{"id":"https://openalex.org/W2510977193","doi":"https://doi.org/10.1007/s11222-016-9698-2","title":"A wavelet lifting approach to long-memory estimation","display_name":"A wavelet lifting approach to long-memory estimation","publication_year":2016,"publication_date":"2016-09-03","ids":{"openalex":"https://openalex.org/W2510977193","doi":"https://doi.org/10.1007/s11222-016-9698-2","mag":"2510977193","pmid":"https://pubmed.ncbi.nlm.nih.gov/32025109"},"language":"en","primary_location":{"id":"doi:10.1007/s11222-016-9698-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-016-9698-2","pdf_url":"https://link.springer.com/content/pdf/10.1007%2Fs11222-016-9698-2.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007%2Fs11222-016-9698-2.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067487855","display_name":"Marina I. Knight","orcid":"https://orcid.org/0000-0001-9926-6092"},"institutions":[{"id":"https://openalex.org/I52099693","display_name":"University of York","ror":"https://ror.org/04m01e293","country_code":"GB","type":"education","lineage":["https://openalex.org/I52099693"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Marina I. Knight","raw_affiliation_strings":["1Department of Mathematics, University of York, Heslington, York, YO10 5DD UK","Department of Mathematics, University of York, Heslington, York, YO10 5DD, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"1Department of Mathematics, University of York, Heslington, York, YO10 5DD UK","institution_ids":["https://openalex.org/I52099693"]},{"raw_affiliation_string":"Department of Mathematics, University of York, Heslington, York, YO10 5DD, UK","institution_ids":["https://openalex.org/I52099693"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083214421","display_name":"Guy P. Nason","orcid":"https://orcid.org/0000-0002-4664-3154"},"institutions":[{"id":"https://openalex.org/I36234482","display_name":"University of Bristol","ror":"https://ror.org/0524sp257","country_code":"GB","type":"education","lineage":["https://openalex.org/I36234482"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Guy P. Nason","raw_affiliation_strings":["2School of Mathematics, University of Bristol, Bristol, BS8 1TW UK","School of Mathematics, University of Bristol, Bristol, BS8 1TW, UK"],"raw_orcid":"https://orcid.org/0000-0002-4664-3154","affiliations":[{"raw_affiliation_string":"2School of Mathematics, University of Bristol, Bristol, BS8 1TW UK","institution_ids":["https://openalex.org/I36234482"]},{"raw_affiliation_string":"School of Mathematics, University of Bristol, Bristol, BS8 1TW, UK","institution_ids":["https://openalex.org/I36234482"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062068953","display_name":"Matthew A. Nunes","orcid":"https://orcid.org/0000-0002-4719-2690"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Matthew A. Nunes","raw_affiliation_strings":["3Department of Mathematics and Statistics, Fylde College, Lancaster University, Lancaster, LA1 4YF UK","Department of Mathematics and Statistics, Fylde College, Lancaster University, Lancaster, LA1 4YF, UK"],"raw_orcid":"https://orcid.org/0000-0002-4719-2690","affiliations":[{"raw_affiliation_string":"3Department of Mathematics and Statistics, Fylde College, Lancaster University, Lancaster, LA1 4YF UK","institution_ids":["https://openalex.org/I67415387"]},{"raw_affiliation_string":"Department of Mathematics and Statistics, Fylde College, Lancaster University, Lancaster, LA1 4YF, UK","institution_ids":["https://openalex.org/I67415387"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5083214421"],"corresponding_institution_ids":["https://openalex.org/I36234482"],"apc_list":{"value":2090,"currency":"EUR","value_usd":2690},"apc_paid":{"value":2090,"currency":"EUR","value_usd":2690},"fwci":6.3437,"has_fulltext":true,"cited_by_count":25,"citation_normalized_percentile":{"value":0.96602536,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"27","issue":"6","first_page":"1453","last_page":"1471"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.983299970626831,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10029","display_name":"Climate variability and models","score":0.9785000085830688,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hurst-exponent","display_name":"Hurst exponent","score":0.7988790273666382},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7442718744277954},{"id":"https://openalex.org/keywords/decorrelation","display_name":"Decorrelation","score":0.6819218397140503},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6291891932487488},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.5524191856384277},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5402054786682129},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4709756374359131},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4573201537132263},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.44820550084114075},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4169197678565979},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4155859351158142},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4044197201728821},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.36732369661331177},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35205015540122986},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3073227107524872},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2737235724925995},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2721860408782959}],"concepts":[{"id":"https://openalex.org/C96835011","wikidata":"https://www.wikidata.org/wiki/Q1638718","display_name":"Hurst