Asymptotic Theory for Extended Asymmetric Multivariate GARCH Processes

The paper considers various extended asymmetric multivariate conditional volatility models, and derives appropriate regularity conditions and associated asymptotic theory. This enables checking of internal consistency and allows valid statistical inferences to be drawn based on empirical estimation. For this purpose, we use an underlying vector random coefficient autoregressive process, for which we show the equivalent representation for the asymmetric multivariate conditional volatility model, to derive asymptotic theory for the quasi-maximum likelihood estimator. As an extension, we develop a new multivariate asymmetric long memory volatility model, and discuss the associated asymptotic properties.

Realized Matrix-Exponential Stochastic Volatility with Asymmetry, Long Memory and Spillovers

The paper develops a novel realized matrix-exponential stochastic volatility model of multivariate returns and realized covariances that incorporates asymmetry and long memory (hereafter the RMESV-ALM model). The matrix exponential transformation guarantees the positivedefiniteness of the dynamic covariance matrix. The contribution of the paper ties in with Robert Basmann’s seminal work in terms of the estimation of highly non-linear model specifications (“Causality tests and observationally equivalent representations of econometric models”, Journal of Econometrics, 1988, 39(1-2), 69–104), especially for developing tests for leverage and spillover effects in the covariance dynamics. Efficient importance sampling is used to maximize the likelihood function of RMESV-ALM, and the finite sample properties of the quasi-maximum likelihood estimator of the parameters are analysed. Using high frequency data for three US financial assets, the new model is estimated and evaluated. The forecasting performance of the new model is compared with a novel dynamic realized matrix-exponential conditional covariance model. The volatility and co-volatility spillovers are examined via the news impact curves and the impulse response functions from returns to volatility and co-volatility.

Análise comparativa e teste empírico da validade dos modelos CAPM tradicional e condicional: o caso das ações da Petrobrás

ALVES, Janaína da Silva. Análise comparativa e teste empírico da validade dos modelos CAPM tradicional e condicional: o caso das ações da Petrobrás. Revista Ciências Administrativas, Fotaleza, v. 13, n. 1, p.147-157, ago. 2007.

