Mô hình Dữ liệu Dọc của Các Đặc Trưng Tiềm Ẩn Phụ Thuộc Tuổi với Các Mô Hình Tiềm Ẩn Phối Hợp Tổng Quát

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. Alfaro-Almagro, F., McCarthy, P., Afyouni, S., Andersson, J. L. R., Bastiani, M., Miller, K. L., Nichols, T. E., & Smith, S. M. (2021). Confound modelling in UK Biobank brain imaging. NeuroImage, 224, 117002. Amestoy, P. R., Davis, T. A., & Duff, I. S. (1996). An approximate minimum degree ordering algorithm. SIAM Journal on Matrix Analysis and Applications, 17(4), 886–905. Andersson, B., & Xin, T. (2021). Estimation of latent regression item response theory models using a second-order Laplace approximation. Journal of Educational and Behavioral Statistics, 46(2), 244–265. Arminger, G., & Muthén, B. O. (1998). A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the Metropolis-Hastings algorithm. Psychometrika, 63(3), 271–300. Arnold, J. B. (2021). ggthemes: Extra themes, scales and geoms for ‘ggplot2’ . Baltes, P. B. (1968). Longitudinal and cross-sectional sequences in the study of age and generation effects. Human Development, 11(3), 145–171. Bates, D. (2022). Computational methods for mixed models. R package vignette, Department of Statistics, University of Wisconsin - Madison. Bates, D., & Eddelbuettel, D. (2013). Fast and elegant numerical linear algebra using the RcppEigen package. Journal of Statistical Software, 52, 1–24. Bates, D. M., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. Bauer, D. J. (2005). A semiparametric approach to modeling nonlinear relations among latent variables. Structural Equation Modeling: A Multidisciplinary Journal, 12(4), 513–535. Baydin, A. G., Pearlmutter, B. A., Radul, A. A., & Siskind, J. M. (2018). Automatic differentiation in machine learning: A survey. Journal of Machine Learning Research, 18(153), 1–43. Bethlehem, R. A. I., Seidlitz, J., White, S. R., Vogel, J. W., Anderson, K. M., Adamson, C., Adler, S., Alexopoulos, G. S., Anagnostou, E., Areces-Gonzalez, A., Astle, D. E., Auyeung, B., Ayub, M., Bae, J., Ball, G., Baron-Cohen, S., Beare, R., Bedford, S. A., Benegal, V., Alexander-Bloch, A. F. (2022). Brain charts for the human lifespan. Nature, 604(7906), 525–533. Blackburn, H. L., & Benton, A. L. (1959). Revised administration and scoring of the Digit Span Test. Journal of Consulting Psychology, 21(2), 139. Booth, J. (1995). Bootstrap methods for generalized linear mixed models with applications to small area estimation. In G. U. H. Seeber, B. J. Francis, R. Hatzinger, & G. Steckel-Berger (Eds.), Statistical modelling. Lecture notes in statistics (pp. 43–51). Springer. Brandmaier, A. M., Driver, C. C., & Voelkle, M. C. (2018). Recursive partitioning in continuous time analysis. In K. van Montfort, J. H. L. Oud, & M. C. Voelkle (Eds.), Continuous time modeling in the behavioral and related sciences (pp. 259–282). Springer. Brandmaier, A. M., Prindle, J. J., McArdle, J. J., & Lindenberger, U. (2016). Theory-guided exploration with structural equation model forests. Psychological Methods, 21, 566–582. Brockwell, S. E., & Gordon, I. R. (2001). A comparison of statistical methods for meta-analysis. Statistics in Medicine, 20(6), 825–840. Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielsen, A., Skaug, H. J., Mächler, M., & Bolker, B. M. (2017). glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R Journal, 9(2), 378–400. Byrd, R. H., Lu, P., Nocedal, J., & Zhu, C. (1995). A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing, 16(5), 1190–1208. Cagnone, S., & Monari, P. (2013). Latent variable models for ordinal data by using the adaptive quadrature approximation. Computational Statistics, 28(2), 597–619. Comets, E., Lavenu, A., & Lavielle, M. (2017). Parameter estimation in nonlinear mixed effect models using saemix, an R implementation of the SAEM algorithm. Journal of Statistical Software, 80(1), 1–41. Coull, B. A., Ruppert, D., & Wand, M. P. (2001). Simple incorporation of interactions into additive models. Biometrics, 57(2), 539–545. Curran, P. J. (2003). Have multilevel models been structural equation models all along? Multivariate Behavioral Research, 38(4), 529–569. Dahl, M. J., Bachman, S. L., Dutt, S., Düzel, S., Bodammer, N. C., Lindenberger, U., Kühn, S., Werkle-Bergner, M., & Mather, M. (2022). The integrity of dopaminergic and noradrenergic brain regions is associated with different aspects of late-life memory performance. Dahl, M. J., Mather, M., Düzel, S., Bodammer, N. C., Lindenberger, U., Kühn, S., & Werkle-Bergner, M. (2019). Rostral locus coeruleus integrity is associated with better memory performance in older adults. Nature Human Behaviour, 3(11), 1203–1214. Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis: I. Segmentation and surface reconstruction. NeuroImage, 9(2), 179–194. Davidson, D. J., Zacks, R. T., & Williams, C. C. (2003). Stroop interference, practice, and aging. Aging, Neuropsychology, and Cognition, 10(2), 85–98. Davis, T. A. (2006). Direct methods for sparse linear systems. Fundamentals of algorithms. Society for Industrial and Applied Mathematics. Delis, D. C., Kaplan, E., & Kramer, J. H. (2001). Delis-Kaplan executive function system. APA PsycTests. Delis, D. C., Kramer, J. H., Kaplan, E., & Ober, B. A. (1987). CVLT, California Verbal Learning Test. Psychological Corporation. Delis, D. C., Kramer, J. H., Kaplan, E., & Ober, B. A. (2000). CVLT, California Verbal Learning Test (2nd ed.). Psychological Corporation. Delyon, B., Lavielle, M., & Moulines, E. (1999). Convergence of a stochastic approximation version of the EM algorithm. The Annals of Statistics, 27(1), 94–128. Demidenko, E. (2013). Mixed models: Theory and applications with R. Wiley series in probability and statistics (2nd ed.). Wiley. Driver, C. C., Oud, J. H. L., & Voelkle, M. C. (2017). Continuous time structural equation modeling with R package ctsem. Journal of Statistical Software, 77, 1–35. Driver, C. C., & Voelkle, M. C. (2018). Hierarchical Bayesian continuous time dynamic modeling. Psychological Methods, 23(4), 774–799. Dubois, B., Hampel, H., Feldman, H. H., Scheltens, P., Aisen, P., Andrieu, S., Bakardjian, H., Benali, H., Bertram, L., Blennow, K., Broich, K., Cavedo, E., Crutch, S., Dartigues, J.-F., Duyckaerts, C., Epelbaum, S., Frisoni, G. B., Gauthier, S., Genthon, R., Gouw, A. A., Habert, M.-O., Holtzman, D. M., Kivipelto, M., Lista, S., Molinuevo, J.-L., O’Bryant, S. E., Rabinovici, G. D., Rowe, C., Salloway, S., Schneider, L. S., Sperling, R., Teichmann, M., Carrillo, M. C., Cummings, J., Jack Jr, C. R., & Proceedings of the Meeting of the International Working Group (IWG) and the American Alzheimer’s Association on “The Preclinical State of AD”; July 23, USA, . W. D. (2016). Preclinical Alzheimer’s disease: Definition, natural history, and diagnostic criteria. Alzheimer’s & Dementia, 12(3), 292–323. Duff, I. S., Erisman, A. M., & Reid, J. K. (2017). Direct methods for sparse matrices. Numerical mathematics and scientific computation (2nd ed.). Oxford University Press. Edwards, J. R., & Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological Methods, 5(2), 155–174. Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. Number 57 in monographs on statistics and applied probability. Chapman & Hall. Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121. Faes, C., Aerts, M., Molenberghs, G., Geys, H., Teuns, G., & Bijnens, L. (2008). A high-dimensional joint model for longitudinal outcomes of different nature. Statistics in Medicine, 27(22), 4408–4427. Fahrmeir, L., & Raach, A. (2007). A Bayesian semiparametric latent variable model for mixed responses. Psychometrika, 72(3), 327. Fieuws, S., & Verbeke, G. (2006). Pairwise fitting of mixed models for the joint modeling of multivariate longitudinal profiles. Biometrics, 62(2), 424–431. Fine, E. M., & Delis, D. C. (2011). Delis-Kaplan executive functioning system. In J. S. Kreutzer, J. DeLuca, & B. Caplan (Eds.), Encyclopedia of clinical neuropsychology (pp. 796–801). Springer. Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., & Dale, A. M. (2002). Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3), 341–355. Fjell, A. M., Idland, A.-V., Sala-Llonch, R., Watne, L. O., Borza, T., Brækhus, A., Lona, T., Zetterberg, H., Blennow, K., Wyller, T. B., & Walhovd, K. B. (2018). Neuroinflammation and tau interact with amyloid in predicting sleep problems in aging independently of atrophy. Cerebral Cortex, 28(8), 2775–2785. Fournier, D. A., Skaug, H. J., Ancheta, J., Ianelli, J., Magnusson, A., Maunder, M. N., Nielsen, A., & Sibert, J. (2012). AD model builder: Using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optimization Methods and Software, 27(2), 233–249. Fraley, C., & Burns, P. J. (1995). Large-scale estimation of variance and covariance components. SIAM Journal on Scientific Computing, 16(1), 192–209. Gajewski, P. D., Falkenstein, M., Thönes, S., & Wascher, E. (2020). Stroop task performance across the lifespan: High cognitive reserve in older age is associated with enhanced proactive and reactive interference control. NeuroImage, 207, 116430. Ganguli, B., Staudenmayer, J., & Wand, M. P. (2005). Additive models with predictors subject to measurement error. Australian & New Zealand Journal of Statistics, 47(2), 193–202. Golub, G. H., Heath, M., & Wahba, G. (1979). Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics, 21(2), 215–223. Grégoire, J., & Van Der Linden, M. (1997). Effect of age on forward and backward digit spans. Aging, Neuropsychology, and Cognition, 4(2), 140–149. Greven, S., & Kneib, T. (2010). On the behaviour of marginal and conditional AIC in linear mixed models. Biometrika, 97(4), 773–789. Guennebaud, G., Jacob, B., et al. (2010). Eigen v3. Hanson, J. L., Chandra, A., Wolfe, B. L., & Pollak, S. D. (2011). Association between income and the hippocampus. PLoS ONE, 6(5), e18712. Härdle, W., & Bowman, A. W. (1988). Bootstrapping in nonparametric regression: Local adaptive smoothing and confidence bands. Journal of the American Statistical Association, 83(401), 102–110. Härdle, W., Huet, S., Mammen, E., & Sperlich, S. (2004). Bootstrap inference in semiparametric generalized additive models. Econometric Theory, 20(2), 265–300. Härdle, W., & Marron, J. S. (1991). Bootstrap simultaneous error bars for nonparametric regression. The Annals of Statistics, 19(2), 778–796. Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310. Hilbert, S., Nakagawa, T. T., Puci, P., Zech, A., & Bühner, M. (2015). The digit span backwards task: Verbal and visual cognitive strategies in working memory assessment. European Journal of Psychological Assessment, 31, 174–180. Hintze, J. L., & Nelson, R. D. (1998). Violin plots: A box plot-density trace synergism. The American Statistician, 52(2), 181–184. Hyatt, C. S., Owens, M. M., Crowe, M. L., Carter, N. T., Lynam, D. R., & Miller, J. D. (2020). The quandary of covarying: A brief review and empirical examination of covariate use in structural neuroimaging studies on psychological variables. NeuroImage, 205, 116225. Iddi, S., & Molenberghs, G. (2012). A joint marginalized multilevel model for longitudinal outcomes. Journal of Applied Statistics, 39(11), 2413–2430. Ivanova, A., Molenberghs, G., & Verbeke, G. (2016). Mixed models approaches for joint modeling of different types of responses. Journal of Biopharmaceutical Statistics, 26(4), 601–618. Jeon, M., & Rabe-Hesketh, S. (2012). Profile-likelihood approach for estimating generalized linear mixed models with factor structures. Journal of Educational and Behavioral Statistics, 37(4), 518–542. Joe, H. (2008). Accuracy of Laplace approximation for discrete response mixed models. Computational Statistics & Data Analysis, 52(12), 5066–5074. Kelava, A., & Brandt, H. (2014). A general non-linear multilevel structural equation mixture model. Frontiers in Psychology, 5, 748. Kelava, A., Nagengast, B., & Brandt, H. (2014). A nonlinear structural equation mixture modeling approach for nonnormally distributed latent predictor variables. