Multiple influential point detection in high dimensional regression spaces

Multiple influential point detection in high dimensional regression spaces

Influence diagnosis is an integrated component of data analysis but has been severely underinvestigated in a high dimensional regression setting. One of the key challenges, even in a fixed dimensional setting, is how to deal with multiple influential points that give rise to masking and swamping eff...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:Journal of the Royal Statistical Society. Series B (Statistical Methodology). - Blackwell Publishers. - 81(2019), 2, Seite 385-408
1. Verfasser: Zhao, Junlong (VerfasserIn)
Weitere Verfasser: Liu, Chao, Niu, Lu, Leng, Chenlei
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2019
Zugriff auf das übergeordnete Werk:Journal of the Royal Statistical Society. Series B (Statistical Methodology)
LEADER 01000caa a22002652 4500
001 JST135924413
003 DE-627
005 20240625195333.0
007 cr uuu---uuuuu
008 240105s2019 xx |||||o 00| ||eng c
035 |a (DE-627)JST135924413 
035 |a (JST)26773215 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Zhao, Junlong  |e verfasserin  |4 aut 
245 1 0 |a Multiple influential point detection in high dimensional regression spaces 
264 1 |c 2019 
336 |a Text  |b txt  |2 rdacontent 
337 |a Computermedien  |b c  |2 rdamedia 
338 |a Online-Ressource  |b cr  |2 rdacarrier 
520 |a Influence diagnosis is an integrated component of data analysis but has been severely underinvestigated in a high dimensional regression setting. One of the key challenges, even in a fixed dimensional setting, is how to deal with multiple influential points that give rise to masking and swamping effects. The paper proposes a novel group deletion procedure referred to as multiple influential point detection by studying two extreme statistics based on a marginal-correlation-based influence measure. Named the min- and max-statistics, they have complementary properties in that the max-statistic is effective for overcoming the masking effect whereas the min-statistic is useful for overcoming the swamping effect. Combining their strengths, we further propose an efficient algorithm that can detect influential points with a prespecified false discovery rate. The influential point detection procedure proposed is simple to implement and efficient to run and enjoys attractive theoretical properties. Its effectiveness is verified empirically via extensive simulation study and data analysis. An R package implementing the procedure is freely available. 
540 |a © 2019 The Authors 
655 4 |a research-article 
700 1 |a Liu, Chao  |e verfasserin  |4 aut 
700 1 |a Niu, Lu  |e verfasserin  |4 aut 
700 1 |a Leng, Chenlei  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t Journal of the Royal Statistical Society. Series B (Statistical Methodology)  |d Blackwell Publishers  |g 81(2019), 2, Seite 385-408  |w (DE-627)30219746X  |w (DE-600)1490719-7  |x 14679868  |7 nnns 
773 1 8 |g volume:81  |g year:2019  |g number:2  |g pages:385-408 