Méthodes de Bootstrap pour les modèles à facteurs

Cette thèse développe des méthodes bootstrap pour les modèles à facteurs qui sont couram- ment utilisés pour générer des prévisions depuis l'article pionnier de Stock et Watson (2002) sur les indices de diffusion. Ces modèles tolèrent l'inclusion d'un grand nombre de variables macroéconomiques et financières comme prédicteurs, une caractéristique utile pour inclure di- verses informations disponibles aux agents économiques. Ma thèse propose donc des outils éco- nométriques qui améliorent l'inférence dans les modèles à facteurs utilisant des facteurs latents extraits d'un large panel de prédicteurs observés. Il est subdivisé en trois chapitres complémen- taires dont les deux premiers en collaboration avec Sílvia Gonçalves et Benoit Perron. Dans le premier article, nous étudions comment les méthodes bootstrap peuvent être utilisées pour faire de l'inférence dans les modèles de prévision pour un horizon de h périodes dans le futur. Pour ce faire, il examine l'inférence bootstrap dans un contexte de régression augmentée de facteurs où les erreurs pourraient être autocorrélées. Il généralise les résultats de Gonçalves et Perron (2014) et propose puis justifie deux approches basées sur les résidus : le block wild bootstrap et le dependent wild bootstrap. Nos simulations montrent une amélioration des taux de couverture des intervalles de confiance des coefficients estimés en utilisant ces approches comparativement à la théorie asymptotique et au wild bootstrap en présence de corrélation sérielle dans les erreurs de régression. Le deuxième chapitre propose des méthodes bootstrap pour la construction des intervalles de prévision permettant de relâcher l'hypothèse de normalité des innovations. Nous y propo- sons des intervalles de prédiction bootstrap pour une observation h périodes dans le futur et sa moyenne conditionnelle. Nous supposons que ces prévisions sont faites en utilisant un ensemble de facteurs extraits d'un large panel de variables. Parce que nous traitons ces facteurs comme latents, nos prévisions dépendent à la fois des facteurs estimés et les coefficients de régres- sion estimés. Sous des conditions de régularité, Bai et Ng (2006) ont proposé la construction d'intervalles asymptotiques sous l'hypothèse de Gaussianité des innovations. Le bootstrap nous permet de relâcher cette hypothèse et de construire des intervalles de prédiction valides sous des hypothèses plus générales. En outre, même en supposant la Gaussianité, le bootstrap conduit à des intervalles plus précis dans les cas où la dimension transversale est relativement faible car il prend en considération le biais de l'estimateur des moindres carrés ordinaires comme le montre une étude récente de Gonçalves et Perron (2014). Dans le troisième chapitre, nous suggérons des procédures de sélection convergentes pour les regressions augmentées de facteurs en échantillons finis. Nous démontrons premièrement que la méthode de validation croisée usuelle est non-convergente mais que sa généralisation, la validation croisée «leave-d-out» sélectionne le plus petit ensemble de facteurs estimés pour l'espace généré par les vraies facteurs. Le deuxième critère dont nous montrons également la validité généralise l'approximation bootstrap de Shao (1996) pour les regressions augmentées de facteurs. Les simulations montrent une amélioration de la probabilité de sélectionner par- cimonieusement les facteurs estimés comparativement aux méthodes de sélection disponibles. L'application empirique revisite la relation entre les facteurs macroéconomiques et financiers, et l'excès de rendement sur le marché boursier américain. Parmi les facteurs estimés à partir d'un large panel de données macroéconomiques et financières des États Unis, les facteurs fortement correlés aux écarts de taux d'intérêt et les facteurs de Fama-French ont un bon pouvoir prédictif pour les excès de rendement.

Análise comparativa e teste empírico da validade dos modelos CAPM tradicional e condicional: o caso das ações da Petrobrás

ALVES, Janaína da Silva. Análise comparativa e teste empírico da validade dos modelos CAPM tradicional e condicional: o caso das ações da Petrobrás. Revista Ciências Administrativas, Fotaleza, v. 13, n. 1, p.147-157, ago. 2007.

