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3. Constrained maximum likelihood estimation of clusterwise linear regression models with unknown number of components

Author

Di Mari, R., Rocci, R., and Gattone, S. A.

Subjects
FOS: Computer and information sciences, Statistics - Computation, Computation (stat.CO)
Abstract

We consider an equivariant approach imposing data-driven bounds for the variances to avoid singular and spurious solutions in maximum likelihood (ML) estimation of clusterwise linear regression models. We investigate its use in the choice of the number of components and we propose a computational shortcut, which significantly reduces the computational time needed to tune the bounds on the data. In the simulation study and the two real-data applications, we show that the proposed methods guarantee a reliable assessment of the number of components compared to standard unconstrained methods, together with accurate model parameters estimation and cluster recovery.

Published
2018
Full Text
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13. Assessing the default risk by means of a discrete-time survival analysis approach

Author

DE LEONARDIS, D and Rocci, R

Subjects
unobserved heterogeneity (frailty), Modeling and Simulation, (baseline) hazard function, Cox's proportional hazards model, Cox’s proportional hazards model, Management Science and Operations Research, Settore SECS-S/01 - Statistica, General Business, Management and Accounting
Published
2008

30. DETERMINANTS OF 6-MONTH MORTALITY IN SURVIVORS OF MYOCARDIAL-INFARCTION AFTER THROMBOLYSIS - RESULTS OF THE GISSI-2 DATA-BASE

