N. Bolger and J.-P. Laurenceau, Intensive Longitudinal Methods: An
Introduction to Diary and Experience Sampling Research. New York,
NY: Guilford Press, 2013.
 T. H. Walls and J. L. Schafer, Models for intensive longitudinal data.
Oxford: University Press, 2006.
 S. Shiffman, A. A. Stone, and M. R. Hufford, “Ecological momentary
assessment.” Annual Review of Clinical Psychology, vol. 4, pp. 1–32,
 A. Stone, S. Shiffman, A. Atienza, and L. Nebeling, The Science of
Real-Time Data Capture: Self-Reports in Health Research. NY: Oxford
University Press, 2008.
 L. Ou, M. D. Hunter, and S.-M. Chow, “What’s for dynr: A package
for linear and nonlinear dynamic modeling in R,” The R Journal, 2019,
 D. B. Rubin, “Inference and missing data,” Biometrika, vol. 63, no. 3,
pp. 581–592, 1976.
 L. Ji, S.-M. Chow, A. C. Schermerhorn, N. C. Jacobson, and E. M.
Cummings, “Handling missing data in the modeling of intensive
longitudinal data,” Structural Equation Modeling: A Multidisciplinary
Journal, pp. 1–22, 2018.
 D. B. Rubin, Multiple imputation for nonresponse in surveys. John
Wiley & Sons, 2004, vol. 81.
 S. van Buuren and C. Oudshoorn, “Multivariate imputation by chained
equations,” MICE V1. 0 user’s manual. Leiden: TNO Preventie en
 S. van Buuren and K. Groothuis-Oudshoorn, “mice: Multivariate
imputation by chained equations in R,” Journal of Statistical
Software, vol. 45, no. 3, pp. 1–67, 2011.
 T. E. Raghunathan, J. M. Lepkowski, J. Van Hoewyk, P. Solenberger
et al., “A multivariate technique for multiply imputing missing values
using a sequence of regression models,” Survey methodology, vol. 27,
no. 1, pp. 85–96, 2001.
 T. W. Anderson, “Maximum likelihood estimates for a multivariate
normal distribution when some observations are missing,” Journal of
the American Statistical Association, vol. 52, pp. 200–203, June 1957.
[Online]. Available: http://www.jstor.org/stable/2280845
 J. A. Russell, “Core affect and the psychological construction of
emotion,” Psychological Review, vol. 110, pp. 145–172, 2003.
 P. Kuppens, Z. Oravecz, and F. Tuerlinckx, “Feelings change:
Accounting for individual differences in the temporal dynamics of
affect,” Journal of Personality and Social Psychology, vol. 99, pp.
 U. Ebner-Priemer, M. Houben, P. Santangelo, N. Kleindienst,
F. Tuerlinckx, Z. Oravecz, G. Verleysen, K. V. Deun, M. Bohus, and
P. Kuppens, “Unraveling affective dysregulation in borderline personality
disorder: a theoretical model and empirical evidence.” Journal of
abnormal psychology, vol. 124 1, pp. 186–98, 2015.
 J. A. Russell and L. F. Barrett, “Core affect, prototypical emotional
episodes, and other things called emotion: dissecting the elephant,”
Journal of Personality and Social Psychology, vol. 76, pp. 805–819,
 R. W. Picard, S. Fedor, and Y. Ayzenberg, “Multiple Arousal
Theory and Daily-Life Electrodermal Activity Asymmetry,”
Emotion Review, pp. 1–14, Mar. 2015.
 N. L. Sin, R. P. Sloan, P. S. McKinley, and D. M. Almeida, “Linking
daily stress processes and laboratory-based heart rate variability in a
national sample of midlife and older adults,” Psychosomatic medicine,
vol. 78(5), pp. 573–582, 2016.
 T. Bossmann, M. K. Kanning, S. Koudela-Hamila, S. Hey, and
U. Ebner-Priemer, “The association between short periods of everyday
life activities and affective states: A replication study using ambulatory
assessment,” Frontiers in Psychology, vol. 4, 2013.
 G. F. Dunton, J. Huh, A. M. Leventhal, N. R. Riggs, D. Hedeker,
D. Spruijt-Metz, and M. A. Pentz, “Momentary assessment of affect,
physical feeling states, and physical activity in children.” Health
psychology : official journal of the Division of Health Psychology,
American Psychological Association, vol. 33 3, pp. 255–63, 2014.