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 468–481. Kimeldorf, G. S., & Wahba, G. (1970). A correspondence between Bayesian estimation on stochastic processes and smoothing by splines. Annals of Mathematical Statistics, 41(2), 495–502. Köhncke, Y., Düzel, S., Sander, M. C., Lindenberger, U., Kühn, S., & Brandmaier, A. M. (2020). Hippocampal and parahippocampal gray matter structural integrity assessed by multimodal imaging is associated with episodic memory in old age. Cerebral Cortex, 31, 1464–1477. Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. M. (2016). TMB: Automatic differentiation and Laplace approximation. Journal of Statistical Software, 70, 1–21. Kuhn, E., & Lavielle, M. (2005). Maximum likelihood estimation in nonlinear mixed effects models. Computational Statistics & Data Analysis, 49(4), 1020–1038. Leal, A. M. M. (2018). Autodiff, a modern, fast and expressive C++ library for automatic differentiation. Lee, S.-Y., & Zhu, H.-T. (2000). Statistical analysis of nonlinear structural equation models with continuous and polytomous data. British Journal of Mathematical and Statistical Psychology, 53(2), 209–232. Lin, X., & Zhang, D. (1999). Inference in generalized additive mixed models by using smoothing splines. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(2), 381–400. Livingston, G., Sommerlad, A., Orgeta, V., Costafreda, S. G., Huntley, J., Ames, D., Ballard, C., Banerjee, S., Burns, A., Cohen-Mansfield, J., Cooper, C., Fox, N., Gitlin, L. N., Howard, R., Kales, H. C., Larson, E. B., Ritchie, K., Rockwood, K., Sampson, E. L., Mukadam, N. (2017). Dementia prevention, intervention, and care. The Lancet, 390(10113), 2673–2734. Margossian, C. C. (2019). A review of automatic differentiation and its efficient implementation. WIREs Data Mining and Knowledge Discovery, 9(4), e1305. Marra, G., & Wood, S. N. (2012). Coverage properties of confidence intervals for generalized additive model components. Scandinavian Journal of Statistics, 39(1), 53–74. McArdle, J. J., Ferrer-Caja, E., Hamagami, F., & Woodcock, R. W. (2002). Comparative longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the life span. Developmental Psychology, 38(1), 115–142. Mehta, P. D., & Neale, M. C. (2005). People are variables too: Multilevel structural equations modeling. Psychological Methods, 10(3), 259–284. Mehta, P. D., & West, S. G. (2000). Putting the individual back into individual growth curves. Psychological Methods, 5(1), 23–43. Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55(1), 107–122. Meyers, S. (2015). Effective modern C++ (1st ed.). O’Reilly. Muthén, B. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49(1), 115–132. Muthén, B. O. (2002). Beyond SEM: General latent variable modeling. Behaviormetrika, 29(1), 81–117. Nilsson, L.-G., Sternäng, O., Rönnlund, M., & Nyberg, L. (2009). Challenging the notion of an early-onset of cognitive decline. Neurobiology of Aging, 30(4), 521–524. Noble, K. G., Houston, S. M., Brito, N. H., Bartsch, H., Kan, E., Kuperman, J. M., Akshoomoff, N., Amaral, D. G., Bloss, C. S., Libiger, O., Schork, N. J., Murray, S. S., Casey, B. J., Chang, L., Ernst, T. M., Frazier, J. A., Gruen, J. R., Kennedy, D. N., Van Zijl, P., Sowell, E. R. (2015). Family income, parental education and brain structure in children and adolescents. Nature Neuroscience, 18(5), 773–778. Noble, K. G., Houston, S. M., Kan, E., & Sowell, E. R. (2012). Neural correlates of socioeconomic status in the developing human brain. Developmental Science, 15(4), 516–527. Nocedal, J., & Wright, S. J. (2006). Numerical optimization. Springer series in operations research (2nd ed.). Springer. Novick, M. R. (1966). The axioms and principal results of classical test theory. Journal of Mathematical Psychology, 3(1), 1–18. Nyberg, L., Magnussen, F., Lundquist, A., Baaré, W., Bartrés-Faz, D., Bertram, L., Boraxbekk, C. J., Brandmaier, A. M., Drevon, C. A., Ebmeier, K., Ghisletta, P., Henson, R. N., Junqué, C., Kievit, R., Kleemeyer, M., Knights, E., Kühn, S., Lindenberger, U., Penninx, B. W. J. H., Fjell, A. M. (2021). Educational