Author

VOLPI A, DEVITA C, FRANZOSI MG, GERACI E, MAGGIONI AP, MAURI F, NEGRI E, SANTORO E, TAVAZZI L, TOGNONI G, FERUGLIO GA, LOTTO A, ROVELLI F, SOLINAS P, BRUNO M, CAPPELLO T, COPPINI A, FINCATI F, MANTOVANI G, PANGRAZZI J, POGNA M, TURAZZA FM, ANSELMI M, BARBONAGLIA L, BIGI R, CAVALLI A, FRIGERIO M, GIORDANO A, GUALTIEROTTI C, TORTA D, CAROLA R, GIORDANO F, BARLOTTI R, LOPARCO G, VIGLINO GL, RUGGERI G, GIAMUNDO L, DANESI A, PACIARONI E, GAMBINI C, URBANO G, PURCARO A, FRANCESCONI M, FIGLIOLIA S, CANNONE M, ANTOLINI R, DEVOTI G, CRISTALLINI P, PORCIELLO PI, TEONI P, BURALI A, ZUCCONELLI V, DEMATTEIS C, IERVOGLINI A, SCATASTA M, AMABILI S, CARATTI CA, ZOLA G, FERRAGUTO P, SALICI G, CENTARO A, ROTIROTI D, GENOVESE M, GINEVRINO P, DAMATO N, ALTAMURA CM, COLONNA L, CASTELLANETA G, BOVENZI F, MESSINA D, GALANTINO A, CAMPOREALE N, CUCCHINI F, CAMPOSTELLA L, MALACRIDA R, GENONI M, PELLEGRINI P, BRIDDA A, RIGGI L, ACONE L, MOSCATIELLO G, BRUNO A, INVERNIZZI G, TESPILI M, GUAGLIUMI G, CASARI A, ALBANO T, TOMASSINI B, DIBIASE G, SCARAMUZZINO G, RUGGERO S, BRACCHETTI D, DECASTRO U, FULVI M, BRAITO E, ERLICHER A, OBERLECHNER W, GAGLIARDI RS, BIGHIGNOLI L, BONIZZATO G, RIZZI GM, SCAZZINA L, PERRINI A, STRANEO G, STRANEO U, SCIRE A, VERRIENTI A, GUADALUPI M, STORELLI A, ZUCCA L, DABUSTI M, ALBONICO B, DEPETRA V, TABACCHI GC, SCERVINO R, MEREU D, MAXIA P, BIANCO A, CRABU E, MANGIAMELI S, CENTAMORE G, MALFITANO D, AMICO C, VANCHERI F, SANTOPUOLI G, BALDINI F, PANTALEONI A, CONTESSOTTO F, TERLIZZI R, MERIGHI A, TURCHI E, TEGLIO V, PIGNATTI F, PEZZANA A, GOZZOLINO G, GIGLIO M, PETTINATI G, IEVA M, CIRICUGNO S, CORREALE E, ROMANO S, DIFUCCIA A, CASTELLANO B, NATALE A, CERNETTI C, CELEGON L, CANDELPERGHER G, ARIENZO F, RUSSO F, DEVIVO L, MAY L, ACHILLI G, BLASI A, SORRENTINO F, DATO A, GALLONE P, PALUMBO C, DELLAMONICA R, PAGANO L, ALBERTI A, ORSELLI L, DEPONTI C, PARMIGIANI ML, FERRARI M, ACITO P, BUSI F, DELLAVITTORIA G, BELLET C, BORTOLINI F, ROSSI A, CORONA C, BONDI S, NICCOLINI D, GAMBERI G, ARCURI G, MAIOLINO P, CARROZZA A, DELIO U, CAPRETTI G, MARINONI C, GUASCONI C, SONNINO S, PAGLIEI M, FERRARI G, LOMBARDI R, AGNELLI D, DERINALDIS G, CALCAGNILE A, SIGNORELLI S, BENDINELLI S, LUSETTI L, MOLLAIOLI M, COSMI F, PLASTINA F, VENNERI N, FERACO E, CATELLI P, POLUZZI C, DISTANTE S, BIANCHI C, COPPETTI S, ZAMPAGLIONE G, GATTO C, ZURLO R, USLENGHI E, MARGARIA F, MILANESE U, LOMANTO B, ZIACCHI V, RIVA D, BERTOCCHI P, TIRELLA G, DAULERIO M, SAURO G, BINI A, MAZZONI V, POGGI P, MARESTA A, JACOPI F, PATRONCINI A, PUPITA F, GAGGI S, FRAUSINI G, ANTONIOLI GE, MALACARNE C, CODECA L, CAPPATO R, ANDREOLI L, VARACCA S, BUIO E, FAZZINI PF, PUCCI P, SARRO F, VERGASSOLA R, BARCHIELLI M, DEMATTEIS D, CARRONE M, BRUNOZZI LT, MENICONI L, LIBERATI R, RADOGNA M, TALLONE M, CONTE R, IERI A, ZIPOLI A, SANSONI M, CANZIANI R, GUIDALI P, CRISTALLO E, MARIELLO F, MUZIO L, BENVENUTO MR, BALDINI MR, VECCHIO C, CHIARELLA F, FALCIDIENO M, CECCHI A, GIULIANO G, SEU V, PERUGINI P, TOSELLI A, BASSO F, CORTI E, ROSSI P, DELFINO R, CAPONNETTO S, GNECCO G, GHIGLIOTTI G, PENNESI A, LOMBARDI G, RUGGIERI A, BERTOLO L, SLOMP L, LANZETTA T, MAZZARONE L, CRESTI A, BELLODI G, ZUARINI AM, VENERI L, PARCHI C, GIOVANELLI N, NEGRONI S, DETHOMATIS M, BARGHINI A, MARINO E, RICCI D, LEMME P, DIGIACOMO U, AQUARO G, RONZANI G, OTTELLO B, VONTI V, MORETTI S, PALERMO R, MARSILI P, SIDERI F, RAGAZZINI G, GRAMENZI S, BATTISTINI S, DIODATO T, VALERIO A, TUCCI C, DEPASQUALE B, GELFO PG, BERTULLA A, BOLLINI R, DEMARCHI E, BACCA F, DEGIORGI V, LOCATELLI V, SAVOIA MT, FERRACINI C, BARBARESI F, COTOGNI A, FRANCO G, PASSONI F, DURBANO M, MORETTI G, PEROTTI S, CAPRETTI M, DELBENE P, CASCONE M, BALDINI U, ORLANDI M, ODDONE A, CAIZZI V, MASINI G, LAZZARI M, BALLERINI B, BOZZI L, MOCETTI T, BERTOLINI A, PASOTTI E, SANGUINETTI M, MANTOVANI R, TOGNOLI T, MAGGI A, TUSA M, CAMERONI E, GUERRA GP, REGGIANI A, REDAELLI S, GIUSTI S, TANTALO L, RIZZI A, DIGIOVANNI N, GUZZO V, GABRIELE M, COLOMBO G, ALBERZONI A, SALVIOLI G, GALFETTI F, DOVICO E, BELLUZZI F, GOLA E, CASELLATO F, LECCHI G, CONSOLO F, SACCA CB, CONSOLO A, PICCOLO E, GASPARINI G, MASSA D, BELLI C, DOSSENA MG, CORSINI C, SANNA GP, AZZOLLINI M, TRUAZZA F, NADOR F, DEMARTINI M, BOZZI G, SEREGNI R, PASTINE I, MORPURGO M, CASAZZA F, REGALIA F, MAGGIOLINI S, RIGO R, PANCALDI S, POZZETTI D, PASCOTTO P, FRANCESCHI L, DAINESE F, MELINI L, CAPPELLI C, BERNARDI C, PALMIERI M, BORGIONI L, ZILIO G, SANDRI R, ALITTO F, MASARO G, VALAGUSSA F, SCHIAVINA R, RAVESI D, DANIELLO L, PIANTADOSI FR, BARRA P, ROMEO D, MININNI N, SEVERINO S, MOSTACCI M, CASTELLARI M, BANDA D, ROLANDI R, VILLA WD, CARBONE V, ALLEGRI M, FASCIOLO L, PITTALIS M, MUREDDU V, SORO F, DELEDDA MG, MARRAS E, MARCHI SM, DELUCA C, MANETTA M, VOLTA SD, SPERANDEO V, DONZELLI