TRIB2 in normal and malignant hematopoiesis - a transcription factor network

Hematopoiesis is the tightly controlled and complex process in which the entire blood system is formed and maintained by a rare pool of hematopoietic stem cells (HSCs), and its dysregulation results in the formation of leukaemia. TRIB2, a member of the Tribbles family of serine/threonine pseudokinases, has been implicated in a variety of cancers and is a potent murine oncogene that induces acute myeloid leukaemia (AML) in vivo via modulation of the essential myeloid transcription factor CCAAT-enhancer binding protein α (C/EBPα). C/EBPα, which is crucial for myeloid cell differentiation, is commonly dysregulated in a variety of cancers, including AML. Two isoforms of C/EBPα exist - the full-length p42 isoform, and the truncated oncogenic p30 isoform. TRIB2 has been shown to selectively degrade the p42 isoform of C/EBPα and induce p30 expression in AML. In this study, overexpression of the p30 isoform in a bone marrow transplant (BMT) leads to perturbation of myelopoiesis, and in the presence of physiological levels of p42, this oncogene exhibited weak transformative ability. It was also shown by BMT that despite their degradative relationship, expression of C/EBPα was essential for TRIB2 mediated leukaemia. A conditional mouse model was used to demonstrate that oncogenic p30 cooperates with TRIB2 to reduce disease latency, only in the presence of p42. At the molecular level, a ubiquitination assay was used to show that TRIB2 degrades p42 by K48-mediated proteasomal ubiquitination and was unable to ubiquitinate p30. Mutation of a critical lysine residue in the C-terminus of C/EBPα abrogated TRIB2 mediated C/EBPα ubiquitination suggesting that this site, which is frequently mutated in AML, is the site at which TRIB2 mediates its degradative effects. The TRIB2-C/EBPα axis was effectively targeted by proteasome inhibition. AML is a very difficult disease to target therapeutically due to the extensive array of chromosomal translocations and genetic aberrations that contribute to the disease. The cell from which a specific leukaemia arises, or leukaemia initiating cell (LIC), can affect the phenotype and chemotherapeutic response of the resultant disease. The LIC has been elucidated for some common oncogenes but it is unknown for TRIB2. The data presented in this thesis investigate the ability of the oncogene TRIB2 to transform hematopoietic stem and progenitor cells in vitro and in vivo. TRIB2 overexpression conferred in vitro serially replating ability to all stem and progenitor cells studied. Upon transplantation, only TRIB2 overexpressing HSCs and granulocyte/macrophage progenitors (GMPs) resulted in the generation of leukaemia in vivo. TRIB2 induced a mature myeloid leukaemia from the GMP, and a mixed lineage leukaemia from the HSC. As such the role of TRIB2 in steady state hematopoiesis was also explored using a Trib2-/- mouse and it was determined that loss of Trib2 had no effect on lineage distribution in the hematopoietic compartment under steady-state conditions. The process of hematopoiesis is controlled by a host of lineage restricted transcription factors. Recently members of the Nuclear Factor 1 family of transcription factors (NFIA, NFIB, NFIC and NFIX) have been implicated in hematopoiesis. Little is known about the role of NFIX in lineage determination. Here we describe a novel role for NFIX in lineage fate determination. In human and murine datasets the expression of Nfix was shown to decrease as cells differentiated along the lymphoid pathway. NFIX overexpression resulted in enhanced myelopoiesis in vivo and in vitro and a block in B cell development at the pre-pro-B cell stage. Loss of NFIX resulted in disruption of myeloid and lymphoid differentiation in vivo. These effects on stem and progenitor cell fate correlated with changes in the expression levels of key transcription factors involved in hematopoietic differentiation including a 15-fold increase in Cebpa expression in Nfix overexpressing cells. The data presented support a role for NFIX as an important transcription factor influencing hematopoietic lineage specification. The identification of NFIX as a novel transcription factor influencing lineage determination will lead to further study of its role in hematopoiesis, and contribute to a better understanding of the process of differentiation. Elucidating the relationship between TRIB2 and C/EBPα not only impacts on our understanding of the pathophysiology of AML but is also relevant in other cancer types including lung and liver cancer. Thus in summary, the data presented in this thesis provide important insights into key areas which will facilitate the development of future therapeutic approaches in cancer treatment.

