{"doi":"10.1002/env.1079","title":"Particle size distribution in natural water via density estimation","abstract":"<jats:title>Abstract</jats:title><jats:p>In this paper we use density estimation to investigate the particle size distribution in surface water and to extract the components of density functions obtained. This distribution helps to investigate quality of water with respect to its physical characterization. The analysis is conducted using the two‐step density estimation method. The first step of the method is based on parametric density estimation and uses a modification of the EM algorithm to a normal mixture model. In the second step we apply the generalized kernel density estimator, being the convex combination of the well‐known kernel density estimators.</jats:p>","journal":"Environmetrics","year":2010,"id":29308,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":161897,"name":"Jolanta Jarnicka","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"24523987","pmcid":null,"openalex_id":"https://openalex.org/W2026307144","authors":[],"funders":[],"total_grants":0,"fwci":0.0,"citation_percentile":0.12108907,"influential_citations":0,"citation_trend":[],"oa_status":"closed","license":"http://onlinelibrary.wiley.com/termsAndConditions#vor","oa_locations":[{"url":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fenv.1079","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1002/env.1079","host_type":"publisher"},{"url":"https://doi.org/10.1002/env.1079","host_type":"journal"}],"fields_of_study":["Soil Geostatistics and Mapping","Integrated Water Resources Management","Hydrology and Drought Analysis","Mathematics","Environmental Science"],"mesh_terms":[],"keywords":["Multivariate kernel density estimation","Kernel density estimation","Density estimation","Estimator","Variable kernel density estimation","Mathematics","Kernel (algebra)","Parametric statistics","Applied mathematics","Statistics","Mathematical optimization","Biological system","Kernel method","Computer science","Artificial intelligence","Support vector machine"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Clean water and sanitation"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-08T23:09:24.586578Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}