M, VITRANO MG, PITROLO F, LAMONICA S, BELLANCA G, MESSINA G, MIRTO U, RAINERI A, TRAINA M, DIBENEDETTO A, RIBAUDO E, DIFRANCESCO M, RONCHITELLI R, CARONE M, DIGREGORIO D, DIPAOLO G, PASQUALE M, COREA L, COCCHIERI M, ALUNNI G, PAPI L, CHIRIATTI G, LUPETTI M, GAZZOLA U, ARRUZZOLI S, VILLANI GQ, MELLINI M, MADRUZZA L, PIAZZA R, MICHELI G, FRANCHINI C, BECHI S, MARTINES C, MARCHESE D, GABBIA G, BIGALLI A, CIUTI M, CABANI E, DELCITERNA F, ALFIERI A, CHITI M, LONGHINI J, CODELUPPI P, NEGRELLI M, ZANUTTINI D, NICOLOSI GL, MARTIN G, PETRELLA A, BARDAZZI L, BIANCO GA, CELLAMARE G, GIANNELLI F, LICITRA G, LICITRA R, LETTICA GV, TUMIOTTO G, BELLANTI G, BOSI S, CASALI G, MONDUCCI I, BARONE A, PARENTI F, HEYMAN J, COZZI E, BALDACCI G, BACCOS D, BRIGHI F, DESANCTIS A, BOCK R, ROSSI F, AMATI P, SEMPRINI P, NARDELLI A, BOTTERO G, VARTOLO C, MILAZZOTTO F, DICROCE G, DIMARIO F, ANGRISANI G, AZZOLINI P, NEJA CP, MANZOLI U, ROSSI E, TRANI C, MASINI V, SEBASTIANI F, TOPAI M, BORGIA MC, LUCIANI C, FERRI F, DEPAOLA D, CAPURSO S, TUGNOLI F, VETTA C, ALTIERI T, BORZI M, VISCOMI A, STRIANO U, SALITURI S, ZONZIN P, FIORENCIS R, BADIN A, RAVERA B, BALDI C, SILVESTRI F, ALLEMANO P, REYNAUD S, SANSON A, MILANI L, DESIMONE MV, RUSSO A, VILLELLA A, GRAZINI M, AMIDEI S, ANSELMI L, PICCANICOLINO R, MASCELLI G, TAGLIAMONTE A, MESSINA V, TEDESCHI C, BOSSI M, BISIOLI M, TACCHI G, PAGNI G, VIVALDI F, IBBA GV, SANNIA L, PEDRAZZINI F, BAGNI E, FABII S, ALVINO A, ANTONIELLI E, DORONZO B, MARTINENGO E, BECCHI G, SALMOIRAGHI A, DIGIOVANNA F, CARAMANNO G, CAPORICCI D, BRUN M, GIANI P, FERRARIO G, PECI P, RONCONI G, SKOUSE D, GIUSTINIANI S, CUCCHI GF, TAVASCI E, SILVERII A, MARCELLINI G, SPECA G, STANISCIA D, CIMINO A, SERAFINI N, DEBONIS P, CERRUTI P, BAZZUCCHI M, DALPRA F, SPEROTTO C, MOLE GD, BARBANO G, POMARI F, GASCHINO G, PARIGI A, GANDOLFO N, RONDONI F, BRUSCA A, DILEO M, GOLZIO PG, ABRATE M, SCLAVO MG, ROCCI R, POGGIO G, GIANI S, CUZZUCREA D, BRASCHI GB, SCIACCA R, SAMMARTANO A, FURLANELLO F, BRAITO G, CUZZATO V, TOTIS O, FAURETTO F, LEO F, GALATI A, PALMA P, CAMERINI F, MORGERA T, BARBIERI L, SLAVICK GA, FRESCO C, CUDA A, SARNICOLA P, ARZILLO P, BINAGHI G, MACCHI G, CALVERI G, DIMARCO G, LEVANTESI G, PANERAI C, CATURELLI G, FACCHIN L, SARTORE G, ZARDINI P, MARINO P, CARBONIERI E, NAVA S, MAZZINI C, NAVA R, SERRA N, SASSARA M, NICROSINI F, GANDOLFI P, BERGOGNONI G, BALLESTRA AM, VIOLO C, VOLPI A, DEVITA C, FRANZOSI MG, GERACI E, MAGGIONI AP, MAURI F, NEGRI E, SANTORO E, TAVAZZI L, TOGNONI G, FERUGLIO GA, LOTTO A, ROVELLI F, SOLINAS P, BRUNO M, CAPPELLO T, COPPINI A, FINCATI F, MANTOVANI G, PANGRAZZI J, POGNA M, TURAZZA FM, ANSELMI M, BARBONAGLIA L, BIGI R, CAVALLI A, FRIGERIO M, GIORDANO A, GUALTIEROTTI C, TORTA D, CAROLA R, GIORDANO F, BARLOTTI R, LOPARCO G, VIGLINO GL, RUGGERI G, GIAMUNDO L, DANESI A, PACIARONI E, GAMBINI C, URBANO G, PURCARO A, FRANCESCONI M, FIGLIOLIA S, CANNONE M, ANTOLINI R, DEVOTI G, CRISTALLINI P, PORCIELLO PI, TEONI P, BURALI A, ZUCCONELLI V, DEMATTEIS C, IERVOGLINI A, SCATASTA M, AMABILI S, CARATTI CA, ZOLA G, FERRAGUTO P, SALICI G, CENTARO A, ROTIROTI D, GENOVESE M, GINEVRINO P, DAMATO N, ALTAMURA CM, COLONNA L, CASTELLANETA G, BOVENZI F, MESSINA D, GALANTINO A, CAMPOREALE N, CUCCHINI F, CAMPOSTELLA L, MALACRIDA R, GENONI M, PELLEGRINI P, BRIDDA A, RIGGI L, ACONE L, MOSCATIELLO G, BRUNO A, INVERNIZZI G, TESPILI M, GUAGLIUMI G, CASARI A, ALBANO T, TOMASSINI B, DIBIASE G, SCARAMUZZINO G, RUGGERO S, BRACCHETTI D, DECASTRO U, FULVI M, BRAITO E, ERLICHER A, OBERLECHNER W, GAGLIARDI RS, BIGHIGNOLI L, BONIZZATO G, RIZZI GM, SCAZZINA L, PERRINI A, STRANEO G, STRANEO U, SCIRE A, VERRIENTI A, GUADALUPI M, STORELLI A, ZUCCA L, DABUSTI M, ALBONICO B, DEPETRA V, TABACCHI GC, SCERVINO R, MEREU D, MAXIA P, BIANCO A, CRABU E, MANGIAMELI S, CENTAMORE G, MALFITANO D, AMICO C, VANCHERI F, SANTOPUOLI G, BALDINI F, PANTALEONI A, CONTESSOTTO F, TERLIZZI R, MERIGHI A, TURCHI E, TEGLIO V, PIGNATTI F, PEZZANA A, GOZZOLINO G, GIGLIO M, PETTINATI G, IEVA M, CIRICUGNO S, CORREALE E, ROMANO S, DIFUCCIA A, CASTELLANO B, NATALE A, CERNETTI C, CELEGON L, CANDELPERGHER G, ARIENZO F, RUSSO F, DEVIVO L, MAY L, ACHILLI G, BLASI A, SORRENTINO F, DATO A, GALLONE P, PALUMBO C, DELLAMONICA R, PAGANO L, ALBERTI A, ORSELLI L, DEPONTI C, PARMIGIANI ML, FERRARI M, ACITO P, BUSI F, DELLAVITTORIA G, BELLET C, BORTOLINI F, ROSSI A, CORONA C, BONDI S, NICCOLINI D, GAMBERI G, ARCURI G, MAIOLINO P, CARROZZA A, DELIO U, CAPRETTI G, MARINONI C, GUASCONI C, SONNINO S, PAGLIEI M, FERRARI G, LOMBARDI R, AGNELLI D, DERINALDIS G, CALCAGNILE A, SIGNORELLI S, BENDINELLI S, LUSETTI L, MOLLAIOLI M, COSMI F, PLASTINA F, VENNERI N, FERACO E, CATELLI P, POLUZZI C, DISTANTE S, BIANCHI C, COPPETTI S, ZAMPAGLIONE G, GATTO C, ZURLO R, USLENGHI E, MARGARIA F, MILANESE