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I would if I could (self‐denial by conditional models)
Author(s) -
Slack J. R.
Publication year - 1973
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr009i001p00247
Subject(s) - estimator , sample (material) , denial , statistics , conditional expectation , econometrics , mathematics , conditional probability distribution , population , conditional probability , conditional variance , process (computing) , computer science , psychology , physics , sociology , demography , volatility (finance) , psychoanalysis , thermodynamics , operating system , autoregressive conditional heteroskedasticity
Since a finite sample of a random process rarely has statistics equal to the population values, a random process model (including the associated estimators of driving parameters) may be conditional in the sense that functional constraints on allowable driving parameters may be numerically violated. A conditional model then may generate finite samples yielding estimates of driving parameters unacceptable to its own restrictions and thereby may deny that it was the source of the sample.