exponent","level":2,"score":0.7988790273666382},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7442718744277954},{"id":"https://openalex.org/C177860922","wikidata":"https://www.wikidata.org/wiki/Q788608","display_name":"Decorrelation","level":2,"score":0.6819218397140503},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6291891932487488},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.5524191856384277},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5402054786682129},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4709756374359131},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4573201537132263},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.44820550084114075},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4169197678565979},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4155859351158142},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4044197201728821},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.36732369661331177},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35205015540122986},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3073227107524872},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2737235724925995},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2721860408782959},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.1007/s11222-016-9698-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-016-9698-2","pdf_url":"https://link.springer.com/content/pdf/10.1007%2Fs11222-016-9698-2.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},{"id":"pmid:32025109","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32025109","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and computing","raw_type":null},{"id":"pmh:oai:research-information.bris.ac.uk:openaire_cris_publications/79e0aa57-3608-4a1b-a9cc-5dcd988452d2","is_oa":true,"landing_page_url":"https://research-information.bris.ac.uk/en/publications/79e0aa57-3608-4a1b-a9cc-5dcd988452d2","pdf_url":null,"source":{"id":"https://openalex.org/S4306400895","display_name":"Bristol Research (University of Bristol)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I36234482","host_organization_name":"University of Bristol","host_organization_lineage":["https://openalex.org/I36234482"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Knight, M I, Nason, G P & Nunes, M A 2017, 'A wavelet lifting approach to long-memory estimation', Statistics and Computing, vol. 27, no. 6, pp. 1453-1471. https://doi.org/10.1007/s11222-016-9698-2","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:eprints.lancs.ac.uk:80895","is_oa":true,"landing_page_url":null,"pdf_url":"https://eprints.lancs.ac.uk/id/eprint/80895/1/HurstKNNr2.pdf","source":{"id":"https://openalex.org/S4306401916","display_name":"Lancaster EPrints (Lancaster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67415387","host_organization_name":"Lancaster University","host_organization_lineage":["https://openalex.org/I67415387"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Journal Article"},{"id":"pmh:oai:eprints.whiterose.ac.uk:104510","is_oa":true,"landing_page_url":"https://orcid.org/0000-0001-9926-6092>,","pdf_url":"https://eprints.whiterose.ac.uk/104510/1/art_3A10.1007_2Fs11222_016_9698_2.pdf","source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"pmh:oai:pubmedcentral.nih.gov:6979511","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6979511","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Stat Comput","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1007/s11222-016-9698-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-016-9698-2","pdf_url":"https://link.springer.com/content/pdf/10.1007%2Fs11222-016-9698-2.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.8399999737739563}],"awards":[{"id":"https://openalex.org/G1240867942","display_name":null,"funder_award_id":"EP/K020951/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G1815804333","display_name":"Locally Stationary Time Series and Multiscale Methods for Statistics (LuSTruM)","funder_award_id":"EP/K020951/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2510977193.pdf","grobid_xml":"https://content.openalex.org/works/W2510977193.grobid-xml"},"referenced_works_count":91,"referenced_works":["https://openalex.org/W59771946","https://openalex.org/W357154381","https://openalex.org/W563452052","https://openalex.org/W607933207","https://openalex.org/W1507807393","https://openalex.org/W1508427692","https://openalex.org/W1515484043","https://openalex.org/W1588860668","https://openalex.org/W1594723396","https://openalex.org/W1598517949","https://openalex.org/W1598649019","https://openalex.org/W1607632979","https://openalex.org/W1653130573","https://openalex.org/W1662052757","https://openalex.org/W1908845184","https://openalex.org/W1965068440","https://openalex.org/W1968798675","https://openalex.org/W1970102186","https://openalex.org/W1974581749","https://