Item talk:Q241821
From geokb
{
"USGS Publications Warehouse": { "@context": "https://schema.org", "@type": "Article", "additionalType": "Journal Article", "name": "L-moments and TL-moments of the generalized lambda distribution", "identifier": [ { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse IndexID", "value": "70029785", "url": "https://pubs.usgs.gov/publication/70029785" }, { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse Internal ID", "value": 70029785 }, { "@type": "PropertyValue", "propertyID": "DOI", "value": "10.1016/j.csda.2006.07.016", "url": "https://doi.org/10.1016/j.csda.2006.07.016" }, { "@type": "PropertyValue", "propertyID": "ISSN", "value": "01679473" } ], "journal": { "@type": "Periodical", "name": "Computational Statistics and Data Analysis", "volumeNumber": "51", "issueNumber": "9" }, "inLanguage": "en", "isPartOf": [ { "@type": "CreativeWorkSeries", "name": "Computational Statistics and Data Analysis" } ], "datePublished": "2007", "dateModified": "2012-03-12", "abstract": "The 4-parameter generalized lambda distribution (GLD) is a flexible distribution capable of mimicking the shapes of many distributions and data samples including those with heavy tails. The method of L-moments and the recently developed method of trimmed L-moments (TL-moments) are attractive techniques for parameter estimation for heavy-tailed distributions for which the L- and TL-moments have been defined. Analytical solutions for the first five L- and TL-moments in terms of GLD parameters are derived. Unfortunately, numerical methods are needed to compute the parameters from the L- or TL-moments. Algorithms are suggested for parameter estimation. Application of the GLD using both L- and TL-moment parameter estimates from example data is demonstrated, and comparison of the L-moment fit of the 4-parameter kappa distribution is made. A small simulation study of the 98th percentile (far-right tail) is conducted for a heavy-tail GLD with high-outlier contamination. The simulations show, with respect to estimation of the 98th-percent quantile, that TL-moments are less biased (more robost) in the presence of high-outlier contamination. However, the robustness comes at the expense of considerably more sampling variability. ?? 2006 Elsevier B.V. All rights reserved.", "publisher": { "@type": "Organization", "name": "U.S. Geological Survey" }, "author": [ { "@type": "Person", "name": "Asquith, W.H.", "givenName": "W.H.", "familyName": "Asquith" } ] }, "OpenAlex": { "abstract_inverted_index": { "The": [ 0, 26, 141 ], "4-parameter": [ 1, 117 ], "generalized": [ 2 ], "lambda": [ 3 ], "distribution": [ 4, 9, 119 ], "(GLD)": [ 5 ], "is": [ 6, 107, 120, 132 ], "a": [ 7, 135 ], "flexible": [ 8 ], "capable": [ 10 ], "of": [ 11, 15, 28, 35, 68, 94, 111, 115, 126, 148, 162, 172 ], "mimicking": [ 12 ], "the": [ 13, 31, 50, 60, 80, 83, 95, 112, 116, 127, 149, 160, 166, 170 ], "shapes": [ 14 ], "many": [ 16 ], "distributions": [ 17, 47 ], "and": [ 18, 30, 52, 64, 100, 109 ], "data": [ 19, 106 ], "samples": [ 20 ], "including": [ 21 ], "those": [ 22 ], "with": [ 23, 138, 144 ], "heavy": [ 24 ], "tails.": [ 25 ], "method": [ 27, 34 ], "L-moments": [ 29, 37 ], "recently": [ 32 ], "developed": [ 33 ], "trimmed": [ 36 ], "(TL-moments)": [ 38 ], "are": [ 39, 71, 76, 88, 154 ], "attractive": [ 40 ], "techniques": [ 41 ], "for": [ 42, 45, 48, 59, 90, 134 ], "parameter": [ 43, 91, 102 ], "estimation": [ 44, 147 ], "heavy-tailed": [ 46 ], "which": [ 49 ], "L-": [ 51, 63, 84, 99 ], "TL-moments": [ 53, 65, 153 ], "have": [ 54 ], "been": [ 55 ], "defined.": [ 56 ], "Analytical": [ 57 ], "solutions": [ 58 ], "first": [ 61 ], "five": [ 62 ], "in": [ 66, 159 ], "terms": [ 67 ], "GLD": [ 69, 96, 137 ], "parameters": [ 70, 81 ], "derived.": [ 72 ], "Unfortunately,": [ 73 ], "numerical": [ 74 ], "methods": [ 75 ], "needed": [ 77 ], "to": [ 78, 146 ], "compute": [ 79 ], "from": [ 82, 104 ], "or": [ 85 ], "TL-moments.": [ 86 ], "Algorithms": [ 87 ], "suggested": [ 89 ], "estimation.": [ 92 ], "Application": [ 93 ], "using": [ 97 ], "both": [ 98 ], "TL-moment": [ 101 ], "estimates": [ 103 ], "example": [ 105 ], "demonstrated,": [ 108 ], "comparison": [ 110 ], "L-moment": [ 113 ], "fit": [ 114 ], "kappa": [ 118 ], "made.": [ 121 ], "A": [ 122 ], "small": [ 123 ], "simulation": [ 124 ], "study": [ 125 ], "98th": [ 128 ], "percentile": [ 129 ], "(far-right": [ 130 ], "tail)": [ 131 ], "conducted": [ 133 ], "heavy-tail": [ 136 ], "high-outlier": [ 139, 163 ], "contamination.": [ 140, 164 ], "simulations": [ 142 ], "show,": [ 143 ], "respect": [ 145 ], "98th-percent": [ 150 ], "quantile,": [ 151 ], "that": [ 152 ], "less": [ 155 ], "biased": [ 156 ], "(more": [ 157 ], "robost)": [ 158 ], "presence": [ 161 ], "However,": [ 165 ], "robustness": [ 167 ], "comes": [ 168 ], "at": [ 169 ], "expense": [ 171 ], "considerably": [ 173 ], "more": [ 174 ], "sampling": [ 175 ], "variability.": [ 176 ] }, "apc_list": { "value": 3340, "currency": "USD", "value_usd": 3340, "provenance": "doaj" }, "apc_paid": null, "authorships": [ { "author_position": "first", "author": { "id": "https://openalex.org/A5073463401", "display_name": "William H. 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