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Author: Dilanka Shenal Dedduwakumara
Publisher:
ISBN:
Category : Statistics
Languages : en
Pages : 260
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Book Description
Statistical modeling and estimation of quantiles is an integral part of statistical data analysis in interpreting real-world phenomena. The purpose of this thesis is to contribute to the existing body of knowledge in quantile-based methods in modeling and estimation while providing simulation studies and real data applications supporting the new contributions. The results are discussed coherently in the thesis, including original publications that have been either published, accepted to be published or submitted for peer review. In the first part of the thesis, we propose a new approach based on the Probability Density Quantile (pdQ) for parameter estimation of the Generalized Lambda Distribution (GLD). Defined in terms of a location parameter, scale parameter, and two shape parameters, the GLD is widely used for modeling in many fields because of its flexibility in being able to mimic many other distributions. However, due to there being four parameters, choosing optimal parameters and/or estimating those parameters is not straightforward. We compare our pdQ approach with the existing methods in regards to time efficiency and performance. Further, we extend the introduced method for the Generalized Beta Distribution, illustrating the applicability of the method more broadly than just the GLD. In the second part of the thesis, we introduce several methods, including a method based on the GLD, to obtain confidence intervals for quantiles when only a frequency distribution or histogram is available. These methods are extended to measuring inequality for grouped income data where data is often provided in such summary format to protect the confidentiality of individuals. Here we show that interval estimators for quantile-based inequality measures are suited to this type of data. The thesis also includes two web-based Shiny Applications for end-users to apply these methods in their research.
Author: Dilanka Shenal Dedduwakumara
Publisher:
ISBN:
Category : Statistics
Languages : en
Pages : 260
Get Book Here
Book Description
Statistical modeling and estimation of quantiles is an integral part of statistical data analysis in interpreting real-world phenomena. The purpose of this thesis is to contribute to the existing body of knowledge in quantile-based methods in modeling and estimation while providing simulation studies and real data applications supporting the new contributions. The results are discussed coherently in the thesis, including original publications that have been either published, accepted to be published or submitted for peer review. In the first part of the thesis, we propose a new approach based on the Probability Density Quantile (pdQ) for parameter estimation of the Generalized Lambda Distribution (GLD). Defined in terms of a location parameter, scale parameter, and two shape parameters, the GLD is widely used for modeling in many fields because of its flexibility in being able to mimic many other distributions. However, due to there being four parameters, choosing optimal parameters and/or estimating those parameters is not straightforward. We compare our pdQ approach with the existing methods in regards to time efficiency and performance. Further, we extend the introduced method for the Generalized Beta Distribution, illustrating the applicability of the method more broadly than just the GLD. In the second part of the thesis, we introduce several methods, including a method based on the GLD, to obtain confidence intervals for quantiles when only a frequency distribution or histogram is available. These methods are extended to measuring inequality for grouped income data where data is often provided in such summary format to protect the confidentiality of individuals. Here we show that interval estimators for quantile-based inequality measures are suited to this type of data. The thesis also includes two web-based Shiny Applications for end-users to apply these methods in their research.
Author: Marco Geraci
Publisher: Wiley
ISBN: 9781118856727
Category : Mathematics
Languages : en
Pages : 500
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Book Description
The book is devoted to quantile-based methods of analysis. It is divided in three parts. Part I introduces general topics in statistics and sets out the goals of statistical analysis and describes the double-faced nature of statistical distributions, namely probability and quantile functions and how the latter can be used to extract information from the data. In particular, chapter 3 (location, scale and shape of probability distributions) describes where such information resides; this is a recurring theme throughout the book and is further developed in Chapters 8 and 14. While inferential procedures based on modelling probability functions have been widely described in a number of statistical textbooks, scientific contributions to the development of quantile-based inference are sparse and lack a comprehensive treatment. The main topics of the book are discussed in parts II and III, which introduce methods and applications for unconditional and conditional quantiles. Each part considers: the distribution-free approach, in which quantile estimation makes no use of parametric probability models; and the model-based approach, in which the quantile function is defined as the inverse of a known distribution function, thus quantile estimation conforms to some statistical model (e.g., Normal, exponential, Pareto). The book emphasises that in a quantile model-based approach the modelling step starts from the quantile function directly (as opposed to modelling the distribution function and deriving the quantiles by inversion).
Author: Bernd Fitzenberger
Publisher: Springer Science & Business Media
ISBN: 3662115921
Category : Business & Economics
Languages : en
Pages : 325
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Book Description
Quantile regression has emerged as an essential statistical tool of contemporary empirical economics and biostatistics. Complementing classical least squares regression methods which are designed to estimate conditional mean models, quantile regression provides an ensemble of techniques for estimating families of conditional quantile models, thus offering a more complete view of the stochastic relationship among variables. This volume collects 12 outstanding empirical contributions in economics and offers an indispensable introduction to interpretation, implementation, and inference aspects of quantile regression.
Author: Bruno Tuffin
Publisher: Springer Nature
ISBN: 3030434656
Category : Computers
Languages : en
Pages : 533
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Book Description
This book presents the refereed proceedings of the 13th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing that was held at the University of Rennes, France, and organized by Inria, in July 2018. These biennial conferences are major events for Monte Carlo and quasi-Monte Carlo researchers. The proceedings include articles based on invited lectures as well as carefully selected contributed papers on all theoretical aspects and applications of Monte Carlo and quasi-Monte Carlo methods. Offering information on the latest developments in these very active areas, this book is an excellent reference resource for theoreticians and practitioners interested in solving high-dimensional computational problems, arising, in particular, in finance, statistics and computer graphics.
