Dataset Information

On Nonparametric Variance Estimation for Second-Order Statistics of Inhomogeneous Spatial Point Processes With a Known Parametric Intensity Form.

ABSTRACT: We introduce new variance estimation procedures for second-order statistics that are computed from a single realization of intensity reweighted stationary spatial point processes. The statistics are defined either on a subset B of the observation window or on the whole window. For the former, we use subblocks that have the same size and shape as B as "replicates" of B in order to estimate the target variance. For the latter, we develop a subsampling estimator for a key component in the target variance and estimate its other components by method-of-moment methods. Under some suitable conditions, we prove that the proposed variance estimators are consistent for the target variances in both cases. Simulations and an application to a real data example are used to demonstrate the usefulness of the proposed methods. This article has supplemental material online.

SUBMITTER: Guan Y

PROVIDER: S-EPMC2844672 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

On Nonparametric Variance Estimation for Second-Order Statistics of Inhomogeneous Spatial Point Processes With a Known Parametric Intensity Form.

Guan Yongtao Y

Journal of the American Statistical Association 20091201 488

We introduce new variance estimation procedures for second-order statistics that are computed from a single realization of intensity reweighted stationary spatial point processes. The statistics are defined either on a subset B of the observation window or on the whole window. For the former, we use subblocks that have the same size and shape as B as "replicates" of B in order to estimate the target variance. For the latter, we develop a subsampling estimator for a key component in the target va ...[more]

PMID: 20336182

Similar Datasets

Project description:Premise of the studyAccess to improved crop cultivars is the foundation for successful agriculture. New cultivars must have improved yields that are determined by quantitative and qualitative traits. Genotype-by-environment interactions (GEI) occur for quantitative traits such as reproductive fitness, longevity, height, weight, yield, and disease resistance. The stability of genotypes across a range of environments can be analyzed using GEI analysis. GEI analysis includes univariate and multivariate analyses with both parametric and non-parametric models.Methods and resultsThe program STABILITYSOFT is online software based on JavaScript and R to calculate several univariate parametric and non-parametric statistics for various crop traits. These statistics include Plaisted and Peterson's mean variance component (θ i ), Plaisted's GE variance component (θ (i) ), Wricke's ecovalence stability index (W i 2 ), regression coefficient (b i ), deviation from regression (S di 2 ), Shukla's stability variance (σ i 2 ), environmental coefficient of variance (CV i ), Nassar and Huhn's statistics (S (1) , S (2) ), Huhn's equation (S (3) and S (6) ), Thennarasu's non-parametric statistics (NP (i) ), and Kang's rank-sum. These statistics are important in the identification of stable genotypes; hence, this program can compare and select genotypes across multiple environment trials for a given data set. This program supports both the repeated data across environments and matrix data types. The accuracy of the results obtained from this software was tested on several crop plants.ConclusionsThis new software provides a user-friendly interface to estimate stability statistics accurately for plant scientists, agronomists, and breeders who deal with large volumes of quantitative data. This software can also show ranking patterns of genotypes and describe associations among different statistics with yield performance through a heat map plot. The software is available at https://mohsenyousefian.com/stabilitysoft/.

Dataset Information

On Nonparametric Variance Estimation for Second-Order Statistics of Inhomogeneous Spatial Point Processes With a Known Parametric Intensity Form.

Publications

On Nonparametric Variance Estimation for Second-Order Statistics of Inhomogeneous Spatial Point Processes With a Known Parametric Intensity Form.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets