State space model multiple imputation for missing data in non-stationary multivariate time series with application in digital Psychiatry
暂无分享,去创建一个
H. R. Eichi | J. Onnela | J. Baker | D. Ongur | L. Dixon | L. Valeri | Xinru Wang | Xiaoxuan Cai | Li Zeng
[1] Emily Huang,et al. Augmented Movelet Method for Activity Classification Using Smartphone Gyroscope and Accelerometer Data , 2019, Sensors.
[2] Hua-Liang Wei,et al. Handling missing data in multivariate time series using a vector autoregressive model-imputation (VAR-IM) algorithm , 2018, Neurocomputing.
[3] Jukka-Pekka Onnela,et al. Inferring mobility measures from GPS traces with missing data. , 2016, Biostatistics.
[4] Thomas R Sullivan,et al. Bias and Precision of the "Multiple Imputation, Then Deletion" Method for Dealing With Missing Outcome Data. , 2015, American journal of epidemiology.
[5] Audie A Atienza,et al. Mobile health technology evaluation: the mHealth evidence workshop. , 2013, American journal of preventive medicine.
[6] G. Casella,et al. Extending the State-Space Model to Accommodate Missing Values in Responses and Covariates , 2013 .
[7] Sanjoy K. Sinha,et al. Robust analysis of longitudinal data with nonignorable missing responses , 2012 .
[8] J. Os,et al. Momentary assessment technology as a tool to help patients with depression help themselves , 2011, Acta psychiatrica Scandinavica.
[9] Arnoud Arntz,et al. Does the weather make us sad? Meteorological determinants of mood and depression in the general population , 2010, Psychiatry Research.
[10] John B. Carlin,et al. Bias and efficiency of multiple imputation compared with complete‐case analysis for missing covariate values , 2010, Statistics in medicine.
[11] G. King,et al. What to Do about Missing Values in Time‐Series Cross‐Section Data , 2010 .
[12] M. Hendryx,et al. Social Support, Activities, and Recovery from Serious Mental Illness: STARS Study Findings , 2009, The Journal of Behavioral Health Services & Research.
[13] Giovanni Petris,et al. Dynamic Linear Models with R , 2009 .
[14] Joseph G. Ibrahim,et al. Missing data methods in longitudinal studies: a review , 2009 .
[15] Paul T. von Hippel,et al. Regression with missing Ys: An improved strategy for analyzing multiply imputed data , 2007, 1605.01095.
[16] Donald Hedeker,et al. Longitudinal Data Analysis , 2006 .
[17] J. Lieberman,et al. Effectiveness of antipsychotic drugs in patients with chronic schizophrenia. , 2005, The New England journal of medicine.
[18] G. Molenberghs. Applied Longitudinal Analysis , 2005 .
[19] S. Lipsitz,et al. Missing-Data Methods for Generalized Linear Models , 2005 .
[20] M. Peluso,et al. Physical activity and mental health: the association between exercise and mood. , 2005, Clinics.
[21] Patrick W. Corrigan,et al. Social Support and Recovery in People with Serious Mental Illnesses , 2004, Community Mental Health Journal.
[22] Jon Rigelsford,et al. Automotive Control Systems: For Engine, Driveline and Vehicle , 2004 .
[23] Howard M. Schwartz,et al. Exponential convergence of the Kalman filter based parameter estimation algorithm , 2003 .
[24] Nicole A. Lazar,et al. Statistical Analysis With Missing Data , 2003, Technometrics.
[25] D. Goldberg,et al. Social precursors to onset and recovery from episodes of common mental illness , 2003, Psychological Medicine.
[26] D. Rivers,et al. Model Selection Tests for Nonlinear Dynamic Models , 2002 .
[27] Jos Twisk,et al. Attrition in longitudinal studies. How to deal with missing data. , 2002, Journal of clinical epidemiology.
[28] M. Manoliu,et al. Energy futures prices: term structure models with Kalman filter estimation , 2002 .
[29] Stuart R. Lipsitz,et al. Analysis of longitudinal data with non‐ignorable non‐monotone missing values , 2002 .
[30] S. Haykin. Kalman Filtering and Neural Networks , 2001 .