Author: David H. Autor
Publisher: DIANE Publishing
ISBN: 143798018X
Category : Income distribution
Languages : en
Pages : 67
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Book Description
We reassess the effect of state and federal minimum wages on U.S. earnings inequality using two additional decades of data and far greater variation in minimum wages than was available to earlier studies. We argue that prior literature suffers from two sources of bias and propose an IV strategy to address both. We find that the minimum wage reduces inequality in the lower tail of the wage distribution (the 50/10 wage ratio), but the impacts are typically less than half as large as those reported elsewhere and are almost negligible for males. Nevertheless, the estimated effects extend to wage percentiles where the minimum is nominally non-binding, implying spillovers. However, we show that spillovers and measurement error (absent spillovers) have similar implications for the effect of the minimum on the shape of the lower tail of the measured wage distribution. With available precision, we cannot reject the hypothesis that estimated spillovers to non-binding percentiles are due to reporting artifacts. Accepting this null, the implied effect of the minimum wage on the actual wage distribution is smaller than the effect of the minimum wage on the measured wage distribution.
Author: J. Pfanzagl
Publisher: Springer Science & Business Media
ISBN: 1461257697
Category : Mathematics
Languages : en
Pages : 324
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Book Description
Author: Lingxin Hao
Publisher: SAGE Publications
ISBN: 1483316904
Category : Social Science
Languages : en
Pages : 142
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Book Description
Quantile Regression, the first book of Hao and Naiman′s two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao and Naiman show, in their application of quantile regression to empirical research, how this model yields a more complete understanding of inequality. Inequality is a perennial concern in the social sciences, and recently there has been much research in health inequality as well. Major software packages have also gradually implemented quantile regression. Quantile Regression will be of interest not only to the traditional social science market but other markets such as the health and public health related disciplines. Key Features: Establishes a natural link between quantile regression and inequality studies in the social sciences Contains clearly defined terms, simplified empirical equations, illustrative graphs, empirical tables and graphs from examples Includes computational codes using statistical software popular among social scientists Oriented to empirical research
Author: John R. Birge
Publisher: Elsevier
ISBN: 9780080553252
Category : Business & Economics
Languages : en
Pages : 1026
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Book Description
The remarkable growth of financial markets over the past decades has been accompanied by an equally remarkable explosion in financial engineering, the interdisciplinary field focusing on applications of mathematical and statistical modeling and computational technology to problems in the financial services industry. The goals of financial engineering research are to develop empirically realistic stochastic models describing dynamics of financial risk variables, such as asset prices, foreign exchange rates, and interest rates, and to develop analytical, computational and statistical methods and tools to implement the models and employ them to design and evaluate financial products and processes to manage risk and to meet financial goals. This handbook describes the latest developments in this rapidly evolving field in the areas of modeling and pricing financial derivatives, building models of interest rates and credit risk, pricing and hedging in incomplete markets, risk management, and portfolio optimization. Leading researchers in each of these areas provide their perspective on the state of the art in terms of analysis, computation, and practical relevance. The authors describe essential results to date, fundamental methods and tools, as well as new views of the existing literature, opportunities, and challenges for future research.
Author: Badi H. Baltagi
Publisher: Oxford University Press
ISBN: 0190210826
Category : Business & Economics
Languages : en
Pages : 705
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Book Description
The Oxford Handbook of Panel Data examines new developments in the theory and applications of panel data. It includes basic topics like non-stationary panels, co-integration in panels, multifactor panel models, panel unit roots, measurement error in panels, incidental parameters and dynamic panels, spatial panels, nonparametric panel data, random coefficients, treatment effects, sample selection, count panel data, limited dependent variable panel models, unbalanced panel models with interactive effects and influential observations in panel data. Contributors to the Handbook explore applications of panel data to a wide range of topics in economics, including health, labor, marketing, trade, productivity, and macro applications in panels. This Handbook is an informative and comprehensive guide for both those who are relatively new to the field and for those wishing to extend their knowledge to the frontier. It is a trusted and definitive source on panel data, having been edited by Professor Badi Baltagi-widely recognized as one of the foremost econometricians in the area of panel data econometrics. Professor Baltagi has successfully recruited an all-star cast of experts for each of the well-chosen topics in the Handbook.
Author: Christian Gouriéroux
Publisher: OUP Oxford
ISBN: 019152509X
Category : Business & Economics
Languages : en
Pages : 190
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Book Description
This book introduces a new generation of statistical econometrics. After linear models leading to analytical expressions for estimators, and non-linear models using numerical optimization algorithms, the availability of high- speed computing has enabled econometricians to consider econometric models without simple analytical expressions. The previous difficulties presented by the presence of integrals of large dimensions in the probability density functions or in the moments can be circumvented by a simulation-based approach. After a brief survey of classical parametric and semi-parametric non-linear estimation methods and a description of problems in which criterion functions contain integrals, the authors present a general form of the model where it is possible to simulate the observations. They then move to calibration problems and the simulated analogue of the method of moments, before considering simulated versions of maximum likelihood, pseudo-maximum likelihood, or non-linear least squares. The general principle of indirect inference is presented and is then applied to limited dependent variable models and to financial series.