attainment does not influence brain aging. Proceedings of the National Academy of Sciences, 118(18), e2101644118. Ogden, H. E. (2015). A sequential reduction method for inference in generalized linear mixed models. Electronic Journal of Statistics, 9(1), 135–152. Ostrosky-Solís, F., & Lozano, A. (2006). Digit Span: Effect of education and culture. International Journal of Psychology, 41(5), 333–341. Oud, J. H. L., & Jansen, R. A. R. G. (2000). Continuous time state space modeling of panel data by means of SEM. Psychometrika, 65(2), 199–215. Pawitan, Y. (2001). In all likelihood. Oxford University Press. Pedersen, E. J., Miller, D. L., Simpson, G. L., & Ross, N. (2019). Hierarchical generalized additive models in ecology: An introduction with mgcv. PeerJ, 7, e6876. Pedersen, T. L. (2020). patchwork: The composer of plots. Pinheiro, J., & Bates, D. M. (2000). Mixed-effects models in S and S-PLUS. Statistics and computing. Springer. Pinheiro, J. C., & Bates, D. M. (1995). Approximations to the log-likelihood function in the nonlinear mixed-effects model. Journal of Computational and Graphical Statistics, 4(1), 12–35. Pinheiro, J. C., & Chao, E. C. (2006). Efficient Laplacian and adaptive Gaussian quadrature algorithms for multilevel generalized linear mixed models. Journal of Computational and Graphical Statistics, 15(1), 58–81. Proust-Lima, C., Amieva, H., & Jacqmin-Gadda, H. (2013). Analysis of multivariate mixed longitudinal data: A flexible latent process approach. British Journal of Mathematical and Statistical Psychology, 66(3), 470–487. Proust-Lima, C., Philipps, V., & Liquet, B. (2017). Estimation of extended mixed models using latent classes and latent processes: The R package lcmm. Journal of Statistical Software, 78(1), 1–56. R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2002). Reliable estimation of generalized linear mixed models using adaptive quadrature. The Stata Journal, 2(1), 1–21. Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2004). Generalized multilevel structural equation modeling. Psychometrika, 69(2), 167–190. Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2005). Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects. Journal of Econometrics, 128(2), 301–323. Raudenbush, S. W., Yang, M.-L., & Yosef, M. (2000). Maximum likelihood for generalized linear models with nested random effects via high-order, multivariate Laplace approximation. Journal of Computational and Graphical Statistics, 9(1), 141–157. Raz, N., & Lindenberger, U. (2011). Only time will tell: Cross-sectional studies offer no solution to the age–brain–cognition triangle: Comment on Salthouse (2011). Psychological Bulletin, 137(5), 790–795. Reiss, P. T., & Ogden, R. T. (2009). Smoothing parameter selection for a class of semiparametric linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(2), 505–523. Reuter, M., Schmansky, N. J., Rosas, H. D., & Fischl, B. (2012). Within-subject template estimation for unbiased longitudinal image analysis. NeuroImage, 61(4), 1402–1418. Rockwood, N. J. (2020). Maximum likelihood estimation of multilevel structural equation models with random slopes for latent covariates. Psychometrika, 85(2), 275–300. Rockwood, N. J., & Jeon, M. (2019). Estimating complex measurement and growth models using the R package PLmixed. Multivariate Behavioral Research, 54(2), 288–306. Rönnlund, M., Nyberg, L., Bäckman, L., & Nilsson, L.-G. (2005). Stability, growth, and decline in adult life span development of declarative memory: Cross-sectional and longitudinal data from a population-based study. Psychology and Aging, 20(1), 3–18. Ruppert, D., Wand, M. P., & Carroll, R. J. (2003). Semiparametric regression. Cambridge University Press. Saefken, B., Kneib, T., van Waveren, C.