Méthodes de Bootstrap pour les modèles à facteurs

Cette thèse développe des méthodes bootstrap pour les modèles à facteurs qui sont couram- ment utilisés pour générer des prévisions depuis l'article pionnier de Stock et Watson (2002) sur les indices de diffusion. Ces modèles tolèrent l'inclusion d'un grand nombre de variables macroéconomiques et financières comme prédicteurs, une caractéristique utile pour inclure di- verses informations disponibles aux agents économiques. Ma thèse propose donc des outils éco- nométriques qui améliorent l'inférence dans les modèles à facteurs utilisant des facteurs latents extraits d'un large panel de prédicteurs observés. Il est subdivisé en trois chapitres complémen- taires dont les deux premiers en collaboration avec Sílvia Gonçalves et Benoit Perron. Dans le premier article, nous étudions comment les méthodes bootstrap peuvent être utilisées pour faire de l'inférence dans les modèles de prévision pour un horizon de h périodes dans le futur. Pour ce faire, il examine l'inférence bootstrap dans un contexte de régression augmentée de facteurs où les erreurs pourraient être autocorrélées. Il généralise les résultats de Gonçalves et Perron (2014) et propose puis justifie deux approches basées sur les résidus : le block wild bootstrap et le dependent wild bootstrap. Nos simulations montrent une amélioration des taux de couverture des intervalles de confiance des coefficients estimés en utilisant ces approches comparativement à la théorie asymptotique et au wild bootstrap en présence de corrélation sérielle dans les erreurs de régression. Le deuxième chapitre propose des méthodes bootstrap pour la construction des intervalles de prévision permettant de relâcher l'hypothèse de normalité des innovations. Nous y propo- sons des intervalles de prédiction bootstrap pour une observation h périodes dans le futur et sa moyenne conditionnelle. Nous supposons que ces prévisions sont faites en utilisant un ensemble de facteurs extraits d'un large panel de variables. Parce que nous traitons ces facteurs comme latents, nos prévisions dépendent à la fois des facteurs estimés et les coefficients de régres- sion estimés. Sous des conditions de régularité, Bai et Ng (2006) ont proposé la construction d'intervalles asymptotiques sous l'hypothèse de Gaussianité des innovations. Le bootstrap nous permet de relâcher cette hypothèse et de construire des intervalles de prédiction valides sous des hypothèses plus générales. En outre, même en supposant la Gaussianité, le bootstrap conduit à des intervalles plus précis dans les cas où la dimension transversale est relativement faible car il prend en considération le biais de l'estimateur des moindres carrés ordinaires comme le montre une étude récente de Gonçalves et Perron (2014). Dans le troisième chapitre, nous suggérons des procédures de sélection convergentes pour les regressions augmentées de facteurs en échantillons finis. Nous démontrons premièrement que la méthode de validation croisée usuelle est non-convergente mais que sa généralisation, la validation croisée «leave-d-out» sélectionne le plus petit ensemble de facteurs estimés pour l'espace généré par les vraies facteurs. Le deuxième critère dont nous montrons également la validité généralise l'approximation bootstrap de Shao (1996) pour les regressions augmentées de facteurs. Les simulations montrent une amélioration de la probabilité de sélectionner par- cimonieusement les facteurs estimés comparativement aux méthodes de sélection disponibles. L'application empirique revisite la relation entre les facteurs macroéconomiques et financiers, et l'excès de rendement sur le marché boursier américain. Parmi les facteurs estimés à partir d'un large panel de données macroéconomiques et financières des États Unis, les facteurs fortement correlés aux écarts de taux d'intérêt et les facteurs de Fama-French ont un bon pouvoir prédictif pour les excès de rendement.

Perceived Accessibility of Public Transport as a Potential Indicator of Social Inclusion

Perceived accessibility has been acknowledged as an important aspect of transport policy since the 70s. Nevertheless, very few empirical studies have been conducted in this field. When aiming to improve social inclusion, by making sus-tainable transport modes accessible to all, it is important to understand the factors driving perceived accessibility. Un-like conventional accessibility measures, perceived accessibility focuses on the perceived possibilities and ease of en-gaging in preferred activities using different transport modes. We define perceived accessibility in terms of how easy it is to live a satisfactory life with the help of the transport system, which is not necessarily the same thing as the objec-tive standard of the system. According to previous research, perceived accessibility varies with the subjectively-rated quality of the mode of transport. Thus, improvements in quality (e.g. trip planning, comfort, or safety) increase the per-ceived accessibility and make life easier to live using the chosen mode of transport. This study (n=750) focuses on the perceived accessibility of public transport, captured using the Perceived Accessibility Scale PAC (Lättman, Olsson, & Fri-man, 2015). More specifically, this study aims to determine how level of quality affects the perceived accessibility in public transport. A Conditional Process Model shows that, in addition to quality, feeling safe and frequency of travel are important predictors of perceived accessibility. Furthermore, elderly and those in their thirties report a lower level of perceived accessibility to their day-to-day activities using public transport. The basic premise of this study is that sub-jective experiences may be as important as objective indicators when planning and designing for socially inclusive transport systems.

