{"_buckets": {"deposit": "2ef19b46-8796-4c7b-aabd-9913bbc11433"}, "_deposit": {"id": "8105", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "8105"}, "status": "published"}, "_oai": {"id": "oai:kitami-it.repo.nii.ac.jp:00008105", "sets": ["1:86"]}, "author_link": ["42322", "42323", "42324", "42319", "44842", "42321"], "item_1646810750418": {"attribute_name": "\u51fa\u7248\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"subitem_version_resource": "http://purl.org/coar/version/c_970fb48d4fbd8a85", "subitem_version_type": "VoR"}]}, "item_3_alternative_title_198": {"attribute_name": "\u305d\u306e\u4ed6\u306e\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_alternative_title": "DETECTION OF MEAN AND VARIANCE JUMP AND NORMALIZATION OF OUTLIER IN HYDROLOGICAL TIME SERIES"}]}, "item_3_biblio_info_186": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2008-02", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "216", "bibliographicPageStart": "211", "bibliographicVolumeNumber": "52", "bibliographic_titles": [{"bibliographic_title": "\u6c34\u5de5\u5b66\u8ad6\u6587\u96c6"}]}]}, "item_3_description_184": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "A non-stationary time series is composed of the trend and some jumps by the change of a mean value and a\nvariance. There is no study example which has dealt with the power of test for a jump of variance in a hydrological\ntime series.\nIn this paper, the Monte Carlo simulations are applied to compare the power of the statistical tests: Student\u2019s\nt test, F test, Mann-Whitney test and bootstrap test to assess the significance of jumps to affect the mean value and the\nvariance of a time series with Normal , Logarithm Normal and General Extreme Value distributions. And also, the\noutliers in the annual maximum daily rainfall (MDR) and annual maximum non-rainfall days (NRD) data of each 22\nmeteorological observatories in Hokkaido are detected at Tomakomai and Hiroo for MDR data, and at Esashi for\nNRD data, respectively. Then the normalized conditions to change these three outliers into normal values are\nintroduced by using a numerical simulation.", "subitem_description_type": "Abstract"}]}, "item_3_full_name_183": {"attribute_name": "\u8457\u8005\u5225\u540d", "attribute_value_mlt": [{"nameIdentifiers": [{"nameIdentifier": "42322", "nameIdentifierScheme": "WEKO"}], "names": [{"name": "SADO, Kimiteru", "nameLang": "en"}]}, {"nameIdentifiers": [{"nameIdentifier": "42323", "nameIdentifierScheme": "WEKO"}], "names": [{"name": "NAKAO, Takashi", "nameLang": "en"}]}, {"nameIdentifiers": [{"nameIdentifier": "42324", "nameIdentifierScheme": "WEKO"}], "names": [{"name": "SUGIYAMA, Ichiro", "nameLang": "en"}]}]}, "item_3_publisher_212": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "\u516c\u76ca\u793e\u56e3\u6cd5\u4eba\u3000\u571f\u6728\u5b66\u4f1a"}]}, "item_3_relation_191": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": 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