U, LOMANTO B, ZIACCHI V, RIVA D, BERTOCCHI P, TIRELLA G, DAULERIO M, SAURO G, BINI A, MAZZONI V, POGGI P, MARESTA A, JACOPI F, PATRONCINI A, PUPITA F, GAGGI S, FRAUSINI G, ANTONIOLI GE, MALACARNE C, CODECA L, CAPPATO R, ANDREOLI L, VARACCA S, BUIO E, FAZZINI PF, PUCCI P, SARRO F, VERGASSOLA R, BARCHIELLI M, DEMATTEIS D, CARRONE M, BRUNOZZI LT, MENICONI L, LIBERATI R, RADOGNA M, TALLONE M, CONTE R, IERI A, ZIPOLI A, SANSONI M, CANZIANI R, GUIDALI P, CRISTALLO E, MARIELLO F, MUZIO L, BENVENUTO MR, BALDINI MR, VECCHIO C, CHIARELLA F, FALCIDIENO M, CECCHI A, GIULIANO G, SEU V, PERUGINI P, TOSELLI A, BASSO F, CORTI E, ROSSI P, DELFINO R, CAPONNETTO S, GNECCO G, GHIGLIOTTI G, PENNESI A, LOMBARDI G, RUGGIERI A, BERTOLO L, SLOMP L, LANZETTA T, MAZZARONE L, CRESTI A, BELLODI G, ZUARINI AM, VENERI L, PARCHI C, GIOVANELLI N, NEGRONI S, DETHOMATIS M, BARGHINI A, MARINO E, RICCI D, LEMME P, DIGIACOMO U, AQUARO G, RONZANI G, OTTELLO B, VONTI V, MORETTI S, PALERMO R, MARSILI P, SIDERI F, RAGAZZINI G, GRAMENZI S, BATTISTINI S, DIODATO T, VALERIO A, TUCCI C, DEPASQUALE B, GELFO PG, BERTULLA A, BOLLINI R, DEMARCHI E, BACCA F, DEGIORGI V, LOCATELLI V, SAVOIA MT, FERRACINI C, BARBARESI F, COTOGNI A, FRANCO G, PASSONI F, DURBANO M, MORETTI G, PEROTTI S, CAPRETTI M, DELBENE P, CASCONE M, BALDINI U, ORLANDI M, ODDONE A, CAIZZI V, MASINI G, LAZZARI M, BALLERINI B, BOZZI L, MOCETTI T, BERTOLINI A, PASOTTI E, SANGUINETTI M, MANTOVANI R, TOGNOLI T, MAGGI A, TUSA M, CAMERONI E, GUERRA GP, REGGIANI A, REDAELLI S, GIUSTI S, TANTALO L, RIZZI A, DIGIOVANNI N, GUZZO V, GABRIELE M, COLOMBO G, ALBERZONI A, SALVIOLI G, GALFETTI F, DOVICO E, BELLUZZI F, GOLA E, CASELLATO F, LECCHI G, CONSOLO F, SACCA CB, CONSOLO A, PICCOLO E, GASPARINI G, MASSA D, BELLI C, DOSSENA MG, CORSINI C, SANNA GP, AZZOLLINI M, TRUAZZA F, NADOR F, DEMARTINI M, BOZZI G, SEREGNI R, PASTINE I, MORPURGO M, CASAZZA F, REGALIA F, MAGGIOLINI S, RIGO R, PANCALDI S, POZZETTI D, PASCOTTO P, FRANCESCHI L, DAINESE F, MELINI L, CAPPELLI C, BERNARDI C, PALMIERI M, BORGIONI L, ZILIO G, SANDRI R, ALITTO F, MASARO G, VALAGUSSA F, SCHIAVINA R, RAVESI D, DANIELLO L, PIANTADOSI FR, BARRA P, ROMEO D, MININNI N, SEVERINO S, MOSTACCI M, CASTELLARI M, BANDA D, ROLANDI R, VILLA WD, CARBONE V, ALLEGRI M, FASCIOLO L, PITTALIS M, MUREDDU V, SORO F, DELEDDA MG, MARRAS E, MARCHI SM, DELUCA C, MANETTA M, VOLTA SD, SPERANDEO V, DONZELLI M, VITRANO MG, PITROLO F, LAMONICA S, BELLANCA G, MESSINA G, MIRTO U, RAINERI A, TRAINA M, DIBENEDETTO A, RIBAUDO E, DIFRANCESCO M, RONCHITELLI R, CARONE M, DIGREGORIO D, DIPAOLO G, PASQUALE M, COREA L, COCCHIERI M, ALUNNI G, PAPI L, CHIRIATTI G, LUPETTI M, GAZZOLA U, ARRUZZOLI S, VILLANI GQ, MELLINI M, MADRUZZA L, PIAZZA R, MICHELI G, FRANCHINI C, BECHI S, MARTINES C, MARCHESE D, GABBIA G, BIGALLI A, CIUTI M, CABANI E, DELCITERNA F, ALFIERI A, CHITI M, LONGHINI J, CODELUPPI P, NEGRELLI M, ZANUTTINI D, NICOLOSI GL, MARTIN G, PETRELLA A, BARDAZZI L, BIANCO GA, CELLAMARE G, GIANNELLI F, LICITRA G, LICITRA R, LETTICA GV, TUMIOTTO G, BELLANTI G, BOSI S, CASALI G, MONDUCCI I, BARONE A, PARENTI F, HEYMAN J, COZZI E, BALDACCI G, BACCOS D, BRIGHI F, DESANCTIS A, BOCK R, ROSSI F, AMATI P, SEMPRINI P, NARDELLI A, BOTTERO G, VARTOLO C, MILAZZOTTO F, DICROCE G, DIMARIO F, ANGRISANI G, AZZOLINI P, NEJA CP, MANZOLI U, ROSSI E, TRANI C, MASINI V, SEBASTIANI F, TOPAI M, BORGIA MC, LUCIANI C, FERRI F, DEPAOLA D, CAPURSO S, TUGNOLI F, VETTA C, ALTIERI T, BORZI M, VISCOMI A, STRIANO U, SALITURI S, ZONZIN P, FIORENCIS R, BADIN A, RAVERA B, BALDI C, SILVESTRI F, ALLEMANO P, REYNAUD S, SANSON A, MILANI L, DESIMONE MV, RUSSO A, VILLELLA A, GRAZINI M, AMIDEI S, ANSELMI L, PICCANICOLINO R, MASCELLI G, TAGLIAMONTE A, MESSINA V, TEDESCHI C, BOSSI M, BISIOLI M, TACCHI G, PAGNI G, VIVALDI F, IBBA GV, SANNIA L, PEDRAZZINI F, BAGNI E, FABII S, ALVINO A, ANTONIELLI E, DORONZO B, MARTINENGO E, BECCHI G, SALMOIRAGHI A, DIGIOVANNA F, CARAMANNO G, CAPORICCI D, BRUN M, GIANI P, FERRARIO G, PECI P, RONCONI G, SKOUSE D, GIUSTINIANI S, CUCCHI GF, TAVASCI E, SILVERII A, MARCELLINI G, SPECA G, STANISCIA D, CIMINO A, SERAFINI N, DEBONIS P, CERRUTI P, BAZZUCCHI M, DALPRA F, SPEROTTO C, MOLE GD, BARBANO G, POMARI F, GASCHINO G, PARIGI A, GANDOLFO N, RONDONI F, BRUSCA A, DILEO M, GOLZIO PG, ABRATE M, SCLAVO MG, ROCCI R, POGGIO G, GIANI S, CUZZUCREA D, BRASCHI GB, SCIACCA R, SAMMARTANO A, FURLANELLO F, BRAITO G, CUZZATO V, TOTIS O, FAURETTO F, LEO F, GALATI A, PALMA P, CAMERINI F, MORGERA T, BARBIERI L, SLAVICK GA, FRESCO C, CUDA A, SARNICOLA P, ARZILLO P, BINAGHI G, MACCHI G, CALVERI G, DIMARCO G, LEVANTESI G, PANERAI C, CATURELLI G, FACCHIN L, SARTORE G, ZARDINI P, MARINO P, CARBONIERI E, NAVA S, MAZZINI C, NAVA R, SERRA N, SASSARA M, NICROSINI F, GANDOLFI P, BERGOGNONI G, BALLESTRA AM, and VIOLO C

