Publication Information

{'id': 'https://openalex.org/W3170771683', 'doi': 'https://doi.org/10.22055/jamm.2021.35112.1859', 'title': 'Regression Modeling Via T-Lasso Bayesian Method', 'display_name': 'Regression Modeling Via T-Lasso Bayesian Method', 'publication_year': 2021, 'publication_date': '2021-06-22', 'ids': {'openalex': 'https://openalex.org/W3170771683', 'doi': 'https://doi.org/10.22055/jamm.2021.35112.1859', 'mag': '3170771683'}, 'language': 'en', 'primary_location': {'is_oa': False, 'landing_page_url': 'https://jamm.scu.ac.ir/article_16849_86e618a502854202a598a1d5cdbf0396.pdf', 'pdf_url': None, 'source': None, 'license': None, 'license_id': None, 'version': None, 'is_accepted': False, 'is_published': False}, 'type': 'article', 'type_crossref': 'journal-article', 'indexed_in': [], 'open_access': {'is_oa': False, 'oa_status': 'closed', 'oa_url': None, 'any_repository_has_fulltext': False}, 'authorships': [{'author_position': 'first', 'author': {'id': 'https://openalex.org/A5068898763', 'display_name': 'Ali Shadrokh', 'orcid': 'https://orcid.org/0000-0003-0069-8165'}, 'institutions': [{'id': 'https://openalex.org/I55547365', 'display_name': 'Payame Noor University', 'ror': 'https://ror.org/031699d98', 'country_code': 'IR', 'type': 'education', 'lineage': ['https://openalex.org/I55547365']}], 'countries': ['IR'], 'is_corresponding': False, 'raw_author_name': 'Ali Shadrokh', 'raw_affiliation_strings': ['Department of Statistics\u200e, \u200ePayame Noor University\u200e, \u200eP\u200e. \u200eO\u200e. \u200eBox 19395-4697\u200e, \u200eTehran\u200e, \u200eIran'], 'affiliations': [{'raw_affiliation_string': 'Department of Statistics\u200e, \u200ePayame Noor University\u200e, \u200eP\u200e. \u200eO\u200e. \u200eBox 19395-4697\u200e, \u200eTehran\u200e, \u200eIran', 'institution_ids': ['https://openalex.org/I55547365']}]}, {'author_position': 'middle', 'author': {'id': 'https://openalex.org/A5045214589', 'display_name': 'Zahra Khadembashiri', 'orcid': None}, 'institutions': [{'id': 'https://openalex.org/I55547365', 'display_name': 'Payame Noor University', 'ror': 'https://ror.org/031699d98', 'country_code': 'IR', 'type': 'education', 'lineage': ['https://openalex.org/I55547365']}], 'countries': ['IR'], 'is_corresponding': False, 'raw_author_name': 'Zahra Khadembashiri', 'raw_affiliation_strings': ['Department of Statistics\u200e, \u200ePayame Noor University\u200e, \u200eP\u200e. \u200eO\u200e. \u200eBox 19395-4697\u200e, \u200eTehran\u200e, \u200eIran'], 'affiliations': [{'raw_affiliation_string': 'Department of Statistics\u200e, \u200ePayame Noor University\u200e, \u200eP\u200e. \u200eO\u200e. \u200eBox 19395-4697\u200e, \u200eTehran\u200e, \u200eIran', 'institution_ids': ['https://openalex.org/I55547365']}]}, {'author_position': 'last', 'author': {'id': 'https://openalex.org/A5065252069', 'display_name': 'Masoud Yarmohammadi', 'orcid': 'https://orcid.org/0000-0001-7038-7106'}, 'institutions': [{'id': 'https://openalex.org/I55547365', 'display_name': 'Payame Noor University', 'ror': 'https://ror.org/031699d98', 'country_code': 'IR', 'type': 'education', 'lineage': ['https://openalex.org/I55547365']}], 'countries': ['IR'], 'is_corresponding': False, 'raw_author_name': 'Masoud Yarmohammadi', 