Contemporary Statistical Models for the Plant and Soil Sciences

By O. Schabenberger, F. Pierce. 2001

899 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c6c5033ffa3f34363988c2ab823fdd3d4d41c67b

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In Environmental Engineering

Citations in the corpus

Comparison of Nearest Neighbor Methods for Estimating Basal Area and Stems per Hectare Using Aerial Auxiliary Variables (2005, methodology): “For geostatistical data, where each observation represents a point in continuous space (Cressie 1993, Schabenberger and Pierce 2002), a wide variety of kriging methods (e.g., universal kriging, regression kriging; see Odeh et al. 1995 and Nalder and Wein 1998 for comparisons of methods) and spatial…”
A STATISTICAL TEST TO SHOW NEGLIGIBLE TREND (2005, methodology): “If errors are correlated, either because of autocorrelation between observations in consecutive years or because of subsampling (e.g., more than one count in the same year), the annual trend and its SE can be estimated using a linear mixed model (Schabenberger and Pierce 2002: chapter 7).”
Prediction of spatial soil property information from ancillary sensor data using ordinary linear regression: Model derivations, residual assumptions and model validation tests (2008, methodology): “, non-multivariate normal) error assumptions (Gotway and Stroup, 1997) and/or nonlinear models (Schabenberger and Pierce, 2002; Schabenberger and Gotway, 2005), we will limit the current discussion to linear Gaussian error models.”
Spatial residual analysis of six modeling techniques (2005, methodology): “The EBLUP predictions from the LMM model were used to compute the model residuals ( Schabenberger and Pierce, 2002, p. 683 ).”
Regional mixed-effects height–diameter models for loblolly pine (Pinus taeda L.) plantations (2007, methodology): “Likelihoodbased methods are used for estimatingD and Ri based on the assumptions that bi and ei are normally distributed (see Littell et al. 1996; Schabenberger and Pierce 2002).”
Spatial Assessment of Model Errors from Four Regression Techniques (2005, methodology): “The EBLUP predictions at two locations with identical dbh values should differ depending on where the trees are located, whereas the regular predictions (not based on EBLUP) would be the same for a given value of dbh regardless of the locations of trees (Schabenberger and Pierce 2002).”
Performance evaluation of spectral vegetation indices using a statistical sensitivity function (2007, methodology): “…standard error, given by ry î ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi r2F iVðFVFÞ−1Fi q ; ð9Þ where F is the matrix of derivatives for approximating least squares estimations and Fi denotes the ith row of F matrix (Schabenberger & Pierce, 2002).”
Predicting weed emergence for eight annual species in the northeastern United States (2004, methodology): “The residual sum of squares and corrected total sum of squares from the nonlinear regression analysis were used to calculate the coefficients of determination between the predicted model and the observed field data (Schabenberger and Pierce 2002).”
On the Analysis of Combined Experiments (2011, background): “Excellent discussions on predicable functions over narrow inference spaces are available elsewhere (McLean et al. 1991; Schabenberger and Pierce 2002; Stroup, 1989a,b; Yang 2007).”
Characterizing properties and drivers of long distance movements by elephants (Loxodonta africana) in the Gourma, Mali (2013, background): “where k = tij tij, j indexes successive measurement times, r(2) is a constant and u is a constant related to the practical range of the correlated time-series (Schabenberger and Pierce, 2002).”
Nonlinear fixed and random generalized height–diameter models for Portuguese cork oak stands (2011, methodology): “As two random effects were specified for the model, an unstructured variance–covariance matrix D for random effects (Schabenberger and Pierce, 2002) was first assumed.”
Improved evaluation of cover crop species by growth and root factors (2010, methodology): “species-wise comparison of growth performance is only targeting the year effect and does not imply any interaction effect (Schabenberger and Pierce, 2002).”
Seventeen years of elevated CO2 exposure in a Chesapeake Bay Wetland: sustained but contrasting responses of plant growth and CO2 uptake (2005, methodology): “Repeated measure analyses were conducted to test for significant differences in shoot densities between the elevated-Ca, ambient-Ca and unchambered control plots, using the mixed model procedure of the SAS system (Littell et al., 1996; Schabenberger & Pierce, 2002).”
Some Parameter Estimation Issues in Functional-Structural Plant Modelling (2011, background): “It corresponds to using only the deterministic part of the dynamical model, which is really restrictive since in dynamic models, errors propagate (see an example on plant growth models in [56]).”
Explaining variation in Brazil nut fruit production (2007, methodology): “, exponential, power, and Gaussian) were incorporated and tested in the mixed model (Schabenberger and Pierce, 2001).”