Subjects
cardiovascular diseases
Abstract

Background. Current knowledge of risk assessment in survivors of myocardial infarction is largely based on data gathered before the advent of thrombolysis. It must be determined whether and to what extent available information and proposed criteria of prognostication are applicable in the thrombolytic era. Methods and Results. We reassessed risk prediction in the 10 219 survivors of myocardial infarction with follow-up data available (ie, 98% of the total) who had been enrolled in the GISSI-2 trial, relying on a set of prespecified variables. The 3.5% 6-month all-cause mortality rate of these patients compared with the higher value of 4.6% found in the corresponding GISSI-1 cohort, originally allocated to streptokinase therapy, indicates a 24% reduction in postdischarge 6-month mortality. On multivariate analysis (Cox model), the following variables were predictors of 6-month all-cause mortality: ineligibility for exercise test for both cardiac (relative risk [RR], 3.30; 95% confidence interval [CI], 2.36-4.62) and noncardiac reasons (RR, 3.28; 95% CI, 2.23-4.72), early left ventricular failure (RR, 2.41; 95% Cl, 1.87-3.09), echocardiographic evidence of recovery phase left ventricular dysfunction (RR, 2.30; 95% CI, 1.78-2.98), advanced (more than 70 years) age (RR, 1.81; 95% Cl, 1.43 -2.30), electrical instability (ie, frequent and/or complex ventricular arrhythmias) (RR, 1.70; 95% Cl, 1.32-2.19), late left ventricular failure (RR, 1.54; 95% Cl, 1.17-2.03), previous myocardial infarction (RR, 1.47; 95% CI, 1.14-1.89), and a history of treated hypertension (RR, 1.32; 95% Cl, 1.05-1.65). Early post-myocardial infarction angina, a positive exercise test, female sex, history of angina, history of insulin-dependent diabetes, and anterior site of myocardial infarction were not risk predictors. On further multivariate analysis, performed on 8315 patients with the echocardiographic indicator of left ventricular dysfunction available, only previous myocardial infarction was not retained as an independent risk predictor. Conclusions. A decline in 6-month mortality of myocardial infarction survivors, seen within 6 hours of symptom onset, has been observed in recent years. Ineligibility for exercise test, early left ventricular failure, and recovery-phase left ventricular dysfunction are the most powerful (RR, >2) predictors of 6-month mortality among patients recovering from myocardial infarction after thrombolysis. Qualitative variables reflecting residual myocardial ischemia do not appear to be risk predictors. The lack of an independent adverse influence of early post-myocardial infarction angina on 6-month survival represents a major difference between this study and those of the prethrombolytic era.

33. Outlier and novelty detection for Functional data: a semi-parametric Bayesian approach

Author

Francesco Denti, Andrea Cappozzo, Francesca Greselin, Ingrassia, S, Punzo, A, Rocci, R, Denti, F, Cappozzo, A, and Greselin, F

Subjects
Bayesian mixture model, Dirichlet Process Mixture Model Functional data, Minimum Regularized Covariance Determinant, SECS-S/01 - STATISTICA
Abstract

Given a sample of unlabeled observations, the goal of a novelty detection method is to identify which units substantially deviate from the observed labeled patterns. Therefore, in a model-based framework, it is firstly of paramount importance to learn the components that correspond to the manifest groups in the training set. Secondly, one needs to take into account the lack of knowledge regarding the statistical novelties. Thirdly, contaminated elements in the known classes could greatly jeopardize the identification of new groups. Motivated by these challenges, we propose a two-stage Bayesian non-parametric novelty detector. At stage one, robust estimates are extracted from the training set and, subsequently, such information is employed to elicit informative priors within a flexible semiparametric mixture. This general paradigm can be easily adapted to complex modeling frameworks: we provide here an application to functional data from a food authenticity study.

