Zobrazeno 1 - 10
of 12
pro vyhledávání: '"David P. Hasza"'
Autor:
David P. Hasza, Wayne A. Fuller
Publikováno v:
Journal of the American Statistical Association. 76:155-161
The prediction of the (n + s)th observation of the pth order autoregressive process is investigated. The mean square of the predictor error through terms of order n —1, conditional on Yn, Y n — 1, …, Y n — p + 1, is obtained for the stationar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ad173fa7682e486c22ef6ded4e360d1d
https://doi.org/10.1080/01621459.1981.10477622
https://doi.org/10.1080/01621459.1981.10477622
Autor:
Wayne A. Fuller, David P. Hasza
Publikováno v:
Journal of Econometrics. 13:139-157
The error made in predicting a first-order autoregressive process with unknown parameters is investigated. It is shown that the least squares predictor is unbiased for symmetric error distributions. Alternative predictors for stationary and non-stati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d1eb26d3418e8db13fe835e8a8869560
https://doi.org/10.1016/0304-4076(80)90012-3
https://doi.org/10.1016/0304-4076(80)90012-3
Autor:
David P. Hasza, V. A. Samaranayake
Publikováno v:
Journal of Time Series Analysis. 8:79-93
In this paper the large sample behaviour of the sample autocorrelation matrix Rn(h), (h being the lag, n the sample size), of a multivariate autoregressive time series with one of its characteristic roots equal to unity and the rest of the roots lyin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::af6ebdd346040737417fe0233d0c2e8b
https://doi.org/10.1111/j.1467-9892.1987.tb00422.x
https://doi.org/10.1111/j.1467-9892.1987.tb00422.x
Autor:
David P. Hasza, V. A. Samaranayake
Publikováno v:
Journal of Time Series Analysis. 9:361-383
The k-dimensional pth-order autoregressive processes {Yt} that are either stationary or have one unstable or explosive root are considered. The properties of the s-periods-ahead predictor Ŷn+s, obtained by replacing the unknown parameters in the exp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6f8ee810961bc43fb0de5125827b5d70
https://doi.org/10.1111/j.1467-9892.1988.tb00477.x
https://doi.org/10.1111/j.1467-9892.1988.tb00477.x
Autor:
David P. Hasza
Publikováno v:
Journal of the American Statistical Association. 75:349-352
The behavior of the sample autocorrelation function, r(k), for an integrated autoregressive moving average time series is examined. The nonnormal asymptotic distribution of r(k) is characterized as a function of lag k and the parameters of the proces
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b40f94983585093492ec1274f92fe4a9
https://doi.org/10.1080/01621459.1980.10477475
https://doi.org/10.1080/01621459.1980.10477475
Publikováno v:
Australian Journal of Statistics. 23:328-336
Summary Let X1…, Xm and Y1…, Yn be two independent sequences of i.i.d. random variables with distribution functions Fx(.|θ) and Fy(. | φ) respectively. Let g(θ, φ) be a real-valued function of the unknown parameters θ and φ. The purpose of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b30ab2cd4f6f8efdd6655c0d11972852
https://doi.org/10.1111/j.1467-842x.1981.tb00795.x
https://doi.org/10.1111/j.1467-842x.1981.tb00795.x
Publikováno v:
Journal of the American Statistical Association. 79:355-367
Regression estimators of coefficients in seasonal autoregressive models are described. The percentiles of the distributions for time series that have unit roots at the seasonal lag are computed by Monte Carlo integration for finite samples and by ana
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::707c6cd80e785dff86ced8e85f5c653a
https://doi.org/10.1080/01621459.1984.10478057
https://doi.org/10.1080/01621459.1984.10478057
Autor:
David P. Hasza
Publikováno v:
Communications in Statistics - Theory and Methods. 9:1411-1415
The maximum likelihood estimator of the parameters of a zero-mean normal stationary first-order autoregressive process is in-vestigated. it is shown that the likelihood function is uniquely maximized at a point in the interior of the parameter space.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a7cace7dc7b9d9ee6a1707ff8060dc13
https://doi.org/10.1080/03610928008827969
https://doi.org/10.1080/03610928008827969
Publikováno v:
Ann. Statist. 9, no. 3 (1981), 531-543
Let $Y_t$ satisfy the stochastic difference equation $Y_t = \sum^q_{i=1} \psi_{ti} \alpha_i + \sum^p_{j=1} \gamma_j Y_{t-j} + e_t,$ where the $\{\psi_{ti}\}$ are fixed sequences and (or) weakly stationary time series and the $e_t$ are independent ran
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::353d7078a086f671e015882099365098
http://projecteuclid.org/euclid.aos/1176345457
http://projecteuclid.org/euclid.aos/1176345457
Autor:
Wayne A. Fuller, David P. Hasza
Publikováno v:
Ann. Statist. 7, no. 5 (1979), 1106-1120
Let $Y_t$ satisfy the stochastic difference equation $Y_t = \sum^p_{j = 1}\eta_jY_{t - j} + e_t$ for $t = 1, 2, \cdots$, where the $e_t$ are independent identically distributed $(0, \sigma^2)$ random variables and the initial conditions $(Y_{-p + 1},
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::35ed71dd0e692e37ea34805a7f7244d4
https://doi.org/10.1214/aos/1176344793
https://doi.org/10.1214/aos/1176344793