bank of Canada, 1999.

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5

Wood, Douglas. The United Kingdom overnight interbank market: A non-parametric nonlinear directional tracking model. Manchester: Manchester Business School, 1995.

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6

Brandt, James M. A parametric cost model for estimating operating and support costs of US Navy (Non-Nuclear) surface ships. Monterey, Calif: Naval Postgraduate School, 1999.

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7

Semiparametric Odds Ratio Model and Its Applications. Taylor & Francis Group, 2021.

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8

Chen, Hua Yun. Semiparametric Odds Ratio Model and Its Applications. CRC Press LLC, 2021.

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9

Chen, Hua Yun. Semiparametric Odds Ratio Model and Its Applications. Taylor & Francis Group, 2021.

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10

Chen, Hua Yun. Semiparametric Odds Ratio Model and Its Applications. Taylor & Francis Group, 2021.

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11

Chen, Hua Yun. Semiparametric Odds Ratio Model and Its Applications. Taylor & Francis Group, 2021.

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12

Ferraty, Frédéric, and Philippe Vieu. A Unifying Classification for Functional Regression Modeling. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.1.

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This article presents a unifying classification for functional regression modeling, and more specifically for modeling the link between two variables X and Y, when the explanatory variable (X) is of a functional nature. It first provides a background on the proposed classification of regression models, focusing on the regression problem and defining parametric, semiparametric, and nonparametric models, and explains how semiparametric modeling can be interpreted in terms of dimension reduction. It then gives four examples of functional regression models, namely: functional linear regression model, additive functional regression model, smooth nonparametric functional model, and single functional index model. It also considers a number of new models, directly adapted to functional variables from the existing standard multivariate literature.

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13

Cumming, Jonathan A., and Michael Goldstein. Bayesian analysis and decisions in nuclear power plant maintenance. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.9.

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This article discusses the results of a study in Bayesian analysis and decision making in the maintenance and reliability of nuclear power plants. It demonstrates the use of Bayesian parametric and semiparametric methodology to analyse the failure times of components that belong to an auxiliary feedwater system in a nuclear power plant at the South Texas Project (STP) Electric Generation Station. The parametric models produce estimates of the hazard functions that are compared to the output from a mixture of Polya trees model. The statistical output is used as the most critical input in a stochastic optimization model which finds the optimal replacement time for a system that randomly fails over a finite horizon. The article first introduces the model for maintenance and reliability analysis before presenting the optimization results. It also examines the nuclear power plant data to be used in the Bayesian models.

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14

Nikulin, M. S., N. Balakrishnan, and Mounir Mesbah. Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life. Springer, 2011.

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15

Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life. Birkhäuser, 2012.

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16

Cheng, Russell. Non-Standard Parametric Statistical Inference. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.001.0001.

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This book discusses the fitting of parametric statistical models to data samples. Emphasis is placed on (i) how to recognize situations where the problem is non-standard, when parameter estimates behave unusually, and (ii) the use of parametric bootstrap resampling methods in analysing such problems. Simple and practical model building is an underlying theme. A frequentist viewpoint based on likelihood is adopted, for which there is a well-established and very practical theory. The standard situation is where certain widely applicable regularity conditions hold. However, there are many apparently innocuous situations where standard theory breaks down, sometimes spectacularly. Most of the departures from regularity are described geometrically in the book, with mathematical detail only sufficient to clarify the non-standard nature of a problem and to allow formulation of practical solutions. The book is intended for anyone with a basic knowledge of statistical methods typically covered in a university statistical inference course who wishes to understand or study how standard methodology might fail. Simple, easy-to-understand statistical methods are presented which overcome these difficulties, and illustrated by detailed examples drawn from real applications. Parametric bootstrap resampling is used throughout for analysing the properties of fitted models, illustrating its ease of implementation even in non-standard situations. Distributional properties are obtained numerically for estimators or statistics not previously considered in the literature because their theoretical distributional properties are too hard to obtain theoretically. Bootstrap results are presented mainly graphically in the book, providing easy-to-understand demonstration of the sampling behaviour of estimators.