Published
2021

34. A parsimonious parameterization of a nonnegative correlation matrix

Author

Cavicchia, Carlo, Vichi, Maurizio, Zaccaria, Giorgia, Ingrassia, S, Punzo, A, Rocci, R, Cavicchia, C, Vichi, M, and Zaccaria, G

Subjects
Ultrametric correlation matrix, Partitioned matrix, Parameterization of a correlation matrix, ultrametric correlation matrix, parameterization of a correlation matrix, nonnegative correlation matrix, partitioned matrix, Nonnegative correlation matrix
Abstract

Hierarchical relationships among manifest variables can be detected by analyzing their correlation matrix. To pinpoint the hierarchy underlying a multidimensional phenomenon, the Ultrametric Correlation Model (UCM) has been proposed with the aim of reconstructing a nonnegative correlation matrix via an ultrametric one. In this paper, we illustrate the mathematical advantages that a simple structure induced by the ultrametric property entails for the estimation of the UCM parameters in a maximum likelihood framework.

Published
2021

35. Factor Uniqueness of the Structural Parafac Model

Author

Giuseppe Bove, Roberto Rocci, Paolo Giordani, Giordani, P., Rocci, R., and Bove, G.

Subjects
Principal Component Analysis, factor uniqueness property, maximum likelihood, three-way factor analysis, Theory and Methods, Psychometrics, Covariance matrix, Applied Mathematics, Structure (category theory), 010103 numerical & computational mathematics, Extension (predicate logic), Scale invariance, Covariance, 01 natural sciences, 010104 statistics & probability, Research Design, Principal component analysis, Applied mathematics, Uniqueness, 0101 mathematics, Set (psychology), Factor Analysis, Statistical, General Psychology, Mathematics
Abstract

Factor analysis is a well-known method for describing the covariance structure among a set of manifest variables through a limited number of unobserved factors. When the observed variables are collected at various occasions on the same statistical units, the data have a three-way structure and standard factor analysis may fail. To overcome these limitations, three-way models, such as the Parafac model, can be adopted. It is often seen as an extension of principal component analysis able to discover unique latent components. The structural version, i.e., as a reparameterization of the covariance matrix, has been also formulated but rarely investigated. In this article, such a formulation is studied by discussing under what conditions factor uniqueness is preserved. It is shown that, under mild conditions, such a property holds even if the specific factors are assumed to be within-variable, or within-occasion, correlated and the model is modified to become scale invariant.

Published
2020

36. Handbook of Cluster Analysis

Author

Sébastien DEJEAN, Geoffrey McLachlan, Agustin Mayo-Iscar, Christian Hirsch, Joshua Knowles, Ivo Düntsch, Marco Alfo', Boris Mirkin, Hennig C, Meila M, Murtagh F, and Rocci R

Subjects
Computer science, media_common.quotation_subject, 02 engineering and technology, Mixture model, Disease cluster, 01 natural sciences, Data science, Field (computer science), Nonparametric density estimation, Visualization, ComputingMilieux_GENERAL, 010104 statistics & probability, ComputingMethodologies_PATTERNRECOGNITION, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, 0202 electrical engineering, electronic engineering, information engineering, Cluster Analysis, 020201 artificial intelligence & image processing, Quality (business), 0101 mathematics, Analysis tools, Cluster analysis, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), media_common
Abstract

Handbook of Cluster Analysis provides a comprehensive and unified account of the main research developments in cluster analysis. Written by active, distinguished researchers in this area, the book helps readers make informed choices of the most suitable clustering approach for their problem and make better use of existing cluster analysis tools. The book is organized according to the traditional core approaches to cluster analysis, from the origins to recent developments. After an overview of approaches and a quick journey through the history of cluster analysis, the book focuses on the four major approaches to cluster analysis. These approaches include methods for optimizing an objective function that describes how well data is grouped around centroids, dissimilarity-based methods, mixture models and partitioning models, and clustering methods inspired by nonparametric density estimation. The book also describes additional approaches to cluster analysis, including constrained and semi-supervised clustering, and explores other relevant issues, such as evaluating the quality of a cluster. This handbook is accessible to readers from various disciplines, reflecting the interdisciplinary nature of cluster analysis. For those already experienced with cluster analysis, the book offers a broad and structured overview. For newcomers to the field, it presents an introduction to key issues. For researchers who are temporarily or marginally involved with cluster analysis problems, the book gives enough algorithmic and practical details to facilitate working knowledge of specific clustering areas.

Published
2015
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37. Latent Class Models for Categorical Data

Author

Celeux, Gilles, Govaert, Gérard, Model selection in statistical learning (SELECT), Laboratoire de Mathématiques d'Orsay (LMO), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Heuristique et Diagnostic des Systèmes Complexes [Compiègne] (Heudiasyc), Université de Technologie de Compiègne (UTC)-Centre National de la Recherche Scientifique (CNRS), Hennig, C., Meila, M., Murthag, F, Rocci, R., Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Mathématiques d'Orsay (LMO), and Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2015

38. Some Notes on the Applicability of Cluster-Weighted Modeling in Effectiveness Studies

Author

MINOTTI, SIMONA CATERINA, Ingrassia, S, Rocci, R, Vichi, M, and Minotti, S

Subjects
SECS-S/01 - STATISTICA, Multilevel Models, Cluster-Weighted Modeling, Effectiveness
Abstract

In the nineties, numerous authors proposed the use of Multilevel Models in effectiveness studies. However, this approach has been strongly criticized. Cluster-Weighted Modeling (CWM) is a flexible statistical framework, which is based on weighted combinations of local models. While Multilevel Models provide rankings of the institutions, in the CWM approach many models of effectiveness are estimated, each of them being valid for a certain subpopulation of users

Published
2011

39. Impact evaluation of job training programs by a latent variable model

Author

Francesco Bartolucci, Fulvia Pennoni, Ingrassia, S, Rocci, R, Vichi, M, Bartolucci, F, Pennoni, F, and Salvatore Ingrassia, S

Subjects
Engineering, business.industry, Impact evaluation, Latent variable, Logistic regression, Latent class model, Unobserved heterogeneity, SECS-S/01 - STATISTICA, Expectation–maximization algorithm, Econometrics, causal inference, business, Latent variable model, Categorical variable, State dependence, Panel data
Abstract

We introduce a model for categorical panel data which is tailored to the dynamic evaluation of the impact of job training programs. The model may be seen as an extension of the dynamic logit model in which unobserved heterogeneity between subjects is taken into account by the introduction of a discrete latent variable. For the estimation of the model parameters we use an EM algorithm and we compute standard errors on the basis of the numerical derivative of the score vector of the complete data log-likelihood. The approach is illustrated through the analysis of a dataset containing the work histories of the employees of the private firms of the province of Milan between 2003 and 2005, some of whom attended job training programs supported by the European Social Fund.