-S., & Greven, S. (2014). A unifying approach to the estimation of the conditional Akaike information in generalized linear mixed models. Electronic Journal of Statistics, 8(1), 201–225. Salthouse, T., Atkinson, T., & Berish, D. (2003). Executive functioning as a potential mediator of age-related cognitive decline in normal adults. Journal of Experimental Psychology: General, 132(4), 566–594. Salthouse, T. A. (2009). When does age-related cognitive decline begin? Neurobiology of Aging, 30(4), 507–514. Salthouse, T. A. (2011). Neuroanatomical substrates of age-related cognitive decline. Psychological Bulletin, 137(5), 753–784. Scarpina, F., & Tagini, S. (2017). The stroop color and word test. Frontiers in Psychology, 8, 557. Schaie, K. W. (2009). When does age-related cognitive decline begin?’’ Salthouse again reifies the ’ ’cross-sectional fallacy. Neurobiology of Aging, 30(4), 528–529. Silverman, B. W. (1985). Some aspects of the spline smoothing approach to non-parametric regression curve fitting. Journal of the Royal Statistical Society: Series B (Methodological), 47(1), 1–21. Sisco, S. M., Slonena, E., Okun, M. S., Bowers, D., & Price, C. C. (2016). Parkinson’s disease and the Stroop color word test: Processing speed and interference algorithms. The Clinical Neuropsychologist, 30(7), 1104–1117. Skaug, H. J. (2002). Automatic differentiation to facilitate maximum likelihood estimation in nonlinear random effects models. Journal of Computational and Graphical Statistics, 11(2), 458–470. Skaug, H. J., & Fournier, D. A. (2006). Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models. Computational Statistics & Data Analysis, 51(2), 699–709. Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling. Interdisciplinary statistics series. Chapman and Hall. Skrondal, A., & Rabe-Hesketh, S. (2007). Latent variable modelling: A survey. Scandinavian Journal of Statistics, 34(4), 712–745. Song, X., Lu, Z., & Feng, X. (2014). Latent variable models with nonparametric interaction effects of latent variables. Statistics in Medicine, 33(10), 1723–1737. Song, X.-Y., Chen, F., & Lu, Z.-H. (2013a). A Bayesian semiparametric dynamic two-level structural equation model for analyzing non-normal longitudinal data. Journal of Multivariate Analysis, 121, 87–108. Song, X.-Y., & Lu, Z.-H. (2010). Semiparametric latent variable models with Bayesian P-splines. Journal of Computational and Graphical Statistics, 19(3), 590–608. Song, X.-Y., Lu, Z.-H., Cai, J.-H., & Ip, E.H.-S. (2013b). A Bayesian modeling approach for generalized semiparametric structural equation models. Psychometrika, 78(4), 624–647. Sørensen, Ø., Walhovd, K. B., & Fjell, A. M. (2021). A recipe for accurate estimation of lifespan brain trajectories, distinguishing longitudinal and cohort effects. NeuroImage, 226, 117596. Spearman, C. (1904). “General Intelligence’’, objectively determined and measured. The American Journal of Psychology, 15(2), 201–292. Speed, T. (1991). That BLUP is a good thing: The estimation of random effects: Comment. Statistical Science, 6(1), 42–44. Staff, R. T., Murray, A. D., Ahearn, T. S., Mustafa, N., Fox, H. C., & Whalley, L. J. (2012). Childhood socioeconomic status and adult brain size: Childhood socioeconomic status influences adult hippocampal size. Annals of Neurology, 71(5), 653–660. Steele, K. M., Ball, T. N., & Runk, R. (1997). Listening to Mozart does not enhance backwards digit span performance. Perceptual and Motor Skills, 84(3–suppl), 1179–1184. Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18(6), 643–662. Swaminathan, H., & Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27(4), 361–370. Tiedemann, F. (2020). gghalves: Compose half-half plots using your favourite geoms. Tucker-Drob, E. M. (2019). Cognitive aging and dementia: A life-span perspective. Annual Review of Developmental Psychology, 1(1), 177–196. Tucker-Drob, E. M., Brandmaier, A. M., & Lindenberger, U. (2019). Coupled cognitive changes in adulthood: A meta-analysis. Psychological Bulletin, 145(3), 273–301. Vaida, F., & Blanchard, S. (2005). Conditional Akaike information for mixed-effects