856 4 0 |u https://www.jstor.org/stable/26773215  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_JST 
912 |a GBV_ILN_11 
912 |a GBV_ILN_20 
912 |a GBV_ILN_22 
912 |a GBV_ILN_23 
912 |a GBV_ILN_24 
912 |a GBV_ILN_26 
912 |a GBV_ILN_31 
912 |a GBV_ILN_32 
912 |a GBV_ILN_39 
912 |a GBV_ILN_40 
912 |a GBV_ILN_60 
912 |a GBV_ILN_62 
912 |a GBV_ILN_63 
912 |a GBV_ILN_65 
912 |a GBV_ILN_69 
912 |a GBV_ILN_70 
912 |a GBV_ILN_73 
912 |a GBV_ILN_74 
912 |a GBV_ILN_90 
912 |a GBV_ILN_95 
912 |a GBV_ILN_100 
912 |a GBV_ILN_101 
912 |a GBV_ILN_105 
912 |a GBV_ILN_110 
912 |a GBV_ILN_120 
912 |a GBV_ILN_138 
912 |a GBV_ILN_150 
912 |a GBV_ILN_151 
912 |a GBV_ILN_152 
912 |a GBV_ILN_161 
912 |a GBV_ILN_165 
912 |a GBV_ILN_170 
912 |a GBV_ILN_171 
912 |a GBV_ILN_187 
912 |a GBV_ILN_213 
912 |a GBV_ILN_224 
912 |a GBV_ILN_230 
912 |a GBV_ILN_266 
912 |a GBV_ILN_285 
912 |a GBV_ILN_293 
912 |a GBV_ILN_370 
912 |a GBV_ILN_374 
912 |a GBV_ILN_602 
912 |a GBV_ILN_636 
912 |a GBV_ILN_647 
912 |a GBV_ILN_702 
912 |a GBV_ILN_2001 
912 |a GBV_ILN_2003 
912 |a GBV_ILN_2004 
912 |a GBV_ILN_2005 
912 |a GBV_ILN_2006 
912 |a GBV_ILN_2007 
912 |a GBV_ILN_2008 
912 |a GBV_ILN_2009 
912 |a GBV_ILN_2010 
912 |a GBV_ILN_2011 
912 |a GBV_ILN_2014 
912 |a GBV_ILN_2015 
912 |a GBV_ILN_2018 
912 |a GBV_ILN_2020 
912 |a GBV_ILN_2021 
912 |a GBV_ILN_2025 
912 |a GBV_ILN_2026 
912 |a GBV_ILN_2027 
912 |a GBV_ILN_2031 
912 |a GBV_ILN_2034 
912 |a GBV_ILN_2037 
912 |a GBV_ILN_2038 
912 |a GBV_ILN_2039 
912 |a GBV_ILN_2044 
912 |a GBV_ILN_2048 
912 |a GBV_ILN_2049 
912 |a GBV_ILN_2050 
912 |a GBV_ILN_2055 
912 |a GBV_ILN_2056 
912 |a GBV_ILN_2057 
912 |a GBV_ILN_2059 
912 |a GBV_ILN_2061 
912 |a GBV_ILN_2064 
912 |a GBV_ILN_2065 
912 |a GBV_ILN_2068 
912 |a GBV_ILN_2088 
912 |a GBV_ILN_2093 
912 |a GBV_ILN_2106 
912 |a GBV_ILN_2107 
912 |a GBV_ILN_2108 
912 |a GBV_ILN_2110 
912 |a GBV_ILN_2111 
912 |a GBV_ILN_2112 
912 |a GBV_ILN_2113 
912 |a GBV_ILN_2118 
912 |a GBV_ILN_2119 
912 |a GBV_ILN_2122 
912 |a GBV_ILN_2129 
912 |a GBV_ILN_2143 
912 |a GBV_ILN_2144 
912 |a GBV_ILN_2147 
912 |a GBV_ILN_2148 
912 |a GBV_ILN_2152 
912 |a GBV_ILN_2153 
912 |a GBV_ILN_2190 
912 |a GBV_ILN_2232 
912 |a GBV_ILN_2336 
912 |a GBV_ILN_2470 
912 |a GBV_ILN_2507 
912 |a GBV_ILN_2522 
912 |a GBV_ILN_2548 
912 |a GBV_ILN_2932 
912 |a GBV_ILN_2947 
912 |a GBV_ILN_2949 
912 |a GBV_ILN_2950 
912 |a GBV_ILN_4012 
912 |a GBV_ILN_4035 
912 |a GBV_ILN_4037 
912 |a GBV_ILN_4046 
912 |a GBV_ILN_4112 
912 |a GBV_ILN_4125 
912 |a GBV_ILN_4126 
912 |a GBV_ILN_4242 
912 |a GBV_ILN_4246 
912 |a GBV_ILN_4249 
912 |a GBV_ILN_4251 
912 |a GBV_ILN_4305 
912 |a GBV_ILN_4306 
912 |a GBV_ILN_4307 
912 |a GBV_ILN_4313 
912 |a GBV_ILN_4322 
912 |a GBV_ILN_4323 
912 |a GBV_ILN_4324 
912 |a GBV_ILN_4325 
912 |a GBV_ILN_4326 
912 |a GBV_ILN_4333 
912 |a GBV_ILN_4334 
912 |a GBV_ILN_4335 
912 |a GBV_ILN_4336 
912 |a GBV_ILN_4338 
912 |a GBV_ILN_4346 
912 |a GBV_ILN_4393 
912 |a GBV_ILN_4700 
951 |a AR 
952 |d 81  |j 2019  |e 2  |h 385-408