openalex.org/W1974618482","https://openalex.org/W1977512462","https://openalex.org/W1986316936","https://openalex.org/W1986366694","https://openalex.org/W1986372184","https://openalex.org/W1986990752","https://openalex.org/W1993444966","https://openalex.org/W1996387237","https://openalex.org/W2011294979","https://openalex.org/W2011413345","https://openalex.org/W2012951355","https://openalex.org/W2012964785","https://openalex.org/W2017821362","https://openalex.org/W2019368654","https://openalex.org/W2020743612","https://openalex.org/W2023815042","https://openalex.org/W2025900077","https://openalex.org/W2030177986","https://openalex.org/W2031753087","https://openalex.org/W2040368492","https://openalex.org/W2040829107","https://openalex.org/W2041817790","https://openalex.org/W2042683845","https://openalex.org/W2047129703","https://openalex.org/W2047627251","https://openalex.org/W2053191845","https://openalex.org/W2055781590","https://openalex.org/W2062962812","https://openalex.org/W2065691363","https://openalex.org/W2066741947","https://openalex.org/W2067180668","https://openalex.org/W2070474233","https://openalex.org/W2076593368","https://openalex.org/W2081191044","https://openalex.org/W2084428435","https://openalex.org/W2084528906","https://openalex.org/W2085040762","https://openalex.org/W2089438877","https://openalex.org/W2092416301","https://openalex.org/W2100010101","https://openalex.org/W2100478220","https://openalex.org/W2102214133","https://openalex.org/W2104224693","https://openalex.org/W2106430853","https://openalex.org/W2107473219","https://openalex.org/W2120236826","https://openalex.org/W2121056014","https://openalex.org/W2136195593","https://openalex.org/W2139997679","https://openalex.org/W2142221197","https://openalex.org/W2144602403","https://openalex.org/W2151567244","https://openalex.org/W2158384564","https://openalex.org/W2161480748","https://openalex.org/W2164214015","https://openalex.org/W2165773639","https://openalex.org/W2168500104","https://openalex.org/W2169078684","https://openalex.org/W2171873915","https://openalex.org/W2178791007","https://openalex.org/W2340179090","https://openalex.org/W2341760625","https://openalex.org/W2497534953","https://openalex.org/W3011254666","https://openalex.org/W3101172635","https://openalex.org/W3159223345","https://openalex.org/W3180436685","https://openalex.org/W4242565096","https://openalex.org/W4251238138","https://openalex.org/W4285719527","https://openalex.org/W4295332281","https://openalex.org/W6603306832"],"related_works":["https://openalex.org/W1604939135","https://openalex.org/W2127058578","https://openalex.org/W2098040275","https://openalex.org/W2735368845","https://openalex.org/W2089764958","https://openalex.org/W3109905219","https://openalex.org/W2073586885","https://openalex.org/W2546477360","https://openalex.org/W2359691516","https://openalex.org/W1990069287"],"abstract_inverted_index":{"Reliable":[0],"estimation":[1,19,60,101],"of":[2,33,84,138],"long-range":[3],"dependence":[4],"parameters":[5],"is":[6,20,46,113,146],"vital":[7],"in":[8,13,36,41,155,172],"time":[9,65],"series.":[10],"For":[11],"example,":[12],"environmental":[14,173],"and":[15,28,67,89,148,174],"climate":[16,25,175],"science":[17,176],"such":[18,37,78],"often":[21,29,91],"key":[22],"to":[23,70,123,132,186],"understanding":[24],"dynamics,":[26],"variability":[27],"prediction.":[30],"The":[31,110],"challenge":[32],"data":[34,107,129,157],"collection":[35],"disciplines":[38],"means":[39],"that,":[40],"practice,":[42],"the":[43,82],"sampling":[44,108],"pattern":[45],"either":[47],"irregular":[48,128],"or":[49,74],"blighted":[50],"by":[51],"missing":[52,75],"observations.":[53],"Unfortunately,":[54],"virtually":[55],"all":[56],"existing":[57],"Hurst":[58,99],"parameter":[59],"methods":[61],"assume":[62],"regularly":[63],"sampled":[64],"series":[66],"require":[68],"modification":[69],"cope":[71],"with":[72,106,160],"irregularity":[73],"data.":[76],"However,":[77],"interventions":[79],"come":[80],"at":[81],"price":[83],"inducing":[85],"higher":[86],"estimator":[87],"bias":[88],"variation,":[90],"worryingly":[92],"ignored.":[93],"This":[94],"article":[95],"proposes":[96],"a":[97,116,134],"new":[98,111,166],"exponent":[100],"method":[102,112,145,164],"which":[103],"naturally":[104],"copes":[105],"irregularity.":[109],"based":[114],"on":[115,127,168],"multiscale":[117],"lifting":[118],"transform":[119],"exploiting":[120],"its":[121],"ability":[122],"produce":[124],"wavelet-like":[125],"coefficients":[126],"and,":[130],"simultaneously,":[131],"effect":[133],"necessary":[135],"powerful":[136],"decorrelation":[137],"those":[139],"coefficients.":[140],"Simulations":[141],"show":[142],"that":[143,180],"our":[144,163],"accurate":[147],"effective,":[149],"performing":[150],"well":[151],"against":[152],"competitors":[153],"even":[154],"regular":[156],"settings.":[158],"Armed":[159],"this":[161],"evidence":[162],"sheds":[165],"light":[167],"long-memory":[169],"intensity":[170],"results":[171],"applications,":[177],"sometimes":[178],"suggesting":[179],"different":[181],"scientific":[182],"conclusions":[183],"may":[184],"need":[185],"be":[187],"drawn.":[188]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-02T09:51:11.867554","created_date":"2025-10-10T00:00:00"}