models. Biometrika, 92(2), 351–370. Verbyla, A. P., Cullis, B. R., Kenward, M. G., & Welham, S. J. (1999). The analysis of designed experiments and longitudinal data by using smoothing splines. Journal of the Royal Statistical Society: Series C (Applied Statistics), 48(3), 269–311. Walhovd, K. B., Fjell, A. M., Wang, Y., Amlien, I. K., Mowinckel, A. M., Lindenberger, U., Düzel, S., Bartrés-Faz, D., Ebmeier, K. P., Drevon, C. A., Baaré, W. F. C., Ghisletta, P., Johansen, L. B., Kievit, R. A., Henson, R. N., Madsen, K. S., Nyberg, L., Harris, R. J., Solé-Padullés, C., Pudas, S., Sørensen, Ø., Westerhausen, R., Zsoldos, E., Nawijn, L., Lyngstad, T. H., Suri, S., Penninx, B., Rogeberg, O. J., & Brandmaier, A. M. (2021). Education and income show heterogeneous relationships to lifespan brain and cognitive differences across European and US cohorts. Cerebral Cortex, 32(4), 839–854. Walhovd, K. B., Krogsrud, S. K., Amlien, I. K., Bartsch, H., Bjørnerud, A., Due-Tønnessen, P., Grydeland, H., Hagler, D. J., Håberg, A. K., Kremen, W. S., Ferschmann, L., Nyberg, L., Panizzon, M. S., Rohani, D. A., Skranes, J., Storsve, A. B., Sølsnes, A. E., Tamnes, C. K., Thompson, W. K., Fjell, A. M. (2016). Neurodevelopmental origins of lifespan changes in brain and cognition. Proceedings of the National Academy of Sciences, 113(33), 9357–9362. West, R. (1996). An application of prefrontal cortex function theory to cognitive aging. Psychological Bulletin, 120(2), 272–292. Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer. Wickham, H., Hester, J., Chang, W., Müller, K., & Cook, D. (2021). Memoise: ‘Memoisation’ of functions. Wood, S. N. (2003). Thin plate regression splines. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 65(1), 95–114. Wood, S. N. (2004). Stable and efficient multiple smoothing parameter estimation for generalized additive models. Journal of the American Statistical Association, 99(467), 673–686. Wood, S. N. (2006a). Low-rank scale-invariant tensor product smooths for generalized additive mixed models. Biometrics, 62(4), 1025–1036. Wood, S. N. (2006b). On confidence intervals for generalized additive models based on penalized regression splines. Australian & New Zealand Journal of Statistics, 48(4), 445–464. Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(1), 3–36. Wood, S. N. (2013). On p-values for smooth components of an extended generalized additive model. Biometrika, 100(1), 221–228. Wood, S. N. (2017a). Generalized additive models: An introduction with R (2nd ed.). Chapman and Hall. Wood, S. N. (2017b). P-splines with derivative based penalties and tensor product smoothing of unevenly distributed data. Statistics and Computing, 27(4), 985–989. Wood, S. N. (2020). Inference and computation with generalized additive models and their extensions. TEST, 29(2), 307–339. Wood, S. N., Pya, N., & Säfken, B. (2016). Smoothing parameter and model selection for general smooth models. Journal of the American Statistical Association, 111(516), 1548–1563. Wood, S. N., Scheipl, F., & Faraway, J. J. (2013). Straightforward intermediate rank tensor product smoothing in mixed models. Statistics and Computing, 23(3), 341–360. Woods, S. P., Delis, D. C., Scott, J. C., Kramer, J. H., & Holdnack, J. A. (2006). The California Verbal Learning Test-second edition: Test–retest reliability, practice effects, and reliable change indices for the standard and alternate forms. Archives of Clinical Neuropsychology, 21(5), 413–420. Yang, M., & Dunson, D. B. (2010). Bayesian semiparametric structural equation models with latent variables. Psychometrika, 75(4), 675–693. Yu, D., & Yau, K. K. W. (2012). Conditional Akaike information criterion for generalized linear mixed models. Computational Statistics & Data Analysis, 56(3), 629–644. Yu, Q., Daugherty, A. M., Anderson, D. M., Nishimura, M., Brush, D., Hardwick, A., Lacey, W., Raz, S., & Ofen, N. (2018). Socioeconomic status and hippocampal volume in children and young adults. Developmental Science, 21(3), e12561.