Published
2011

40. Analysis of Skew-Symmetry in Proximity Data

Author

Giuseppe Bove, Bove, Giuseppe, and INGRASSIA, S., ROCCI, R., VICHI, M. EDS.

Subjects
Theoretical computer science, Software, Computer science, business.industry, Simple (abstract algebra), Spatial model, Skew, Multidimensional scaling, business, Computer Science::Databases, Symmetry (physics)
Abstract

Skew-symmetric data matrices can be represented in graphical displays in different ways. Some simple procedures that can be easily performed by standard software will be proposed and applied in this communication. Other methods based on spatial models that need ad hoc computational programs will be also reviewed emphasizing advantages and disadvantages in their applications.

Published
2011
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41. Conditional classification trees by weighting the Gini impurity measure

Author

Valerio A. Tutore, Antonio D’Ambrosio, Ingrassia, S., Rocci R., Vichi, M., D'Ambrosio, Antonio, and Tutore, V. A.

Subjects
Index (economics), Sample (statistics), Classification tree, Measure (mathematics), Three-way data, Weighting, Set (abstract data type), Variable (computer science), Condensed Matter::Superconductivity, Statistics, Condensed Matter::Strongly Correlated Electrons, Tree (set theory), Predictability, Mathematics, Conditional impurity
Abstract

This paper introduces the concept of the conditional impurity in the framework of tree-based models in order to deal with the analysis of three-way data, where a response variable and a set of predictors are measured on a sample of objects in different occasions. The conditional impurity in the definition of splitting criterion is defined as a classical impurity measure weighted by a predictability index.

Published
2011

42. Control Sample, Association and Causality

Author

Riccardo Borgoni, Donata Marasini, Piero Quatto, Ingrassia, S, Rocci, R, Vichi, M, Borgoni, R, Marasini, D, and Quatto, P

Subjects
Optimal allocation, Risk difference, Group (mathematics), Control sample, Association (object-oriented programming), Absolute risk reduction, Survey sampling, Target population, Causality, Bias of an estimator, Statistics, Exchangeability, SECS-S/01 - STATISTICA, Consistency, Mathematics
Abstract

We introduce the control sample in survey sampling as a tool for measuring the association between a possible cause X and a particular effect Y. The elements of the target population are divided in two groups according to whether X is present or not, the absence of X identifies the control group. A random sample is selected from each group with the aim of measuring the association between X and Y. We propose an unbiased estimator of the associational risk difference between groups and then generalize our approach to the problem of estimating the causal risk difference.

Published
2011

43. Clustering Spatial Functional Data: A Method Based on a Nonparametric Variogram Estimation

Author

Elvira Romano, Valentina Cozza, Rosanna Verde, vari, Ingrassia, S., Rocci, R., Vichi, M., Romano, Elvira, Verde, Rosanna, and Cozza, V.

Subjects
Estimation, Nonparametric statistics, Variogram function, Estimator, Spatial Functional Data, Function (mathematics), computer.software_genre, Clustering, Physics::Geophysics, Kernel (statistics), Statistics::Methodology, Data mining, Variogram, Cluster analysis, computer, Mathematics, TRACE (psycholinguistics)
Abstract

"\"In this paper we propose an extended version of a model-based strategy for clustering spatial functional data. The strategy, we refer, aims simultaneously to classify spatially dependent curves and to obtain a spatial functional model prototype for each cluster. The fit of these models implies to estimate a variogram function, the trace variogram function. Our proposal is to introduce an alternative estimator for the trace-variogram function: a kernel variogram estimator. This works better to adapt spatial varying features of the functional data pattern. Experimental comparisons show this approach has some advantages over the previous one. \""

Published
2011
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44. Using Poset Theory to Compare Fuzzy Multidimensional Material Deprivation Across Regions

Author

Rainer Brüggemann, Jan W. Owsiński, Marco Fattore, Ingrassia S, Rocci R, Vichi M, Fattore, M, Brueggemann, R, and Owsinski, J

Subjects
Relational structure, computer.software_genre, Fuzzy logic, GeneralLiterature_MISCELLANEOUS, SECS-S/03 - STATISTICA ECONOMICA, Partial order theory, Linear extension, Feature (machine learning), Data mining, Partial order, multidimensional poverty, material deprivation, Hasse diagrams, evaluation, Partially ordered set, computer, Scaling, Membership function, Mathematics
Abstract

In this paper, a new approach to the fuzzy analysis of multidimensional material deprivation data is provided, based on partial order theory. The main feature of the methodology is that the information needed for the deprivation assessment is extracted directly from the relational structure of the dataset, avoiding any kind of scaling and aggregation procedure, so as to respect the ordinal nature of the data. An example based on real data is worked out, pertaining to material deprivation in Italy for the year 2004.

Published
2011
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45. Comparing air quality indices aggregated by pollutant

Author

Antonella Plaia, Mariantonietta Ruggieri, Ingrassia, S, Rocci, R, Vichi, M, Ruggieri, M, and Plaia, A

Subjects
Functional principal component analysis, Pollutant, Human health, Index (economics), Aggregate (data warehouse), Statistics, Air pollution, medicine, Environmental science, Aggregate AQI, standardizing transformation, Relative Risk, relative variability, medicine.disease_cause, Air quality index, Health impact assessment
Abstract

In this paper a new aggregate Air Quality Index (AQI) useful for describing the global air pollution situation for a given area is proposed. The index, unlike most of currently used AQIs, takes into account the combined effects of all the considered pollutants to human health. Its good performance, tested by means of a simulation plan, is confirmed by a comparison with two other indices proposed in the literature, one of which is based on the Relative Risk of daily mortality, considering an application to real data.