'raw_affiliation_strings': ['Department of Statistics\u200e, \u200ePayame Noor University\u200e, \u200eP\u200e. \u200eO\u200e. \u200eBox 19395-4697\u200e, \u200eTehran\u200e, \u200eIran'], 'affiliations': [{'raw_affiliation_string': 'Department of Statistics\u200e, \u200ePayame Noor University\u200e, \u200eP\u200e. \u200eO\u200e. \u200eBox 19395-4697\u200e, \u200eTehran\u200e, \u200eIran', 'institution_ids': ['https://openalex.org/I55547365']}]}], 'institution_assertions': [], 'countries_distinct_count': 1, 'institutions_distinct_count': 1, 'corresponding_author_ids': [], 'corresponding_institution_ids': [], 'apc_list': None, 'apc_paid': None, 'fwci': 0.0, 'has_fulltext': False, 'cited_by_count': 0, 'citation_normalized_percentile': {'value': 0.0, 'is_in_top_1_percent': False, 'is_in_top_10_percent': False}, 'cited_by_percentile_year': {'min': 0, 'max': 57}, 'biblio': {'volume': '11', 'issue': '2', 'first_page': '365', 'last_page': '381'}, 'is_retracted': False, 'is_paratext': False, 'primary_topic': {'id': 'https://openalex.org/T11901', 'display_name': 'Bayesian Methods and Mixture Models', 'score': 0.9278, 'subfield': {'id': 'https://openalex.org/subfields/1702', 'display_name': 'Artificial Intelligence'}, 'field': {'id': 'https://openalex.org/fields/17', 'display_name': 'Computer Science'}, 'domain': {'id': 'https://openalex.org/domains/3', 'display_name': 'Physical Sciences'}}, 'topics': [{'id': 'https://openalex.org/T11901', 'display_name': 'Bayesian Methods and Mixture Models', 'score': 0.9278, 'subfield': {'id': 'https://openalex.org/subfields/1702', 'display_name': 'Artificial Intelligence'}, '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/T10100', 'display_name': 'Metaheuristic Optimization Algorithms Research', 'score': 0.9229, 'subfield': {'id': 'https://openalex.org/subfields/1702', 'display_name': 'Artificial Intelligence'}, '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/T11512', 'display_name': 'Anomaly Detection Techniques and Applications', 'score': 0.9102, 'subfield': {'id': 'https://openalex.org/subfields/1702', 'display_name': 'Artificial Intelligence'}, 'field': {'id': 'https://openalex.org/fields/17', 'display_name': 'Computer Science'}, 'domain': {'id': 'https://openalex.org/domains/3', 'display_name': 'Physical Sciences'}}], 'keywords': [{'id': 'https://openalex.org/keywords/lasso', 'display_name': 'Lasso', 'score': 0.737216}, {'id': 'https://openalex.org/keywords/regression-diagnostic', 'display_name': 'Regression diagnostic', 'score': 0.5872133}, {'id': 'https://openalex.org/keywords/factor-regression-model', 'display_name': 'Factor regression model', 'score': 0.42447978}, {'id': 'https://openalex.org/keywords/robust-regression', 'display_name': 'Robust regression', 'score': 0.42018178}], 'concepts': [{'id': 'https://openalex.org/C37616216', 'wikidata': 'https://www.wikidata.org/wiki/Q3218363', 'display_name': 'Lasso (programming language)', 'level': 2, 'score': 0.737216}, {'id': 'https://openalex.org/C79337645', 'wikidata': 'https://www.wikidata.org/wiki/Q779824', 'display_name': 'Outlier', 'level': 2, 'score': 0.61300015}, {'id': 'https://openalex.org/C37903108', 'wikidata': 'https://www.wikidata.org/wiki/Q4874474', 'display_name': 'Bayesian linear regression', 'level': 4, 'score': 