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| 01968nam 2200301za 4500 |
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| 001 | 9.837887 |
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| 003 | CaOODSP |
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| 005 | 20221107151241 |
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| 007 | cr ||||||||||| |
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| 008 | 170607s1985 onc |o f|0| 0 eng d |
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| 040 | |aCaOODSP|beng |
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| 043 | |an-cn--- |
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| 086 | 1 |aCS11-614/85-28E-PDF |
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| 100 | 1 |aDagum, Estela Bee. |
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| 245 | 10|aModels for stationary stochastic processes |h[electronic resource] / |cby Estela Bee Dagum. |
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| 260 | |a[Ottawa] : |bStatistics Canada, |c[1985]. |
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| 300 | |a33 [27] p. : |bfigures. |
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| 490 | 1 |aWorking paper ; |v85-28 |
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| 500 | |aDigitized edition from print [produced by Statistics Canada]. |
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| 500 | |a"Working paper TSRA 85-028E." |
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| 504 | |aIncludes bibliographic references. |
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| 520 | |a"Two important categories of stochastic processes, the Normal Linear Stationary and the Normal Homogeneous Linear Non-Stationary processes have proved to be the easiest to deal with from a mathematical point of view. Furthermore, they seem to describe quite accurately the generating mechanism of many physical problems. The properties that make these types of processes very useful are that, by the assumption of normality they are fully characterized by their moments of the first and second order and, by being assumed stationary or stationary in the differences (homogeneous non-stationary) the mean and variance are constants and, thus, the autocovariance functions depend only on the time lags. Linear stochastic processes have often been applied to describe phenomena that belong to the natural and social sciences"--Introduction, p. 1. |
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| 692 | 07|2gccst|aMethodology |
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| 692 | 07|2gccst|aStatistical analysis |
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| 710 | 1 |aCanada. |bStatistics Canada. |bMethodology Branch. |
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| 830 | #0|aWorking paper (Statistics Canada. Methodology Branch)|v85-28|w(CaOODSP)9.834763 |
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| 856 | 40|qPDF|s4.07 MB|uhttps://publications.gc.ca/collections/collection_2017/statcan/11-613/CS11-614-85-28-eng.pdf |
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