Generare con transformer lineare spiegazioni di concept set medicali costituiti da termini correlati estratti da social post di pazienti

Le malattie rare pongono diversi scogli ai pazienti, ai loro familiari e ai sanitari. Uno fra questi è la mancanza di informazione che deriva dall'assenza di fonti sicure e semplici da consultare su aspetti dell'esperienza del paziente. Il lavoro presentato ha lo scopo di generare da set termini correlati semanticamente, delle frasi che abbiamo la capacità di spiegare il legame fra di essi e aggiungere informazioni utili e veritiere in un linguaggio semplice e comprensibile. Il problema affrontato oggigiorno non è ben documentato in letteratura e rappresenta una sfida interessante si per complessità che per mancanza di dataset per l'addestramento. Questo tipo di task, come altri di NLP, è affrontabile solo con modelli sempre più potenti ma che richiedono risorse sempre più elevate. Per questo motivo, è stato utilizzato il meccanismo di recente pubblicazione del Performer, dimostrando di riuscire a mantenere uno stesso grado di accuratezza e di qualità delle frasi prodotte, con una parallela riduzione delle risorse utilizzate. Ciò apre la strada all'utilizzo delle reti neurali più recenti anche senza avere i centri di calcolo delle multinazionali. Il modello proposto dunque è in grado di generare frasi che illustrano le relazioni semantiche di termini estratti da un mole di documenti testuali, permettendo di generare dei riassunti dell'informazione e della conoscenza estratta da essi e renderla facilmente accessibile e comprensibile al pazienti o a persone non esperte.

Timeliness, efficiency and public procurement in hip surgery in Italy

Considering different perspectives, the scope of this thesis is to investigate how to improve healthcare resources allocation and the provision efficiency for hip surgeries, a resource-intensive operation, among the most frequently performed on the elderly, with a trend in volume that is increasing in years due to population aging. Firstly, the effect of Time-To-Surgery (TTS) on mortality for hip fracture patients is investigated. The analysis attempts to account for TTS endogeneity due to the inability to fully control for variables affecting patient delay – e.g. patient severity. Exploiting an instrumental variable model, where being admitted on Friday or Saturday predicts longer TTS, findings show exogenous TTS does not have a significant effect on mortality. Thus suggesting surgeons prioritize patients effectively, neutralizing the adverse impact of longer TTS. Then, the volume-outcome relation for total hip replacement surgery is analyzed, seeking to account for selective referral, which may be present in elective surgery context, and induce reverse causality issue in the volume-outcome relation. The analysis employs a conditional choice model where patient travel distance from all regions' hospitals is used as a hospital choice predictor. Findings show the exogenous hospital volume significantly decreases adverse outcomes probability, especially in the short run. Finally, the change in public procurement design enforced in the Romagna LHA (Italy) is exploited to assess its impact on hip prostheses cost, surgeons' implant choice, and patient health outcomes. Hip prostheses are the major cost-driver of hip replacement surgeries, hence it is crucial to design the public tender such that implant prices are minimized, but cost-containment policies have to be weighted with patient well-being. Evidence shows that a cost reduction occurred without a significant surgeons’ choices impact. Positive or no effect of surgeons specialization is found on patients outcomes after the new procurement introduction.

A Multi-DAG Model for Real-Time Parallel Applications with Conditional Execution

The 30th ACM/SIGAPP Symposium On Applied Computing (SAC 2015). 13 to 17, Apr, 2015, Embedded Systems. Salamanca, Spain.

Can a Lucas model with habit generate realistic conditional volatility in exchange rate returns?

In this paper, we attempt to give a theoretical underpinning to the well established empirical stylized fact that asset returns in general and the spot FOREX returns in particular display predictable volatility characteristics. Adopting Moore and Roche s habit persistence version of Lucas model we nd that both the innovation in the spot FOREX return and the FOREX return itself follow "ARCH" style processes. Using the impulse response functions (IRFs) we show that the baseline simulated FOREX series has "ARCH" properties in the quarterly frequency that match well the "ARCH" properties of the empirical monthly estimations in that when we scale the x-axis to synchronize the monthly and quarterly responses we find similar impulse responses to one unit shock in variance. The IRFs for the ARCH processes we estimate "look the same" with an approximately monotonic decreasing fashion. The Lucas two-country monetary model with habit can generate realistic conditional volatility in spot FOREX return.