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17

(Editor), Mikhail S. Nikulin, N. Balakrishnan (Editor), M. Mesbah (Editor), and Nikolaos Limnios (Editor), eds. Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life (Statistics for Industry and Technology). Birkhäuser Boston, 2004.

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18

A Parametric Cost Model for Estimating Operating and Support Costs of U. S. Navy (Non-Nuclear) Surface Ships. Storming Media, 1999.

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19

Cheng, Russell. Introduction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0001.

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This chapter provides an overview of the book. The book investigates non-standard parametric, mainly continuous univariate estimation problems. The basic difference between standard and non-standard problems is explained in this chapter. The book considers different non-standard problems that can arise. Though some of the problems are advanced, a strong emphasis is placed on providing statistical methods to analyse them that are simple to understand and implement. Maximum likelihood (ML) estimation is the main method used to estimate parameters when fitting parametric models. This chapter outlines the method, emphasizing how it can be implemented numerically. Parametric bootstrapping is used throughout the book to analyse the statistical behaviour of estimators. This chapter gives the rationale of the approach, explaining its simplicity and wide applicability. Also explained is the underlying model building theme of the book.

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20

Brazier, John, Julie Ratcliffe, Joshua A. Salomon, and Aki Tsuchiya. Modelling health state valuation data. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198725923.003.0005.

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This chapter examines the technical issues in modelling health state valuation data. Most measures of health define too many states to directly value all of them (e.g. SF-6D defines 18,000 health states). The solution has been to value a subset and by using modelling to predict the values of all states. This chapter reviews two approaches to modelling: one using multiattribute utility theory to determine health values given an assumed functional form; and the other is using statistical modelling of SF-6D preference data that are skewed, bimodal, and clustered by respondents. This chapter examines the selection of health states for valuation, data preparation, model specification, and techniques for modelling the data starting with ordinary least squares (OLS) and moving on to more complex techniques including Bayesian non-parametric and semi-parametric approaches, and a hybrid approach that combines cardinal preference data with the results of paired data from a discrete choice experiment.

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21

Brůha, Jan, and Oxana Babecká Kucharčuková. Growth, Unemployment, and Wages in EU Countries since the Great Recession. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198821878.003.0004.

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In this chapter, we contribute to the research investigating how institutions and regulations affect the resilience of countries to adverse macroeconomic shocks. To do this, we apply a hierarchical non-parametric curve fitting model to compare economic growth and labour market developments in EU countries since the beginning of the Great Recession. Using the model, we identify four latent classes that represent distinct patterns of the labour market and economic developments. We present evidence that countries in the different classes systematically differ by labour market regulation and quality of institutions. This demonstrates the relevance of institutions and regulation for economies’ resilience to shocks.

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22

Cheng, Russell. Bootstrapping Linear Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0016.

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Bootstrap model selection is proposed for the difficult problem of selecting important factors in non-orthogonal linear models when the number of factors, P, is large. In the method, the full model is first fitted to the original data. Then B parametric bootstrap samples are drawn from the fitted model, and the full model fitted to each. A submodel is obtained from each fitted full model by rejecting those factors found unimportant in the fit. Each distinct selected submodel is then fitted to the original data and its Mallows Cp statistic calculated. A subset of good submodels based on the Cp values is then obtained. A reliability check can be made by fitting this subset to the BS samples also, to see how often each submodel is found to be a good fit. Use of the method is illustrated using a real-data sample.

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23

Cheng, Russell. Change-Point Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0011.

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This chapter investigates change-point (hazard rate) probability models for the random survival time in some population of interest. A parametric probability distribution is assumed with parameters to be estimated from a sample of observed survival times. If a change-point parameter, denoted by τ‎, is included to represent the time at which there is a discrete change in hazard rate, then the model is non-standard. The profile log-likelihood, with τ‎ as profiling parameter, has a discontinuous jump at every τ‎ equal to a sampled value, becoming unbounded as τ‎ tends to the largest observation. It is known that maximum likelihood estimation can still be used provided the range of τ‎ is restricted. It is shown that the alternative maximum product of spacings method is consistent without restriction on τ‎. Censored observations which commonly occur in survival-time data can be accounted for using Kaplan-Meier estimation. A real data numerical example is given.

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