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Maximum Likelihood Estimation for Sample Surveys.
Sample surveys provide data used by researcher 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 standa...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
CRC Press,
2012.
|
| Colección: | Chapman & Hall/CRC Monographs on Statistics & Applied Probability.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Chambers, Raymond L. | |
| 245 | 1 | 0 | |a Maximum Likelihood Estimation for Sample Surveys. |
| 260 | |a Hoboken : |b CRC Press, |c 2012. | ||
| 300 | |a 1 online resource (374 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Chapman & Hall/CRC Monographs on Statistics & Applied Probability | |
| 505 | 0 | |6 880-01 |a Front Cover; Dedication; Contents; Preface; 1. Introduction; 2. Maximum likelihood theory for sample surveys; 3. Alternative likelihood-based methods for sample survey data; 4. Populations with independent units; 5. Regression models; 6. Clustered populations; 7. Informative nonresponse; 8. Maximum likelihood in other complicated situations; Notation. | |
| 520 | |a Sample surveys provide data used by researcher 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 mat. | ||
| 588 | 0 | |a Print version record. | |
| 504 | |a Includes bibliographical references and indexes. | ||
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Sampling (Statistics) | |
| 650 | 0 | |a Surveys |x Statistical methods. | |
| 650 | 6 | |a Échantillonnage (Statistique) | |
| 650 | 6 | |a Levés |x Méthodes statistiques. | |
| 650 | 7 | |a REFERENCE |x Research. |2 bisacsh | |
| 650 | 7 | |a Sampling (Statistics) |2 fast | |
| 650 | 7 | |a Surveys |x Statistical methods |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Chambers, Raymond L. |t Maximum Likelihood Estimation for Sample Surveys. |d Hoboken : CRC Press, ©2012 |z 9781584886327 |
| 830 | 0 | |a Chapman & Hall/CRC Monographs on Statistics & Applied Probability. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=912006 |z Texto completo |
| 880 | 0 | 0 | |6 505-01/(S |g Machine generated contents note: |g 1. |t Introduction -- |g 1.1. |t Nature and role of sample surveys -- |g 1.2. |t Sample designs -- |g 1.3. |t Survey data, estimation and analysis -- |g 1.4. |t Why analysts of survey data should be interested in maximum likelihood estimation -- |g 1.5. |t Why statisticians should be interested in the analysis of survey data -- |g 1.6. |t sample survey example -- |g 1.7. |t Maximum likelihood estimation for infinite populations -- |g 1.7.1. |t Data -- |g 1.7.2. |t Statistical models -- |g 1.7.3. |t Likelihood -- |g 1.7.4. |t Score and information functions -- |g 1.7.5. |t Maximum likelihood estimation -- |g 1.7.6. |t Hypothesis tests -- |g 1.7.7. |t Confidence intervals -- |g 1.7.8. |t Sufficient and ancillary statistics -- |g 1.8. |t Bibliographic notes -- |g 2. |t Maximum likelihood theory for sample surveys -- |g 2.1. |t Introduction -- |g 2.2. |t Maximum likelihood using survey data -- |g 2.2.1. |t Basic concepts -- |g 2.2.2. |t missing information principle -- |g 2.3. |t Illustrative examples with complete response -- |g 2.3.1. |t Estimation of a Gaussian mean: Noninformative selection -- |g 2.3.2. |t Estimation of an exponential mean: Cutoff sampling -- |g 2.3.3. |t Estimation of an exponential mean: Size-biased sampling -- |g 2.4. |t Dealing with nonresponse -- |g 2.4.1. |t score and information functions under nonresponse -- |g 2.4.2. |t Noninformative nonresponse -- |g 2.5. |t Illustrative examples with nonresponse -- |g 2.5.1. |t Estimation of a Gaussian mean under noninformative nonresponse: Noninformative selection -- |g 2.5.2. |t Estimation of a Gaussian mean under noninformative item nonresponse: Noninformative selection -- |g 2.5.3. |t Estimation of a Gaussian mean under informative unit nonresponse: Noninformative selection -- |g 2.5.4. |t Estimation of an exponential mean under informative nonresponse: Cutoff sampling -- |g 2.6. |t Bibliographic notes -- |g 3. |t Alternative likelihood-based methods for sample survey data -- |g 3.1. |t Introduction -- |g 3.1.1. |t Design-based analysis for population totals -- |g 3.2. |t Pseudo-likelihood -- |g 3.2.1. |t Maximum pseudo-likelihood estimation -- |g 3.2.2. |t Pseudo-likelihood for an exponential mean under size-biased sampling -- |g 3.2.3. |t Pseudo-Likelihood for an exponential mean under cutoff sampling -- |g 3.3. |t Sample likelihood -- |g 