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Specifying and Estimating Multi-Level Models for Geographical Research
Abstract
It is argued that multi-level models based on shrinkage estimators represent a considerable improvement over single-level models estimated by ordinary-least squares. In substantive terms, the ML models allow relationships to vary in time and space according to context. Shrinkage estimators make very efficient use of the information contained in the hierprchical data sets that are estimated by ML models. A number of ML models for house-price variation are specified in terms of fixed and random, allowed-to-vary, effects. Empirical illustrations of some of these ML models are given for house-price variation in Southampton.
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Cited by
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Bell, A., & Jones, K. (2015) · Political Science Research and Methods · doi:10.1017/psrm.2014.7 · Cited by 1,515
Self-citationContext, composition and heterogeneity: using multilevel models in health research
Duncan, C., Jones, K., & Moon, G. (1998) · Social Science & Medicine · doi:10.1016/s0277-9536(97)00148-2 · Cited by 689
Self-citationTrends in quantitative methods I: stressing the local
Fotheringham, A. S. (1997) · Progress in Human Geography · doi:10.1191/030913297676693207 · Cited by 276
Modelling spatial patterns and temporal trends of wildfires in Galicia (NW Spain)
Barreal, J., & Loureiro, M. L. (2015) · Forest Systems · doi:10.5424/fs/2015242-05713 · Cited by 41
Metadata
- Authors
- Jones, K.
- Venue
- Transactions of the Institute of British Geographers
- Publisher
- JSTOR
- Published
- 1991
- Volume
- 16
- Issue
- 2
- Pages
- 148
- ISSN
- 0020-2754 (ISSN-L)
- OpenAlex
- W2058801639
- Cited by
- 173
- References
- 0
- FWCI
- 14.75
- Type
- journal article
- Language
- en
- Open access
- closed
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- Jones, K. (1991). Specifying and Estimating Multi-Level Models for Geographical Research. Transactions of the Institute of British Geographers. https://doi.org/10.2307/622610
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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