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Volumn 18, Issue 2, 2002, Pages 283-290

Predicting the distribution function for long-memory processes

Author keywords

Kernel smoothing; Logistic transformation; Long range dependence; Nonparametric methods; Prediction; Probability; Time series

Indexed keywords


EID: 0036205785     PISSN: 01692070     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0169-2070(01)00158-3     Document Type: Article
Times cited : (15)

References (16)
  • 2
    • 0003502960 scopus 로고
    • Statistics For Long-memory Processes
    • New York: Chapman & Hall
    • (1994)
    • Beran, J.1
  • 3
    • 85001713374 scopus 로고    scopus 로고
    • SEMIFAR Models: A Semiparametric Framework for Modelling Trends, Long Range Dependence and Nonstationarity, Discussion Paper No. 99/16. Center of Finance and Econometrics, June
    • (1999)
    • Beran, J.1
  • 8
    • 0003524833 scopus 로고
    • Spline Smoothing and Nonparametric Regression
    • New York: Marcel Dekker
    • (1988)
    • Eubank, R.L.1
  • 9
    • 0035297926 scopus 로고    scopus 로고
    • Detection probability of trends in rare events: Theory and application to heavy precipitation in the alpine region
    • (2000) Journal of Climate , vol.14 , pp. 1568-1584
    • Frei, C.1    Schär, C.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.