Published
2011

46. Probabilistic Forecast for Northern New Zealand Seismic Process Based on a Forward Predictive Kernel Estimator

Author

Marcello Chiodi, Giada Adelfio, Ingrassia, S, Rocci, R, Vichi,M, Adelfio, G, and Chiodi, M

Subjects
Earthquake prediction, Probabilistic logic, Estimator, Geodesy, Physics::Geophysics, Latitude, Geography, Kernel (statistics), Kernel smoother, Space-time intensity function, kernel smoothing, earthquakes forecast, Settore SECS-S/01 - Statistica, Longitude, Seismology, Smoothing
Abstract

In seismology predictive properties of the estimated intensity function are often pursued. For this purpose, we propose an estimation procedure in time, longitude, latitude and depth domains, based on the subsequent increments of likelihood obtained adding an observation one at a time. On the basis of this estimation approach a forecast of earthquakes of a given area of Northern New Zealand is provided, assuming that future earthquakes activity may be based on the smoothing of past earthquakes.

Published
2011
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47. Visualizing and Exploring High Frequency Financial Data: Beanplot Time Series

Author

Germana Scepi, Carlo Drago, Ingrassia S., Rocci R., Vichi M., Drago, Carlo, and Scepi, Germana

Subjects
Finance, Transaction price, business.industry, Computer science, Specific time, Kernel density estimation, HIGH FREQUENCY DATA, VISUALITATION, BEAN PLOT, Data mining, business, computer.software_genre, computer
Abstract

In this paper we deal with the problem of visualizing and exploring specific time series such as high-frequency financial data. These data present unique features, absent in classical time series, which involve the necessity of searching and analysing an aggregate behaviour. Therefore, we define peculiar aggregated time series called beanplot time series. We show the advantages of using them instead of scalar time series when the data have a complex structure. Furthermore, we underline the interpretative proprieties of beanplot time series by comparing different types of aggregated time series. In particular, with simulated and real examples, we illustrate the different statistical performances of beanplot time series respect to boxplot time series.

Published
2011
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48. Satisfaction, loyalty and WOM, in dental care sector

Author

Paolo Mariani, Emma Zavarrone, Ingrassia, S, Rocci, R, Vichi, M, Mariani, P, and Zavarrone, M

Subjects
media_common.quotation_subject, Applied psychology, Satisfaction, loyalty, WOM, Dental care, Brand loyalty, Loyalty business model, SERVQUAL, Patient satisfaction, SECS-S/03 - STATISTICA ECONOMICA, Loyalty, Sociology, Marketing, Consumer behaviour, media_common
Abstract

We propose two different measures of the dental care industry: (a) BALG matrix, a new instrument to measure patient loyalty and its extent; (b) SERVQUAL based approach to measure patient satisfaction. Further investigation concerns the link between patient satisfaction and loyalty. The results prove that patient loyalty in the dental care industry is similar to consumer behaviour in all the other B2B and B2C services and furthermore, the results highlight low dependency of patient satisfaction on loyalty We propose two differentmeasures of the dental care industry: (a) BALG matrix, a new instrument to measure patient loyalty and its extent; (b) SERVQUAL based approach to measure patient satisfaction. Further investigation concerns the link between patient satisfaction and loyalty. The results prove that patient loyalty in the dental care industry is similar to consumer behaviour in all the other B2B and B2C services and furthermore, the results highlight low dependency of patient satisfaction on loyalty. © Springer-Verlag Berlin Heidelberg 2011.

Published
2011

49. Controlled calibration in presence of clustered measures

Author

Silvia Salini, Nadia Solaro, Ingrassia, S, Rocci, R, Vichi, M, Salini, S, and Solaro, N

Subjects
classical calibration estimator, multilevel models, random slopes, Calibration (statistics), Computer science, Order (business), SECS-S/01 - STATISTICA, Estimator, Context (language use), Data mining, computer.software_genre, classical calibration estimator, multilevel calibration estimator, multilevel model, Oman’s estimators, Building industry, computer
Abstract

In the context of statistical controlled calibration we introduce the ‘multilevel calibration estimator’ in order to account for clustered measurements. To tackle this issue more closely, results from a simulation study will be extensively discussed. Finally, an application from a building industry will be presented.

Published
2011

50. A Dynamic Analysis of Stock Markets through Multivariate Latent Markov Models

Author

Michele Costa, Luca De Angelis, INGRASSIA, S., ROCCI, R., VICHI, M., M. Costa, and L. De Angelis

Subjects
Computer science, Variable-order Markov model, LATENT MARKOV MODELS, Financial market, TRANSITION PROBABILITIES, Markov model, Stock market index, CLASSIFICATION, Latent class model, Investment decisions, Econometrics, Stock market, STOCK MARKET REGIMES, STOCK INDEX NUMBER, Modern portfolio theory
Abstract

A correct classification of financial products represents the essential and required step for achieving optimal investment decisions. The first goal in portfolio analysis should be the allocation of each asset into a class which groups investment opportunities characterized by a homogenous risk-return profile. Furthermore, the second goal should be the assessment of the stability of the classes composition. In this paper we address both objectives by means of the latent Markov models, which allow us to investigate the dynamic pattern of financial time series through an innovative framework. First, we propose to exploit the potential of latent Markov models in order to achieve latent classes able to group stocks with a similar risk-return profiles. Second, we interpret the transition probabilities estimated within latent Markov models as the probabilities of switching between the well-known states of financial markets: the upward trend, the downward trend and the lateral phases. Our results allow us both to discriminate the stock’s performance following a powerful classification approach and to assess the stock’s dynamics by predicting which state is going to experience next.

Published
2011
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