3.3.1. |t Maximum sample likelihood for an exponential mean under size-biased sampling -- |g 3.3.2. |t Maximum sample likelihood for an exponential mean under cutoff sampling -- |g 3.4. |t Analytic comparisons of maximum likelihood, pseudo-likelihood and sample likelihood estimation -- |g 3.5. |t role of sample inclusion probabilities in analytic analysis -- |g 3.6. |t Bayesian analysis -- |g 3.7. |t Bibliographic notes -- |g 4. |t Populations with independent units -- |g 4.1. |t Introduction -- |g 4.2. |t score and information functions for independent units -- |g 4.3. |t Bivariate Gaussian populations -- |g 4.4. |t Multivariate Gaussian populations -- |g 4.5. |t Non-Gaussian auxiliary variables -- |g 4.5.1. |t Modeling the conditional distribution of the survey variable -- |g 4.5.2. |t Modeling the marginal distribution of the auxiliary variable -- |g 4.5.3. |t Maximum likelihood analysis for μ and σ2 -- |g 4.5.4. |t Fitting the auxiliary variable distribution via method of moments -- |g 4.5.5. |t Semiparametric estimation -- |g 4.6. |t Stratified populations -- |g 4.7. |t Multinomial populations -- |g 4.8. |t Heterogeneous multinomial logistic populations -- |g 4.9. |t Bibliographic notes -- |g 5. |t Regression models -- |g 5.1. |t Introduction -- |g 5.2. |t Gaussian example -- |g 5.3. |t Parameterization in the Gaussian model -- |g 5.4. |t Other methods of estimation -- |g 5.5. |t Non-Gaussian models -- |g 5.6. |t Different auxiliary variable distributions -- |g 5.6.1. |t folded Gaussian model for the auxiliary variable -- |g 5.6.2. |t Regression in stratified populations -- |g 5.7. |t Generalized linear models -- |g 5.7.1. |t Binary regression -- |g 5.7.2. |t Generalized linear regression -- |g 5.8. |t Semiparametric and nonparametric methods -- |g 5.9. |t Bibliographic notes -- |g 6. |t Clustered populations -- |g 6.1. |t Introduction -- |g 6.2. |t Gaussian group dependent model -- |g 6.2.1. |t Auxiliary information at the unit level -- |g 6.2.2. |t Auxiliary information at the cluster level -- |g 6.2.3. |t No auxiliary information -- |g 6.3. |t Gaussian group dependent regression model -- |g 6.4. |t Extending the Gaussian group dependent regression model -- |g 6.5. |t Binary group dependent models -- |g 6.6. |t Grouping models -- |g 6.7. |t Bibliographic notes -- |g 7. |t Informative nonresponse -- |g 7.1. |t Introduction -- |g 7.2. |t Nonresponse in innovation surveys -- |g 7.2.1. |t mixture approach -- |g 7.2.2. |t mixture approach with an additional variable -- |g 7.2.3. |t mixture approach with a follow up survey -- |g 7.2.4. |t selection approach -- |g 7.3. |t Regression with item nonresponse -- |g 7.3.1. |t Item nonresponse in y -- |g 7.3.2. |t Item nonresponse in x -- |g 7.3.3. |t Selection models for item nonresponse in y -- |g 7.4. |t Regression with arbitrary nonresponse -- |g 7.4.1. |t Calculations for s01 -- |g 7.4.2. |t Calculations for s10 -- |g 7.4.3. |t Calculations for s00 -- |g 7.5. |t Imputation versus estimation -- |g 7.6. |t Bibliographic notes -- |g 8. |t Maximum likelihood in other complicated situations -- |g 8.1. |t Introduction -- |g 8.2. |t Likelihood analysis under informative selection -- |g 8.2.1. |t When is selection informative-- |g 8.2.2. |t Maximum likelihood under informative Hartley-Rao sampling -- |g 8.2.3. |t Maximum sample likelihood under informative Hartley-Rao sampling -- |g 8.2.4. |t extension to the case with auxiliary variables -- |g 8.2.5. |t Informative stratification -- |g 8.3. |t Secondary analysis of sample survey data -- |g 8.3.1. |t Data structure in secondary analysis -- |g 8.3.2. |t Approximate maximum likelihood with partial information -- |g 8.4. |t Combining summary population information with likelihood analysis -- |g 8.4.1. |t Summary population information -- |g 8.4.2. |t Linear regression with summary population information -- |g 8.4.3. |t Logistic regression with summary population information -- |g 8.4.4. |t Smearing and saddlepoint approximations under case-control sampling -- |g 8.4.5. |t Variance estimation -- |g 8.4.6. |t derivation of the saddlepoint approximation in Subsection 8.4.3 -- |g 8.5. |t Likelihood analysis with probabilistically linked data -- |g 8.5.1. |t model for probabilistic linkage -- |g 8.5.2. |t Linear regression with population-linked data -- |g 8.5.3. |t Linear regression with sample-linked data -- |g 8.6. |t Bibliographic notes. |
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