0.6071433}, {'id': 'https://openalex.org/C152877465', 'wikidata': 'https://www.wikidata.org/wiki/Q208042', 'display_name': 'Regression analysis', 'level': 2, 'score': 0.6033002}, {'id': 'https://openalex.org/C57381214', 'wikidata': 'https://www.wikidata.org/wiki/Q55631393', 'display_name': 'Regression diagnostic', 'level': 4, 'score': 0.5872133}, {'id': 'https://openalex.org/C33923547', 'wikidata': 'https://www.wikidata.org/wiki/Q395', 'display_name': 'Mathematics', 'level': 0, 'score': 0.5633911}, {'id': 'https://openalex.org/C148483581', 'wikidata': 'https://www.wikidata.org/wiki/Q446488', 'display_name': 'Feature selection', 'level': 2, 'score': 0.55491614}, {'id': 'https://openalex.org/C93959086', 'wikidata': 'https://www.wikidata.org/wiki/Q6888345', 'display_name': 'Model selection', 'level': 2, 'score': 0.54814255}, {'id': 'https://openalex.org/C32224588', 'wikidata': 'https://www.wikidata.org/wiki/Q7250175', 'display_name': 'Proper linear model', 'level': 4, 'score': 0.507364}, {'id': 'https://openalex.org/C48921125', 'wikidata': 'https://www.wikidata.org/wiki/Q10861030', 'display_name': 'Linear regression', 'level': 2, 'score': 0.5048836}, {'id': 'https://openalex.org/C105795698', 'wikidata': 'https://www.wikidata.org/wiki/Q12483', 'display_name': 'Statistics', 'level': 1, 'score': 0.4991901}, {'id': 'https://openalex.org/C107673813', 'wikidata': 'https://www.wikidata.org/wiki/Q812534', 'display_name': 'Bayesian probability', 'level': 2, 'score': 0.44860792}, {'id': 'https://openalex.org/C83546350', 'wikidata': 'https://www.wikidata.org/wiki/Q1139051', 'display_name': 'Regression', 'level': 2, 'score': 0.42602074}, {'id': 'https://openalex.org/C93698799', 'wikidata': 'https://www.wikidata.org/wiki/Q5428730', 'display_name': 'Factor regression model', 'level': 5, 'score': 0.42447978}, {'id': 'https://openalex.org/C70259352', 'wikidata': 'https://www.wikidata.org/wiki/Q1847839', 'display_name': 'Robust regression', 'level': 3, 'score': 0.42018178}, {'id': 'https://openalex.org/C64946054', 'wikidata': 'https://www.wikidata.org/wiki/Q4874476', 'display_name': 'Bayesian multivariate linear regression', 'level': 3, 'score': 0.40436482}, {'id': 'https://openalex.org/C160234255', 'wikidata': 'https://www.wikidata.org/wiki/Q812535', 'display_name': 'Bayesian inference', 'level': 3, 'score': 0.30935228}, {'id': 'https://openalex.org/C41008148', 'wikidata': 'https://www.wikidata.org/wiki/Q21198', 'display_name': 'Computer science', 'level': 0, 'score': 0.2687418}, {'id': 'https://openalex.org/C154945302', 'wikidata': 'https://www.wikidata.org/wiki/Q11660', 'display_name': 'Artificial intelligence', 'level': 1, 'score': 0.2579452}, {'id': 'https://openalex.org/C136764020', 'wikidata': 'https://www.wikidata.org/wiki/Q466', 'display_name': 'World Wide Web', 'level': 1, 'score': 0.0}], 'mesh': [], 'locations_count': 1, 'locations': [{'is_oa': False, 'landing_page_url': 'https://jamm.scu.ac.ir/article_16849_86e618a502854202a598a1d5cdbf0396.pdf', 'pdf_url': None, 'source': None, 'license': None, 'license_id': None, 'version': None, 'is_accepted': False, 'is_published': False}], 'best_oa_location': None, 'sustainable_development_goals': [{'id': 'https://metadata.un.org/sdg/16', 'score': 0.55, 'display_name': 'Peace, justice, and