Aassve, A., Billari, F. C., & Piccarreta, R. (2007). Strings of adulthood: A sequence analysis of young British women’s work-family trajectories. European Journal of Population/Revue Européenne de Démographie,
23(3–4), 369–388.
CrossRef
Google Scholar
Bartolucci, F., Pennoni, F., & Francis, B. (2007). A latent Markov model for detecting patterns of criminal activity. Journal of the Royal Statistical Society: Series A (Statistics in Society),
170(1), 115–132.
CrossRef
Google Scholar
Bassi, F. (2014). Dynamic segmentation of financial markets: A mixture latent class markov approach. In M. Carpita, E. Brentari, & E. M. Qannari (Eds.), Advances in latent variables (pp. 61–72). Berlin/Heidelberg: Springer.
Google Scholar
Baum, L. E., & Petrie, T. (1966). Statistical inference for probabilistic functions of finite state Markov chains. The Annals of Mathematical Statistics,
67(6), 1554–1563.
CrossRef
Google Scholar
Blossfeld, H.-P., Roßbach, H.-G., & von Maurice, J. (Eds.) (2011). Education as a lifelong process-the German national educational panel study (NEPS) (Vol. 14) [Special Issue] of Zeitschrift für Erziehungswissenschaft. Wiesbaden: Springer.
Google Scholar
Breen, R., & Moisio, P. (2004). Poverty dynamics corrected for measurement error. The Journal of Economic Inequality,
2(3), 171–191.
CrossRef
Google Scholar
Collins, L. M., & Wugalter, S. E. (1992). Latent class models for stage-sequential dynamic latent variables. Multivariate Behavioral Research,
27(1), 131–157.
CrossRef
Google Scholar
Durbin, R., Eddy, S., Krogh, A., & Mitchison, G. (1998). Biological sequence analysis: Probabilistic models of proteins and nucleic acids. Cambridge: Cambridge University Press.
CrossRef
Google Scholar
Eerola, M., & Helske, S. (2016). Statistical analysis of life history calendar data. Statistical Methods in Medical Research,
25(2), 571–597.
CrossRef
Google Scholar
Gabadinho, A., Ritschard, G., Müller, N. S., & Studer, M. (2011). Analyzing and visualizing state sequences in R with TraMineR. Journal of Statistical Software,
40(4), 1–37.
CrossRef
Google Scholar
Gauthier, J.-A., Widmer, E. D., Bucher, P., & Notredame, C. (2010). Multichannel sequence analysis applied to social science data. Sociological Methodology,
40(1), 1–38.
CrossRef
Google Scholar
Helske, S., & Helske, J. (2018, forthcoming). Mixture hidden Markov models for sequence data: The seqHMM package in R. Journal of Statistical Software.
Google Scholar
Helske, S., Steele, F., Kokko, K., Räikkönen, E., & Eerola, M. (2015). Partnership formation and dissolution over the life course: Applying sequence analysis and event history analysis in the study of recurrent events. Longitudinal and Life Course Studies,
6(1), 1–25.
CrossRef
Google Scholar
Ip, E. H., Saldana, S., Arcury, T. A., Grzywacz, J. G., Trejo, G., & Quandt, S. A. (2015). Profiles of food security for US farmworker households and factors related to dynamic of change. American Journal of Public Health,
105(10), e42–e47.
CrossRef
Google Scholar
Lopez, A. (2008). Markov models for longitudinal course of youth bipolar disorder. Ph.D. thesis, University of Pittsburgh, Ann Arbor, MI.
Google Scholar
MacDonald, I. L., & Zucchini, W. (1997). Hidden Markov and other models for discrete-valued time series (Vol. 110). Boca Raton: CRC Press.
Google Scholar
Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., & Hornik, K. (2015). Cluster: Cluster analysis basics and extensions. R package version 2.0.3.
Google Scholar
McDonough, P., Worts, D., & Sacker, A. (2010). Socioeconomic inequalities in health dynamics: A comparison of Britain and the United States. Social Science & Medicine,
70(2), 251–260.
CrossRef
Google Scholar
Müller, N. S., Sapin, M., Gauthier, J.-A., Orita, A., & Widmer, E. D. (2012). Pluralized life courses? An exploration of the life trajectories of individuals with psychiatric disorders. International Journal of Social Psychiatry,
58(3), 266–277.
CrossRef
Google Scholar
Pavlopoulos, D., & Vermunt, J. K. (2015). Measuring temporary employment: Do survey or register data tell the truth? Statistics Canada, Catalogue No. 12–001-X,
41(1), 197–214.
Google Scholar
Poulsen, C. S. (1990). Mixed Markov and latent Markov modelling applied to brand choice behaviour. International Journal of Research in Marketing,
7(1), 5–19.
CrossRef
Google Scholar
R Core Team. (2015). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
Google Scholar
Rabiner, L. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE,
77(2), 257–286.
CrossRef
Google Scholar
Rijmen, F., Vansteelandt, K., & De Boeck, P. (2008). Latent class models for diary method data: Parameter estimation by local computations. Psychometrika,
73(2), 167–182.
CrossRef
Google Scholar
Spallek, M., Haynes, M., & Jones, A. (2014). Holistic housing pathways for Australian families through the childbearing years. Longitudinal and Life Course Studies,
5(2), 205–226.
CrossRef
Google Scholar
Studer, M., & Ritschard, G. (2016). What matters in differences between life trajectories: A comparative review of sequence dissimilarity measures. Journal of the Royal Statistical Society: Series A (Statistics in Society),
179(2), 481–511.
CrossRef
Google Scholar
Taushanov, Z., & Berchtold, A. (2018). Markovian-based clustering of internet addiction trajectories. In G. Ritschard & M. Studer (Eds.), Sequence analysis and related approaches: Innovative methods and applications. Cham: Springer (this volume).
Google Scholar
Van de Pol, F., & De Leeuw, J. (1986). A latent Markov model to correct for measurement error. Sociological Methods & Research,
15(1–2), 118–141.
Google Scholar
Van de Pol, F., & Langeheine, R. (1990). Mixed Markov latent class models. Sociological Methodology,
20, 213–247.
CrossRef
Google Scholar
Vermunt, J. K., Langeheine, R., & Bockenholt, U. (1999). Discrete-time discrete-state latent Markov models with time-constant and time-varying covariates. Journal of Educational and Behavioral Statistics,
24(2), 179–207.
CrossRef
Google Scholar
Vermunt, J. K., Tran, B., & Magidson, J. (2008). Latent class models in longitudinal research. In S. Menard (Ed.), Handbook of longitudinal research: Design, measurement, and analysis (pp. 373–385). Burlington: Elsevier.
Google Scholar
Viterbi, A. J. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory,
13(2), 260–269.
CrossRef
Google Scholar
Wiggins, L. M. (1955). Mathematical models for the interpretation of attitude and behavior change: The analysis of multi-wave panel. Ph.D. thesis, Columbia University, New York.
Google Scholar
Wiggins, L. M. (1973). Panel analysis: Latent probability models for attitude and behavior processes. Oxford: Jossey-Bass.
Google Scholar
Zucchini, W., & MacDonald, I. L. (2009). Hidden Markov models for time series: An introduction using R (Vol. 110). Boca Raton: CRC Press.
Google Scholar