 M. K. Kanning and D. Schoebi, “Momentary affective states are
associated with momentary volume, prospective trends, and fluctuation
of daily physical activity,” Frontiers in Psychology, vol. 7, 2016.
 C. Y. N. Niermann, C. Herrmann, B. von Haaren, D. H. H. V. Kann,
and A. Woll, “Affect and subsequent physical activity: An ambulatory
assessment study examining the affect-activity association in a real-life
context,” Frontiers in Psychology, vol. 7, 2016.
 Y. Liao, C.-P. Chou, J. Huh, A. M. Leventhal, and G. F. Dunton,
“Examining acute bi-directional relationships between affect, physical
feeling states, and physical activity in free-living situations using
electronic ecological momentary assessment,” Journal of Behavioral
Medicine, vol. 40, pp. 445–457, 2016.
 S.-M. Chow, M.-H. R. Ho, E. J. Hamaker, and C. V. Dolan,
“Equivalences and differences between structural equation and
state-space modeling frameworks,” Structural Equation Modeling,
vol. 17, pp. 303–332, 2010.
 J. Durbin and S. J. Koopman, Time Series Analysis by State Space
Methods. Oxford, United Kingdom: Oxford University Press, 2001.
 S.-M. Chow, L. Ou, A. Ciptadi, E. Prince, M. D. Hunter, D. You, J. M.
Rehg, A. Rozga, and D. S. Messinger, “Representing sudden shifts in
intensive dyadic interaction data using differential equation models with
regime switching,” Psychometrika, vol. 83, no. 2, pp. 476–510, 2018.
 R. E. Kalman, “A new approach to linear filtering and prediction
problems,” Journal of Basic Engineering, vol. 82, no. 1, pp. 35–45,
 P. De Jong, “The likelihood for a state space model,” Biometrika, vol. 75,
no. 1, pp. 165–169, March 1988.
 A. C. Harvey, Forecasting, Structural Time Series Models and the
Kalman Filter. Cambridge, United Kingdom: Cambridge University
 J. D. Hamilton, Time Series Analysis. Princeton, NJ: Princeton
University Press, 1994.
 S.-M. Chow and G. Zhang, “Nonlinear regime-switching state-space
(RSSS) models,” Psychometrika, vol. 78, no. 4, pp. 740–768, 2013.
 H. Akaike, “Information theory and an extension of the maximum
likelihood principle,” in Second International Symposium on Information
Theory, B. N. Petrov and F. Csaki, Eds. Budapest: Akademiai Kiado,
1973, pp. 267–281.
 G. Schwarz, “Estimating the dimension of a model,” The Annals of
Statistics, vol. 6, no. 2, pp. 461–464, 1978.
 F. Thoemmes and N. Rose, “A cautious note on auxiliary variables that
can increase bias in missing data problems,” Multivariate Behavioral
Research, vol. 49, no. 5, pp. 443–459, 2014.
 L. M. Collins, J. L. Schafer, and C.-M. Kam, “A comparison of
inclusive and restrictive strategies in modern missing data procedures.”
Psychological methods, vol. 6, no. 4, p. 330, 2001.
 A. Gelman and D. B. Rubin, “Inference from iterative simulation using
multiple sequences,” Statistical science, vol. 7, no. 4, pp. 457–472, 1992.
 S. Brooks and A. Gelman, “Some issues for monitoring convergence
of iterative simulations,” Computing Science and Statistics, pp. 30–36,
 R. W. Picard, “Recognizing Stress, Engagement, and Positive Emotion,”
in Proceedings of the 20th International Conference on Intelligent User
Interfaces, ser. IUI ’15. New York, NY, USA: ACM, 2015, pp. 3–4.
[Online]. Available: http://doi.acm.org/10.1145/2678025.2700999
 P. Kuppens, N. B. Allen, and L. B. Sheeber, “Emotional inertia and
psychological maladjustment,” Psychological Science, 2010.
 P. Royston, “Multiple imputation of missing values,” The Stata Journal,
vol. 4, no. 3, pp. 227–241, 2004.
 K. Lu, “Number of imputations needed to stabilize estimated treatment
difference in longitudinal data analysis,” Statistical methods in medical
research, vol. 26, no. 2, pp. 674–690, 2017.
 R. J. Little and D. B. Rubin, Statistical analysis with missing data.
Wiley, 2019, vol. 793.