strong institutions'}], 'grants': [], 'datasets': [], 'versions': [], 'referenced_works_count': 0, 'referenced_works': [], 'related_works': ['https://openalex.org/W36728617', 'https://openalex.org/W3121879701', 'https://openalex.org/W3045482412', 'https://openalex.org/W3035655668', 'https://openalex.org/W3005100705', 'https://openalex.org/W2980732017', 'https://openalex.org/W2951570555', 'https://openalex.org/W261761476', 'https://openalex.org/W2555487448', 'https://openalex.org/W2498354217', 'https://openalex.org/W2396538508', 'https://openalex.org/W2379748222', 'https://openalex.org/W2145358250', 'https://openalex.org/W2049936270', 'https://openalex.org/W2044635265', 'https://openalex.org/W2023325412', 'https://openalex.org/W2001414552', 'https://openalex.org/W2000259142', 'https://openalex.org/W1818777641', 'https://openalex.org/W1638663471'], 'abstract_inverted_index': {'Choosing': [0], 'the': [1, 7, 35, 47, 66, 75, 88, 92, 97, 133, 139, 153, 158, 163, 165, 188], 'optimal': [2, 16], 'model': [3, 17, 123, 131, 145, 160], 'is': [4, 23, 70, 124, 146, 169], 'one': [5], 'of': [6, 15, 49, 65, 87, 94, 135, 152, 175, 183, 190], 'important': [8, 26], 'issues': [9], 'in': [10, 20, 73, 91, 132, 138], 'regression': [11, 21, 59, 68, 72, 122, 130, 144, 159, 194], 'models.': [12], 'The': [13, 141], 'purpose': [14], 'selection': [18, 52, 117], 'methods': [19, 60, 197], 'models': [22, 69], 'to': [24, 33, 46, 79], 'determine': [25], 'explanatory': [27, 41], 'variables': [28, 31, 42], 'and': [29, 32, 40, 106, 178, 198], 'negligible': [30], 'express': [34], 'relationship': [36], 'between': [37], 'response': [38], 'variable': [39, 51, 116], 'more': [43], 'simply.': [44], 'Due': [45], 'limitations': [48], 'classical': [50], 'processes': [53], 'such': [54], 'as': [55, 171], 'stepwise': [56], 'selection,': [57], 'penalized': [58, 67], 'can': [61, 103, 109], 'be': [62, 104, 110], 'used.': [63], 'One': [64], 'Lasso': [71, 128], 'which': [74], 'errors': [76], 'are': [77], 'assumed': [78], 'follow': [80], 'a': [81, 115, 172, 180], 'normal': [82, 176], 'distribution.': [83, 185], 'For': [84], 'statistical': [85], 'analysis': [86], 'data': [89, 200], 'set': [90], 'presence': [93, 134], 'outlier': [95, 136], 'observations,': [96], 'student’s': [98], 't': [99], 'distribution': [100, 177], 'for': [101, 157], 'error': [102], 'used': [105], 'robust': [107], 'estimators': [108], 'provided.': [111], 'In': [112], 'this': [113], 'article,': [114], 'method': [118], 'called': [119], 'Bayesian': [120, 129, 142, 192], 'T-Lasso': [121, 143, 193], 'proposed': [125], 'based': [126], 'on': [127], 'observations': [137], 'data.': [140], 'presented': [147], 'with': [148], 'two': [149], 'different': [150], 'representations': [151], 'Laplace': [154, 166], 'density': [155, 167], 'function': [156, 168], 'coefficients,': [161], 'At': [162], 'first': [164], 'represented': [170], 'scale': [173, 181], 'mixture': [174, 182], 'then': [179], 'uniform': [184], 'We': [186], 'demonstrate': [187], 'utility': [189], 'our': [191], 'using': [195], 'simulation': [196], 'real': [199], 'analysis.': [201]}, 'cited_by_api_url': 'https://api.openalex.org/works?filter=cites:W3170771683', 'counts_by_year': [], 'updated_date': '2024-12-12T02:34:54.187744', 'created_date': '2021-06-22'}