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| Author | : David Luther Sylwester |
| Publisher | : |
| Total Pages | : 172 |
| Release | : 1965 |
| Genre | : Mathematical statistics |
| ISBN | : |
| Author | : STANFORD UNIV CALIF DEPT OF STATISTICS. |
| Publisher | : |
| Total Pages | : 11 |
| Release | : 1970 |
| Genre | : |
| ISBN | : |
Consistency and asymptotic normality are established for the maximum likelihood estimates of parameters in the two-phase regression model. (Author).
| Author | : Hyunju Son |
| Publisher | : |
| Total Pages | : 42 |
| Release | : 2020 |
| Genre | : |
| ISBN | : |
Two-phase regression models are a class of nonlinear regression models that are known for their flexibility and interpretability. An important feature of two-phase regression models is the existence of a threshold at which the relationship between an outcome and a covariate of interest changes. A standard estimation method, such as that used for generalized linear models, cannot be applied to two-phase regression models since the likelihood function is not differentiable with respect to the threshold parameter. We resolve this difficulty by using a grid search method which reduces the problem to a set of well-behaved likelihood functions for given candidate threshold values. Previously, a fast grid search algorithm that dramatically improved computational efficiency over a brute-force grid search was developed for two-phase regression models with linear trends. Here we generalize this algorithm to higher-order two-phase regression models where two separate polynomial regressions, not limited to linear, are used to model each phase (i.e., before and after the threshold). Based on the proposed fast grid search algorithm, we perform Monte Carlo simulations to examine the behavior of the parameter estimates. A real data example is also presented to illustrate the practical use of two-phase regression models.
| Author | : Hengqing Tong |
| Publisher | : John Wiley & Sons |
| Total Pages | : 489 |
| Release | : 2011-12-12 |
| Genre | : Business & Economics |
| ISBN | : 0470681772 |
Statistical Theories and Methods with Applications to Economics and Business highlights recent advances in statistical theory and methods that benefit econometric practice. It deals with exploratory data analysis, a prerequisite to statistical modelling and part of data mining. It provides recently developed computational tools useful for data mining, analysing the reasons to do data mining and the best techniques to use in a given situation. Provides a detailed description of computer algorithms. Provides recently developed computational tools useful for data mining Highlights recent advances in statistical theory and methods that benefit econometric practice. Features examples with real life data. Accompanying software featuring DASC (Data Analysis and Statistical Computing). Essential reading for practitioners in any area of econometrics; business analysts involved in economics and management; and Graduate students and researchers in economics and statistics.
| Author | : P. K. Bhattacharya |
| Publisher | : |
| Total Pages | : 20 |
| Release | : 1988 |
| Genre | : |
| ISBN | : |
| Author | : Raymond L. Chambers |
| Publisher | : CRC Press |
| Total Pages | : 374 |
| Release | : 2012-05-02 |
| Genre | : Mathematics |
| ISBN | : 1420011359 |
Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using well-established and widely used likelihood methods. The methods used to select samples often result in the sample differing in important ways from the target population and standard application of likelihood methods can lead to
| Author | : Raymond L. Chambers |
| Publisher | : CRC Press |
| Total Pages | : 393 |
| Release | : 2012-05-02 |
| Genre | : Mathematics |
| ISBN | : 1584886323 |
Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using well-established and widely used likelihood methods. The methods used to select samples often result in the sample differing in important ways from the target population and standard application of likelihood methods can lead to biased and inefficient estimates. Maximum Likelihood Estimation for Sample Surveys presents an overview of likelihood methods for the analysis of sample survey data that account for the selection methods used, and includes all necessary background material on likelihood inference. It covers a range of data types, including multilevel data, and is illustrated by many worked examples using tractable and widely used models. It also discusses more advanced topics, such as combining data, non-response, and informative sampling. The book presents and develops a likelihood approach for fitting models to sample survey data. It explores and explains how the approach works in tractable though widely used models for which we can make considerable analytic progress. For less tractable models numerical methods are ultimately needed to compute the score and information functions and to compute the maximum likelihood estimates of the model parameters. For these models, the book shows what has to be done conceptually to develop analyses to the point that numerical methods can be applied. Designed for statisticians who are interested in the general theory of statistics, Maximum Likelihood Estimation for Sample Surveys is also aimed at statisticians focused on fitting models to sample survey data, as well as researchers who study relationships among variables and whose sources of data include surveys.
| Author | : Dick J. Brus |
| Publisher | : CRC Press |
| Total Pages | : 549 |
| Release | : 2022-09-26 |
| Genre | : Mathematics |
| ISBN | : 1000600017 |
Scientific research often starts with data collection. However, many researchers pay insufficient attention to this first step in their research. The author, researcher at Wageningen University and Research, often had to conclude that the data collected by fellow researchers were suboptimal, or in some cases even unsuitable for their aim. One reason is that sampling is frequently overlooked in statistics courses. Another reason is the lack of practical textbooks on sampling. Numerous books have been published on the statistical analysis and modelling of data using R, but to date no book has been published in this series on how these data can best be collected. This book fills this gap. Spatial Sampling with R presents an overview of sampling designs for spatial sample survey and monitoring. It shows how to implement the sampling designs and how to estimate (sub)population- and space-time parameters in R. Key features Describes classical, basic sampling designs for spatial survey, as well as recently developed, advanced sampling designs and estimators Presents probability sampling designs for estimating parameters for a (sub)population, as well as non-probability sampling designs for mapping Gives comprehensive overview of model-assisted estimators Covers Bayesian approach to sampling design Illustrates sampling designs with surveys of soil organic carbon, above-ground biomass, air temperature, opium poppy Explains integration of wall-to-wall data sets (e.g. remote sensing images) and sample data Data and R code available on github Exercises added making the book suitable as a textbook for students The target group of this book are researchers and practitioners of sample surveys, as well as students in environmental, ecological, agricultural science or any other science in which knowledge about a population of interest is collected through spatial sampling. This book helps to implement proper sampling designs, tailored to their problems at hand, so that valuable data are collected that can be used to answer the research questions.
| Author | : Aleksandra P. Djukic-Vukovic |
| Publisher | : Frontiers Media SA |
| Total Pages | : 255 |
| Release | : 2022-07-27 |
| Genre | : Science |
| ISBN | : 2889766195 |
| Author | : Ronald Christensen |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 392 |
| Release | : 2013-11-11 |
| Genre | : Mathematics |
| ISBN | : 1475719515 |
This book was written to rigorously illustrate the practical application of the projective approach to linear models. To some, this may seem contradictory. I contend that it is possible to be both rigorous and illustrative and that it is possible to use the projective approach in practical applications. Therefore, unlike many other books on linear models, the use of projections and sub spaces does not stop after the general theory. They are used wherever I could figure out how to do it. Solving normal equations and using calculus (outside of maximum likelihood theory) are anathema to me. This is because I do not believe that they contribute to the understanding of linear models. I have similar feelings about the use of side conditions. Such topics are mentioned when appropriate and thenceforward avoided like the plague. On the other side of the coin, I just as strenuously reject teaching linear models with a coordinate free approach. Although Joe Eaton assures me that the issues in complicated problems frequently become clearer when considered free of coordinate systems, my experience is that too many people never make the jump from coordinate free theory back to practical applications. I think that coordinate free theory is better tackled after mastering linear models from some other approach. In particular, I think it would be very easy to pick up the coordinate free approach after learning the material in this book. See Eaton (1983) for an excellent exposition of the coordinate free approach.
