HRS Bibliography

Bibliography Search
Export 440 results:
Filters: First Letter Of Title is P  [Clear All Filters]

P

Lee Y. Poverty Transitions for the Elderly. 2009:Ph.D.
Bronshtein G, Scott JS, Shoven JB, Slavov SNataraj. The Power of Working Longer. Cambridge, MA: National Bureau of Economic Research; 2018. doi:10.3386/w24226.
Barcelo H, Faul JD, Crimmins EM, Thyagarajan B. A Practical Cryopreservation and Staining Protocol for Immunophenotyping in Population Studies. Current Protocols in Cytometry. 2018;84(1):e35. doi:10.1002/cpcy.35.
http://www.ncbi.nlm.nih.gov/pubmed/30040214?dopt=Abstract
Lusardi A. Precautionary Saving and the Accumulation of Wealth. Dartmouth College; 2000.
Yilmazer T, Scharff RL. Precautionary Savings Against Health Risks: Evidence From the Health and Retirement Study. Research on Aging. 2013;36(2):180-206. doi:10.1177/0164027512473487.
Barsky R, Bound J, Charles KK, Lupton JP. A Precise Characterization of The Black-White Wealth Gap. University of Michigan; 2001.
Giustinelli P, Manski CF, Molinari F. Precise or Imprecise Probabilities? Evidence from Survey Response on Late-onset Dementia. National Bureau of Economic Research Working Paper Series. 2019;No. 26125. doi:10.3386/w26125.PDF icon w26125.pdf (535.1 KB)
Bobo JKay, Greek AA, Klepinger DH, Herting JR. Predicting 10-year alcohol use trajectories among men age 50 years and older. Am J Geriatr Psychiatry. 2013;21(2):204. doi:10.1097/JGP.0b013e3182423b4b.
Cruz M, Covinsky KE, Widera EW, Stijacic-Cenzer I, Lee SJ. Predicting 10-year mortality for older adults. JAMA. 2013;309(9):874-6. doi:10.1001/jama.2013.1184.
http://www.ncbi.nlm.nih.gov/pubmed/23462780?dopt=Abstract
Aschwanden D, Aichele S, Ghisletta P, et al. Predicting Cognitive Impairment and Dementia: A Machine Learning Approach. Journal of Alzheimer's disease : JAD. 2020;75(3):717-728. doi:10.3233/JAD-190967.
Hill NL, Mogle J, Bell TReed, Bhargava S, Wion RK, Bhang I. Predicting current and future anxiety symptoms in cognitively intact older adults with memory complaints. International Journal of Geriatric Psychiatry. 2019;34(12):1874-1882. doi:10.1002/gps.5204.
Boveda I, Metz AJ. Predicting End-of-Career Transitions for Baby Boomers Nearing Retirement Age. The Career Development Quarterly. 2016;64(2):153 - 168. doi:10.1002/cdq.2016.64.issue-210.1002/cdq.12048.
Puterman E, Weiss J, Hives BA, et al. Predicting mortality from 57 economic, behavioral, social, and psychological factors. Proceedings of the National Academy of Sciences. 2020. doi:10.1073/pnas.1918455117.
Banaszak-Holl J, A. Fendrick M, Foster NL, et al. Predicting Nursing Home Admission: Estimates from a seven-year follow-up of a nationally representative sample of older Americans. Alzheimer Disease and Associated Disorders. 2004;18(2):83-89.
Waddell EL, Jacobs-Lawson JM. Predicting positive well-being in older men and women. Int J Aging Hum Dev. 2010;70(3):181-97. doi:10.2190/AG.70.3.a.
http://www.ncbi.nlm.nih.gov/pubmed/20503804?dopt=Abstract
Leaf DErmini, Tysinger B, Goldman DP, Lakdawalla D. Predicting quantity and quality of life with the Future Elderly Model. Health Economics. 2020. doi:10.1002/hec.4169.
Blue L, Gill L, Faul JD, Bradway K, Stapleton D. Predicting Receipt of Social Security Administration Disability Benefits Using Biomarkers and Other Physiological Measures: Evidence From the Health and Retirement Study. Journal of Aging and Health. 2019;31(4):555-579. doi:10.1177/0898264317737893.
http://www.ncbi.nlm.nih.gov/pubmed/29254420?dopt=Abstract
Geiger JR, Wilks SE, Livermore MM. Predicting SNAP Participation in Older Adults: Do Age Categorizations Matter?. Educational Gerontology. 2014;40(12):932-946. doi:10.1080/03601277.2014.912837.
Choi NG, Bohman TM. Predicting the changes in depressive symptomatology in later life: how much do changes in health status, marital and caregiving status, work and volunteering, and health-related behaviors contribute?. J Aging Health. 2007;19(1):152-77. doi:10.1177/0898264306297602.
http://www.ncbi.nlm.nih.gov/pubmed/17215206?dopt=Abstract
M. Islam A, Chowdhury RI. Prediction of disease status: A regressive model approach for repeated measures. Statistical Methodology. 2010;7(5):520-540.
Chowdhury RI, M. Islam A. Prediction of Disease Status: Transition Model Approach for Repeated Measures. Pakistan Journal of Statistics . 2014;30(2):181-196.PDF icon PAKJSTAT.pdf (403.25 KB)
Elder TE. The predictive validity of subjective mortality expectations: evidence from the health and retirement study. Demography. 2013;50(2):569. doi:10.1007/s13524-012-0164-2.
Hurd MD, McGarry K. The Predictive Validity of Subjective Probabilities of Survival. The Economic Journal. 2002;112(482).
Lundin A, Leijon O, Vaez M, Hallgren M, Torgén M. Predictive validity of the Work Ability Index and its individual items in the general population. Scandinavian Journal of Public Health. 2017;45(4):350-356. doi:10.1177/1403494817702759.
Levine ME, Harrati A, Crimmins EM. Predictors and implications of accelerated cognitive aging. Biodemography and Social Biology. 2018;64(2):83-101. doi:10.1080/19485565.2018.1552513.