A transgenic model for conditional induction and rescue of portal hypertension reveals a role of VEGF-mediated regulation of sinusoidal fenestrations

Portal hypertension (PH) is a common complication and a leading cause of death in patients with chronic liver diseases. PH is underlined by structural and functional derangement of liver sinusoid vessels and its fenestrated endothelium. Because in most clinical settings PH is accompanied by parenchymal injury, it has been difficult to determine the precise role of microvascular perturbations in causing PH. Reasoning that Vascular Endothelial Growth Factor (VEGF) is required to maintain functional integrity of the hepatic microcirculation, we developed a transgenic mouse system for a liver-specific-, reversible VEGF inhibition. The system is based on conditional induction and de-induction of a VEGF decoy receptor that sequesters VEGF and preclude signaling. VEGF blockade results in sinusoidal endothelial cells (SECs) fenestrations closure and in accumulation and transformation of the normally quiescent hepatic stellate cells, i.e. provoking the two processes underlying sinusoidal capillarization. Importantly, sinusoidal capillarization was sufficient to cause PH and its typical sequela, ascites, splenomegaly and venous collateralization without inflicting parenchymal damage or fibrosis. Remarkably, these dramatic phenotypes were fully reversed within few days from lifting-off VEGF blockade and resultant re-opening of SECs' fenestrations. This study not only uncovered an indispensible role for VEGF in maintaining structure and function of mature SECs, but also highlights the vasculo-centric nature of PH pathogenesis. Unprecedented ability to rescue PH and its secondary manifestations via manipulating a single vascular factor may also be harnessed for examining the potential utility of de-capillarization treatment modalities.

Dephosphorylation of p-ERK1/2 in relation to tumor remission after HER-2 and Raf1 blocking therapy in a conditional mouse tumor model

Several studies have shown that HER-2/neu (erbB-2) blocking therapy strategies can cause tumor remission. However, the responsible molecular mechanisms are not yet known. Both ERK1/2 and Akt/PKB are critical for HER-2-mediated signal transduction. Therefore, we used a mouse tumor model that allows downregulation of HER-2 in tumor tissue by administration of anhydrotetracycline (ATc). Switching-off HER-2 caused a rapid tumor remission by more than 95% within 7 d of ATc administration compared to the volume before switching-off HER-2. Interestingly, HER-2 downregulation caused a dephosphorylation of p-ERK1/2 by more than 80% already before tumor remission occurred. Levels of total ERK protein were not influenced. In contrast, dephosphorylation of p-Akt occurred later, when the tumor was already in remission. These data suggest that in our HER-2 tumor model dephosphorylation of p-ERK1/2 may be more critical for tumor remission than dephosphorylation of p-Akt. To test this hypothesis we used a second mouse tumor model that allows ATc controlled expression of BXB-Raf1 because the latter constitutively signals to ERK1/2, but cannot activate Akt/PKB. As expected, downregulation of BXB-Raf1 in tumor tissue caused a strong dephosphorylation of p-ERK1/2, but did not decrease levels of p-Akt. Interestingly, tumor remission after switching-off BXB-Raf1 was similarly efficient as the effect of HER-2 downregulation, despite the lack of p-Akt dephosphorylation. In conclusion, two lines of evidence strongly suggest that dephosphorylation of p-ERK1/2 and not that of p-Akt is critical for the rapid tumor remission after downregulation of HER-2 or BXB-Raf1 in our tumor model: (i) dephosphorylation of p-ERK1/2 but not that of p-Akt precedes tumor remission after switching-off HER-2 and (ii) downregulation of BXB-Raf1 leads to a similarly efficient tumor remission as downregulation of HER-2, although no p-Akt dephosphorylation was observed after switching-off BXB-Raf1.

