A Unifying Framework for Markov Modeling in Discrete Space and Discrete Time

Langeheine, Rolf; Pol, Frank van de

doi:10.1177/0049124190018004002

Cited by 99 publications

(63 citation statements)

References 17 publications

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“…To our knowledge only Rendtel et al (1998) have used a latent structure analysis to separate true poverty mobility from measurement error. Using a latent Markov model, originally introduced for this purpose by Langeheine and van de Pol (1990), they arrived at the striking finding that almost half of the observed poverty mobility in their German Socio-Economic Panel data might be due to measurement error. Their study dealt only with Germany, but we can expect that, if we do not correct the measurement error in poverty transition tables, poverty mobility will be over-estimated in other countries as well.…”

Section: Previous Studies Of Poverty Dynamics and The Problem Of Measmentioning

confidence: 99%

“…In labelling these we follow the terminology of Langeheine and van de Pol (1990). The results are shown in Table 5, together with the observed proportion of stable cases (OBS) and of change (OBC).…”

Section: Error Corrected Estimates Of Poverty Dynamicsmentioning

confidence: 99%

“…Qualitative difference is also (implicitly) emphasised in most definitions of poverty, where relative differences in wealth and other resources are assumed to cause an absolute difference in the capability to function or attain some minimum acceptable way of living in the society (Atkinson et al 2002, 57;Sen 1983;Townsend 1979, 31). For correcting error in head-count poverty measures we draw specifically on latent class models (Van de Pol and Leeuw 1986;Langeheine and Van de Pol 1990). One advantage of such models is that they yield separate estimates of the reliability of measurement of both the poor and non-poor.…”

Section: Introductionmentioning

confidence: 99%

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Poverty dynamics corrected for measurement error

Breen¹,

Moisio

2004

J Econ Inequal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. • ESRC Research Centre on Micro-social Change. Established in 1989 to identify, explain, model and forecast social change in Britain at the individual and household level, the Centre specialises in research using longitudinal data. Terms of use: Documents in• ESRC UK Longitudinal Centre. This national resource centre was established in October 1999 to promote the use of longitudinal data and to develop a strategy for the future of large-scale longitudinal surveys. It was responsible for the British Household Panel Survey (BHPS) and for the ESRC's interest in the National Child Development Study and the 1970 British Cohort Study• European Centre for Analysis in the Social Sciences. ECASS is an interdisciplinary research centre which hosts major research programmes and helps researchers from the EU gain access to longitudinal data and cross-national datasets from all over Europe.The British Household Panel Survey is one of the main instruments for measuring social change in Britain. The BHPS comprises a nationally representative sample of around 5,500 households and over 10,000 individuals who are reinterviewed each year. The questionnaire includes a constant core of items accompanied by a variable component in order to provide for the collection of initial conditions data and to allow for the subsequent inclusion of emerging research and policy concerns.Among the main projects in ISER's research programme are: the labour market and the division of domestic responsibilities; changes in families and households; modelling households' labour force behaviour; wealth, well-being and socio-economic structure; resource distribution in the household; and modelling techniques and survey methodology.BHPS data provide the academic community, policymakers and private sector with a unique national resource and allow for comparative research with similar studies in Europe, the United States and Canada.BHPS data are available from the Data Archive at the University of Essex http://www.data-archive.ac.ukFurther information about the BHPS and other longitudinal surveys can be obtained by telephoning +44 (0) 1206 873543. The support of both the Economic and Social Research Council (ESRC) and the University of Essex is gratefully acknowledged. The work reported in this paper is part of the scientific programme of the Institute for Social and Economic Research. Acknowledgement:Comments welcome but please do not quote without authors' permission. 4 ABSTRACTWe use latent class ...

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Section: Previous Studies Of Poverty Dynamics and The Problem Of Measmentioning

confidence: 99%

Section: Error Corrected Estimates Of Poverty Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Poverty dynamics corrected for measurement error

Breen¹,

Moisio

2004

J Econ Inequal

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“…Several extensions of standard MCMs with greater flexibility overcome some of these drawbacks, e.g., hidden Markov models (Rabiner 1989), mixture Markov models and latent mixed Markov models (Langeheine and Van de Pol 1990) Ron, Singer, and Tishby 1996;Bühlmann and Wyner 1999), also known as variable-order Markov chains (VOMCs), are a class of models that exhibit many interesting characteristics. They are especially effective in capturing particular high-order dependencies in sequences while remaining parsimonious and simple to estimate.…”

Section: The Markov Hypothesismentioning

confidence: 99%

“…This issue has been addressed for fixed-length Markov chains in the social sciences literature (Spilerman 1972;Singer and Spilerman 1973;Langeheine and Van de Pol 1990). For VLMCs, we propose two approaches that are explained in this section.…”

Section: Segmented Models and Comparison Of Pstsmentioning

confidence: 99%

Analyzing State Sequences with Probabilistic Suffix Trees: ThePSTRPackage

Gabadinho¹,

Ritschard²

2016

J. Stat. Soft.

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This article presents the PST R package for categorical sequence analysis with probabilistic suffix trees (PSTs), i.e., structures that store variable-length Markov chains (VLMCs). VLMCs allow to model high-order dependencies in categorical sequences with parsimonious models based on simple estimation procedures. The package is specifically adapted to the field of social sciences, as it allows for VLMC models to be learned from sets of individual sequences possibly containing missing values; in addition, the package is extended to account for case weights. This article describes how a VLMC model is learned from one or more categorical sequences and stored in a PST. The PST can then be used for sequence prediction, i.e., to assign a probability to whole observed or artificial sequences. This feature supports data mining applications such as the extraction of typical patterns and outliers. This article also introduces original visualization tools for both the model and the outcomes of sequence prediction. Other features such as functions for pattern mining and artificial sequence generation are described as well. The PST package also allows for the computation of probabilistic divergence between two models and the fitting of segmented VLMCs, where sub-models fitted to distinct strata of the learning sample are stored in a single PST.

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Latent Transition Models

Pol

Langeheine²

2005

Encyclopedia of Statistics in Behavioral Science

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Flows between states for discrete periods of time can be described with a Markov chain, a process without memory. More realistic for social science data is a heterogeneous mixture of Markov chains, of movers and stayers. Alternatively, or in conjunction with heterogeneity in the tendency to change, one can take measurement unreliability into account, using latent or indirectly measured Markov models. The basic model variants are explained with data examples. References to overview papers are given.

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A Unifying Framework for Markov Modeling in Discrete Space and Discrete Time

Cited by 99 publications

References 17 publications

Poverty dynamics corrected for measurement error

Poverty dynamics corrected for measurement error

Analyzing State Sequences with Probabilistic Suffix Trees: ThePSTRPackage

Latent Transition Models

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