Current issue: 56(2)

Under compilation: 56(3)

Scopus CiteScore 2021: 2.8
Scopus ranking of open access forestry journals: 8th
PlanS compliant
Silva Fennica 1926-1997
1990-1997
1980-1989
1970-1979
1960-1969
Acta Forestalia Fennica
1953-1968
1933-1952
1913-1932

Articles by Timothy G. Gregoire

Category: Article

article id 5615, category Article
Andrew P. Robinson, Timothy G. Gregoire, Harry T. Valentine. (1997). Cut-off importance sampling of bole volume. Silva Fennica vol. 31 no. 2 article id 5615. https://doi.org/10.14214/sf.a8516
Keywords: Monte Carlo methods; forest mensuration; mean-square error; two-stage sampling
Abstract | View details | Full text in PDF | Author Info

Cut-off importance sampling (CIS) is introduced as a means of sampling individual trees for the purpose of estimating bole volume. The novel feature of this variant of importance sampling is the establishment on the bole of a cut-off height, HC, above which sampling is precluded. An estimator of bole volume between predetermined heights HL and HU > HC is proposed, and its design-based bias and mean square error are derived. In an application of CIS as the second stage of a two-stage sample to estimate aggregate bole volume, the gain in precision realized from CIS more than offset its bias when compared to the precision of importance sampling when HC = HU.

  • Robinson, E-mail: ar@mm.unknown (email)
  • Gregoire, E-mail: tg@mm.unknown
  • Valentine, E-mail: hv@mm.unknown
article id 5546, category Article
Oliver Schabenberger, Timothy G. Gregoire. (1995). A conspectus on Estimating Function theory and its applicability to recurrent modeling issues in forest biometry. Silva Fennica vol. 29 no. 1 article id 5546. https://doi.org/10.14214/sf.a9197
Keywords: modelling; mixed models; statistical methods; longitudinal data; generalized linear models; optimality
Abstract | View details | Full text in PDF | Author Info

Much of forestry data is characterized by a longitudinal or repeated measures structure where multiple observations taken on some units of interest are correlated. Such dependencies are often ignored in favour of an apparently simpler analysis at the cost of invalid inferences. The last decade has brought to light many new statistical techniques that enable one to successfully deal with dependent observations. Although apparently distinct at first, the theory of Estimating Functions provides a natural extension of classical estimation that encompasses many of these new approaches. This contribution introduces Estimating Function Theory as a principle with potential for unification and presents examples covering a variety of modelling issues to demonstrate its applicability.

  • Schabenberger, E-mail: os@mm.unknown (email)
  • Gregoire, E-mail: tg@mm.unknown

Register
Click this link to register to Silva Fennica.
Log in
If you are a registered user, log in to save your selected articles for later access.
Contents alert
Sign up to receive alerts of new content
Your selected articles