[31] S. Lipsitz,et al. Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable , 2001 .
[32] N M Laird,et al. Generalized linear mixture models for handling nonignorable dropouts in longitudinal studies. , 2000, Biostatistics.
[33] D. Rubin,et al. Small-sample degrees of freedom with multiple imputation , 1999 .
[34] I. Miller,et al. Social support and the course of bipolar disorder. , 1999, Journal of abnormal psychology.
[35] Ehud Weinstein,et al. Iterative and sequential Kalman filter-based speech enhancement algorithms , 1998, IEEE Trans. Speech Audio Process..
[36] Rosario Romera,et al. Kalman filter with outliers and missing observations , 1997 .
[37] N M Laird,et al. Mixture models for the joint distribution of repeated measures and event times. , 1997, Statistics in medicine.
[38] R. Shumway. Longitudinal data with serial correlation: A state-space approach , 1995 .
[39] L. Ljung,et al. Exponential stability of general tracking algorithms , 1995, IEEE Trans. Autom. Control..
[40] D. Follmann,et al. An approximate generalized linear model with random effects for informative missing data. , 1995, Biometrics.
[41] Xiao-Li Meng,et al. Multiple-Imputation Inferences with Uncongenial Sources of Input , 1994 .
[42] R. Fildes. Forecasting structural time series models and the kalman filter: Andrew Harvey, 1989, (Cambridge University Press), 554 pp., ISBN 0-521-32196-4 , 1992 .
[43] Lennart Ljung,et al. Adaptation and tracking in system identification - A survey , 1990, Autom..
[44] Lei Guo. Estimating time-varying parameters by the Kalman filter based algorithm: stability and convergence , 1990 .
[45] Lennart Ljung,et al. Adaptation and Tracking in System Identification , 1988 .
[46] Raymond J. Carroll,et al. The Limiting Distribution of Least Squares in an Errors-in-Variables Regression Model , 1987 .
[47] Raymond J. Carroll,et al. Comparison of Least Squares and Errors-in-Variables Regression, with Special Reference to Randomized Analysis of Covariance , 1985 .
[48] Richard M. Johnstone,et al. Adaptive systems and time varying plants , 1983 .
[49] R. E. Kalman,et al. New Results in Linear Filtering and Prediction Theory , 1961 .
[50] R. E. Kalman,et al. A New Approach to Linear Filtering and Prediction Problems , 2002 .
[51] Ying Zhang,et al. Multivariate Time Series Imputation with Generative Adversarial Networks , 2018, NeurIPS.
[52] D. Ben-Zeev,et al. Mobile Health for Illness Management , 2017 .
[53] Franziska Abend,et al. State Space Modeling Of Time Series , 2016 .
[54] Phillipp Meister,et al. Statistical Signal Processing Detection Estimation And Time Series Analysis , 2016 .
[55] G. Arbanas. Diagnostic and Statistical Manual of Mental Disorders (DSM-5) , 2015 .
[56] Ciprian M Crainiceanu,et al. Normalization and extraction of interpretable metrics from raw accelerometry data. , 2014, Biostatistics.
[57] T. Lai,et al. Adaptive Filtering, Nonlinear State-Space Models, and Applications in Finance and Econometrics , 2013 .
[58] M. Hamer,et al. Physical activity, stress reduction, and mood: insight into immunological mechanisms. , 2012, Methods in molecular biology.
[59] Ciprian M Crainiceanu,et al. Movelets: A dictionary of movement. , 2012, Electronic journal of statistics.
[60] Jaap J. A. Denissen,et al. The Effects of Weather on Daily Mood: a Multilevel Approach , 2008 .
[61] Joseph G. Ibrahim,et al. Missing covariates in generalized linear models when the missing data mechanism is non‐ignorable , 1999 .
[62] Roderick J. A. Little,et al. Modeling the Drop-Out Mechanism in Repeated-Measures Studies , 1995 .
[63] S. F. Schmidt,et al. Application of State-Space Methods to Navigation Problems , 1966 .
[64] John B Carlin,et al. American Journal of Epidemiology Practice of Epidemiology Strategies for Multiple Imputation in Longitudinal Studies , 2022 .