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Sieve Estimators for Spatial Data

Sieve Estimators for Spatial Data
Author: Johannes Theodor Nikolaus Krebs
Publisher:
Total Pages:
Release: 2017
Genre:
ISBN: 9783839611869

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Statistics for Spatial Data

Statistics for Spatial Data
Author: Noel Cressie
Publisher: John Wiley & Sons
Total Pages: 931
Release: 2015-03-18
Genre: Mathematics
ISBN: 1119115183

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The Wiley Classics Library consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. Spatial statistics — analyzing spatial data through statistical models — has proven exceptionally versatile, encompassing problems ranging from the microscopic to the astronomic. However, for the scientist and engineer faced only with scattered and uneven treatments of the subject in the scientific literature, learning how to make practical use of spatial statistics in day-to-day analytical work is very difficult. Designed exclusively for scientists eager to tap into the enormous potential of this analytical tool and upgrade their range of technical skills, Statistics for Spatial Data is a comprehensive, single-source guide to both the theory and applied aspects of spatial statistical methods. The hard-cover edition was hailed by Mathematical Reviews as an "excellent book which will become a basic reference." This paper-back edition of the 1993 edition, is designed to meet the many technological challenges facing the scientist and engineer. Concentrating on the three areas of geostatistical data, lattice data, and point patterns, the book sheds light on the link between data and model, revealing how design, inference, and diagnostics are an outgrowth of that link. It then explores new methods to reveal just how spatial statistical models can be used to solve important problems in a host of areas in science and engineering. Discussion includes: Exploratory spatial data analysis Spectral theory for stationary processes Spatial scale Simulation methods for spatial processes Spatial bootstrapping Statistical image analysis and remote sensing Computational aspects of model fitting Application of models to disease mapping Designed to accommodate the practical needs of the professional, it features a unified and common notation for its subject as well as many detailed examples woven into the text, numerous illustrations (including graphs that illuminate the theory discussed) and over 1,000 references. Fully balancing theory with applications, Statistics for Spatial Data, Revised Edition is an exceptionally clear guide on making optimal use of one of the ascendant analytical tools of the decade, one that has begun to capture the imagination of professionals in biology, earth science, civil, electrical, and agricultural engineering, geography, epidemiology, and ecology.


Modern Sieve Estimators for Nonparametric Problems

Modern Sieve Estimators for Nonparametric Problems
Author: Tianyu Zhang
Publisher:
Total Pages: 0
Release: 2022
Genre:
ISBN:

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Estimation of a regression function, linking a set of features to an outcome of interest, is a fundamental statistical task. This dissertation focuses on the application of sieve estimators in modern statistical learning problems. The method of sieves, or estimation via basis expansion, has its roots in Fourier analysis. In the past decades, it has achieved much success in smaller sample size, lower dimensional data science problems. In this dissertation, we will demonstrate its effectiveness in modern statistical learning settings. Sieve estimators can achieve statistical and computational optimality (almost) simultaneously, which makes them very suitable for online and/or large scale nonparametric estimation tasks. Sieve estimators can also be applied to high-dimensional nonparametric problems. They can effectively alleviate the “curse of dimensionality” by leveraging additional structures such as feature sparsity. For each topic covered in this dissertation, we will present both theoretical discussion and a variety of numerical examples.


Estimation and Inference in Functional-Coefficient Spatial Autoregressive Panel Data Models with Fixed Effects

Estimation and Inference in Functional-Coefficient Spatial Autoregressive Panel Data Models with Fixed Effects
Author: Yiguo Sun
Publisher:
Total Pages: 40
Release: 2017
Genre:
ISBN:

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This paper develops an innovative way of estimating a functional-coefficient spatial autoregressive panel data model with unobserved individual effects which can accommodate (multiple) time-invariant regressors in the model with a large number of cross-sectional units and a fixed number of time periods. The methodology we propose removes unobserved fixed effects from the model by transforming the latter into a semiparametric additive model, the estimation of which however does not require the use of backfitting or marginal integration techniques. We derive the consistency and asymptotic normality results for the proposed kernel and sieve estimators. We also construct a consistent nonparametric test to test for spatial endogeneity in the data. A small Monte Carlo study shows that our proposed estimators and the test statistic exhibit good finite-sample performance.


Advances in Economics and Econometrics

Advances in Economics and Econometrics
Author: Econometric Society. World Congress
Publisher: Cambridge University Press
Total Pages: 633
Release: 2013-05-27
Genre: Business & Economics
ISBN: 1107016061

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The third volume of edited papers from the Tenth World Congress of the Econometric Society 2010.


Subsampling

Subsampling
Author: Dimitris N. Politis
Publisher: Springer Science & Business Media
Total Pages: 359
Release: 2012-12-06
Genre: Mathematics
ISBN: 1461215544

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Since Efron's profound paper on the bootstrap, an enormous amount of effort has been spent on the development of bootstrap, jacknife, and other resampling methods. The primary goal of these computer-intensive methods has been to provide statistical tools that work in complex situations without imposing unrealistic or unverifiable assumptions about the data generating mechanism. This book sets out to lay some of the foundations for subsampling methodology and related methods.


Spatial Econometrics: Spatial Autoregressive Models

Spatial Econometrics: Spatial Autoregressive Models
Author: Lung-fei Lee
Publisher: World Scientific
Total Pages: 894
Release: 2023-10-16
Genre: Business & Economics
ISBN: 9811270503

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This is the most recently developed book in Spatial Econometrics which cover important models and estimation methods. Its coverage is rather broad, and some of the topics covered have only been developed in the recent econometric literature in spatial econometrics.The book summarizes our devoted efforts on spatial econometrics that represent joint contributions with former PhD advisees from the Ohio State University in Columbus, Ohio, USA.The coverage is comprehensive and there are a total of sixteen chapters from basic statistics and statistical theory of linear-quadratic forms, law of large numbers (LLN) and central limit theory (CLT) on martingales to nonlinear spatial mixing and spatial near-epoch dependence theories, which can justify the statistic inferences for various spatial models and their estimation. New estimation and testing approaches in empirical likelihood and general empirical likelihood, and Bootstrapping are presented. Model selection is also discussed in this book. In addition to the popular spatial autoregressive models, there are chapters on multivariate SAR models, simultaneous SAR models, and panel dynamic spatial models. Recent econometric developments on intertemporal spatial models with rational expectations and flows data in trade theory will also be included. In terms of statistics, classical estimation, testing and inference are the main concerns, and we provide classical inference for the justification of Bayesian simulation approaches.


The Oxford Handbook of Panel Data

The Oxford Handbook of Panel Data
Author: Badi Hani Baltagi
Publisher:
Total Pages: 705
Release: 2015
Genre: Business & Economics
ISBN: 0199940045

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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.