FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3118782803> ?p ?o ?g. }

• W3118782803 endingPage "100905" @default.
• W3118782803 startingPage "100905" @default.
• W3118782803 abstract "• In this paper, a nonparametric regression based approach is proposed for mean surface estimation and local threshold exceedance testing in a spatial context. • The procedure takes into account complex structural issues in the data, such as non-linearity of the regression surface and spatially varying non-Gaussian nature of the error distribution, which may be related to latent ecological and other community related local and large-scale conditions of the landscape. • Kernel smoothing is used for surface estimation and bias corrected tests statistics are used for exceedance location detection. • Asymptotic formulas are given which can be directly implemented in any statistical software for analyzing other large spatial data sets. • A forestry data set from Alaska (source: USDA) and some simulated observations are used to illustrate the proposed method. In the era of big data analysis, it is of interest to develop diagnostic tools for preliminary scanning of large spatial databases. One problem is identification of locations where certain characteristics exceed a given norm, e.g. timber volume or mean tree diameter exceeding a user-defined threshold. Some of the challenges are, large size of the database, randomness, complex shape of the spatial mean surface, heterogeneity and others. In a step-by-step procedure, we propose a method for achieving this for large spatial data sets. For illustration, we work through a simulated spatial data set as well as a forest inventory data set from Alaska (source: USDA Forest Services). Working within the framework of nonparametric regression modeling, the proposed method can attain a high degree of flexibility regarding the shape of the spatial mean surface. Taking advantage of the large sample size, we also provide asymptotic formulas that are easy to implement in any statistical software." @default.
• W3118782803 created "2021-01-18" @default.
• W3118782803 creator A5012151815 @default.
• W3118782803 creator A5081525335 @default.
• W3118782803 date "2021-01-01" @default.
• W3118782803 modified "2023-09-25" @default.
• W3118782803 title "Finding exceedance locations in a large spatial database using nonparametric regression" @default.
• W3118782803 cites W1971358920 @default.
• W3118782803 cites W1984151813 @default.
• W3118782803 cites W2039411949 @default.
• W3118782803 cites W2041679522 @default.
• W3118782803 cites W2043463105 @default.
• W3118782803 cites W2071181166 @default.
• W3118782803 cites W2072075941 @default.
• W3118782803 cites W2074093400 @default.
• W3118782803 cites W2077423061 @default.
• W3118782803 cites W2134221848 @default.
• W3118782803 cites W2157460168 @default.
• W3118782803 cites W2167752422 @default.
• W3118782803 cites W2270358758 @default.
• W3118782803 cites W2572369644 @default.
• W3118782803 cites W263101040 @default.
• W3118782803 cites W2788650779 @default.
• W3118782803 cites W2988186520 @default.
• W3118782803 doi "https://doi.org/10.1016/j.ecocom.2020.100905" @default.
• W3118782803 hasPublicationYear "2021" @default.
• W3118782803 type Work @default.
• W3118782803 sameAs 3118782803 @default.
• W3118782803 citedByCount "0" @default.
• W3118782803 crossrefType "journal-article" @default.
• W3118782803 hasAuthorship W3118782803A5012151815 @default.
• W3118782803 hasAuthorship W3118782803A5081525335 @default.
• W3118782803 hasBestOaLocation W31187828031 @default.
• W3118782803 hasConcept C102366305 @default.
• W3118782803 hasConcept C105795698 @default.
• W3118782803 hasConcept C124101348 @default.
• W3118782803 hasConcept C159620131 @default.
• W3118782803 hasConcept C185429906 @default.
• W3118782803 hasConcept C203689450 @default.
• W3118782803 hasConcept C33923547 @default.
• W3118782803 hasConcept C3770464 @default.
• W3118782803 hasConcept C41008148 @default.
• W3118782803 hasConcept C58489278 @default.
• W3118782803 hasConcept C71134354 @default.
• W3118782803 hasConcept C74127309 @default.
• W3118782803 hasConceptScore W3118782803C102366305 @default.
• W3118782803 hasConceptScore W3118782803C105795698 @default.
• W3118782803 hasConceptScore W3118782803C124101348 @default.
• W3118782803 hasConceptScore W3118782803C159620131 @default.
• W3118782803 hasConceptScore W3118782803C185429906 @default.
• W3118782803 hasConceptScore W3118782803C203689450 @default.
• W3118782803 hasConceptScore W3118782803C33923547 @default.
• W3118782803 hasConceptScore W3118782803C3770464 @default.
• W3118782803 hasConceptScore W3118782803C41008148 @default.
• W3118782803 hasConceptScore W3118782803C58489278 @default.
• W3118782803 hasConceptScore W3118782803C71134354 @default.
• W3118782803 hasConceptScore W3118782803C74127309 @default.
• W3118782803 hasFunder F4320306114 @default.
• W3118782803 hasFunder F4320323874 @default.
• W3118782803 hasLocation W31187828031 @default.
• W3118782803 hasLocation W31187828032 @default.
• W3118782803 hasOpenAccess W3118782803 @default.
• W3118782803 hasPrimaryLocation W31187828031 @default.
• W3118782803 hasRelatedWork W1582575377 @default.
• W3118782803 hasRelatedWork W1823774651 @default.
• W3118782803 hasRelatedWork W2026116646 @default.
• W3118782803 hasRelatedWork W2168810229 @default.
• W3118782803 hasRelatedWork W2351560682 @default.
• W3118782803 hasRelatedWork W2964078813 @default.
• W3118782803 hasRelatedWork W2994374309 @default.
• W3118782803 hasRelatedWork W878795171 @default.
• W3118782803 hasRelatedWork W94454824 @default.
• W3118782803 hasRelatedWork W2250262961 @default.
• W3118782803 hasVolume "45" @default.
• W3118782803 isParatext "false" @default.
• W3118782803 isRetracted "false" @default.
• W3118782803 magId "3118782803" @default.
• W3118782803 workType "article" @default.