Bayesian and survival model for tumor relapse status and disease-specific survival, with applications to breast cancer

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women in the United States. Studies on ipsilateral breast tumor relapse (IBTR) status and disease-specific survival will help guide clinic treatment and predict patient prognosis.^ After breast conservation therapy, patients with breast cancer may experience breast tumor relapse. This relapse is classified into two distinct types: true local recurrence (TR) and new ipsilateral primary tumor (NP). However, the methods used to classify the relapse types are imperfect and are prone to misclassification. In addition, some observed survival data (e.g., time to relapse and time from relapse to death)are strongly correlated with relapse types. The first part of this dissertation presents a Bayesian approach to (1) modeling the potentially misclassified relapse status and the correlated survival information, (2) estimating the sensitivity and specificity of the diagnostic methods, and (3) quantify the covariate effects on event probabilities. A shared frailty was used to account for the within-subject correlation between survival times. The inference was conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in softwareWinBUGS. Simulation was used to validate the Bayesian method and assess its frequentist properties. The new model has two important innovations: (1) it utilizes the additional survival times correlated with the relapse status to improve the parameter estimation, and (2) it provides tools to address the correlation between the two diagnostic methods conditional to the true relapse types.^ Prediction of patients at highest risk for IBTR after local excision of ductal carcinoma in situ (DCIS) remains a clinical concern. The goals of the second part of this dissertation were to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center, to determine the risk of IBTR in patients with DCIS treated with local excision, and to determine whether there is a subset of patients at low risk of IBTR. Patients who had undergone local excision from 1990 through 2007 at MD Anderson Cancer Center with a final diagnosis of DCIS (n=794) were included in this part. Clinicopathologic factors and the performance of the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 patients with complete data. Nomogram for prediction of 5- and 10-year IBTR probabilities were found to demonstrate imperfect calibration and discrimination, with an area under the receiver operating characteristic curve of .63 and a concordance index of .63. In conclusion, predictive models for IBTR in DCIS patients treated with local excision are imperfect. Our current ability to accurately predict recurrence based on clinical parameters is limited.^ The American Joint Committee on Cancer (AJCC) staging of breast cancer is widely used to determine prognosis, yet survival within each AJCC stage shows wide variation and remains unpredictable. For the third part of this dissertation, biologic markers were hypothesized to be responsible for some of this variation, and the addition of biologic markers to current AJCC staging were examined for possibly provide improved prognostication. The initial cohort included patients treated with surgery as first intervention at MDACC from 1997 to 2006. Cox proportional hazards models were used to create prognostic scoring systems. AJCC pathologic staging parameters and biologic tumor markers were investigated to devise the scoring systems. Surveillance Epidemiology and End Results (SEER) data was used as the external cohort to validate the scoring systems. Binary indicators for pathologic stage (PS), estrogen receptor status (E), and tumor grade (G) were summed to create PS+EG scoring systems devised to predict 5-year patient outcomes. These scoring systems facilitated separation of the study population into more refined subgroups than the current AJCC staging system. The ability of the PS+EG score to stratify outcomes was confirmed in both internal and external validation cohorts. The current study proposes and validates a new staging system by incorporating tumor grade and ER status into current AJCC staging. We recommend that biologic markers be incorporating into revised versions of the AJCC staging system for patients receiving surgery as the first intervention.^ Chapter 1 focuses on developing a Bayesian method to solve misclassified relapse status and application to breast cancer data. Chapter 2 focuses on evaluation of a breast cancer nomogram for predicting risk of IBTR in patients with DCIS after local excision gives the statement of the problem in the clinical research. Chapter 3 focuses on validation of a novel staging system for disease-specific survival in patients with breast cancer treated with surgery as the first intervention. ^