Study of the sensitivity of the XENON1T experiment with the profile likelihood method

Oggi sappiamo che la materia ordinaria rappresenta solo una piccola parte dell'intero contenuto in massa dell'Universo. L'ipotesi dell'esistenza della Materia Oscura, un nuovo tipo di materia che interagisce solo gravitazionalmente e, forse, tramite la forza debole, è stata avvalorata da numerose evidenze su scala sia galattica che cosmologica. Gli sforzi rivolti alla ricerca delle cosiddette WIMPs (Weakly Interacting Massive Particles), il generico nome dato alle particelle di Materia Oscura, si sono moltiplicati nel corso degli ultimi anni. L'esperimento XENON1T, attualmente in costruzione presso i Laboratori Nazionali del Gran Sasso (LNGS) e che sarà in presa dati entro la fine del 2015, segnerà un significativo passo in avanti nella ricerca diretta di Materia Oscura, che si basa sulla rivelazione di collisioni elastiche su nuclei bersaglio. XENON1T rappresenta la fase attuale del progetto XENON, che ha già realizzato gli esperimenti XENON10 (2005) e XENON100 (2008 e tuttora in funzione) e che prevede anche un ulteriore sviluppo, chiamato XENONnT. Il rivelatore XENON1T sfrutta circa 3 tonnellate di xeno liquido (LXe) e si basa su una Time Projection Chamber (TPC) a doppia fase. Dettagliate simulazioni Monte Carlo della geometria del rivelatore, assieme a specifiche misure della radioattività dei materiali e stime della purezza dello xeno utilizzato, hanno permesso di predire con accuratezza il fondo atteso. In questo lavoro di tesi, presentiamo lo studio della sensibilità attesa per XENON1T effettuato tramite il metodo statistico chiamato Profile Likelihood (PL) Ratio, il quale nell'ambito di un approccio frequentista permette un'appropriata trattazione delle incertezze sistematiche. In un primo momento è stata stimata la sensibilità usando il metodo semplificato Likelihood Ratio che non tiene conto di alcuna sistematica. In questo modo si è potuto valutare l'impatto della principale incertezza sistematica per XENON1T, ovvero quella sulla emissione di luce di scintillazione dello xeno per rinculi nucleari di bassa energia. I risultati conclusivi ottenuti con il metodo PL indicano che XENON1T sarà in grado di migliorare significativamente gli attuali limiti di esclusione di WIMPs; la massima sensibilità raggiunge una sezione d'urto σ=1.2∙10-47 cm2 per una massa di WIMP di 50 GeV/c2 e per una esposizione nominale di 2 tonnellate∙anno. I risultati ottenuti sono in linea con l'ambizioso obiettivo di XENON1T di abbassare gli attuali limiti sulla sezione d'urto, σ, delle WIMPs di due ordini di grandezza. Con tali prestazioni, e considerando 1 tonnellata di LXe come massa fiduciale, XENON1T sarà in grado di superare gli attuali limiti (esperimento LUX, 2013) dopo soli 5 giorni di acquisizione dati.

A maximum likelihood approach for estimating growth from tag–recapture data

The Fabens method is commonly used to estimate growth parameters k and l infinity in the von Bertalanffy model from tag-recapture data. However, the Fabens method of estimation has an inherent bias when individual growth is variable. This paper presents an asymptotically unbiassed method using a maximum likelihood approach that takes account of individual variability in both maximum length and age-at-tagging. It is assumed that each individual's growth follows a von Bertalanffy curve with its own maximum length and age-at-tagging. The parameter k is assumed to be a constant to ensure that the mean growth follows a von Bertalanffy curve and to avoid overparameterization. Our method also makes more efficient use nf thp measurements at tno and recapture and includes diagnostic techniques for checking distributional assumptions. The method is reasonably robust and performs better than the Fabens method when individual growth differs from the von Bertalanffy relationship. When measurement error is negligible, the estimation involves maximizing the profile likelihood of one parameter only. The method is applied to tag-recapture data for the grooved tiger prawn (Penaeus semisulcatus) from the Gulf of Carpentaria, Australia.

Improved likelihood inference for the shape parameter in Weibull regression

We obtain adjustments to the profile likelihood function in Weibull regression models with and without censoring. Specifically, we consider two different modified profile likelihoods: (i) the one proposed by Cox and Reid [Cox, D.R. and Reid, N., 1987, Parameter orthogonality and approximate conditional inference. Journal of the Royal Statistical Society B, 49, 1-39.], and (ii) an approximation to the one proposed by Barndorff-Nielsen [Barndorff-Nielsen, O.E., 1983, On a formula for the distribution of the maximum likelihood estimator. Biometrika, 70, 343-365.], the approximation having been obtained using the results by Fraser and Reid [Fraser, D.A.S. and Reid, N., 1995, Ancillaries and third-order significance. Utilitas Mathematica, 47, 33-53.] and by Fraser et al. [Fraser, D.A.S., Reid, N. and Wu, J., 1999, A simple formula for tail probabilities for frequentist and Bayesian inference. Biometrika, 86, 655-661.]. We focus on point estimation and likelihood ratio tests on the shape parameter in the class of Weibull regression models. We derive some distributional properties of the different maximum likelihood estimators and likelihood ratio tests. The numerical evidence presented in the paper favors the approximation to Barndorff-Nielsen`s adjustment.

Skovgaard`s adjustment to likelihood ratio tests in exponential family nonlinear models

Likelihood ratio tests can be substantially size distorted in small- and moderate-sized samples. In this paper, we apply Skovgaard`s [Skovgaard, I.M., 2001. Likelihood asymptotics. Scandinavian journal of Statistics 28, 3-321] adjusted likelihood ratio statistic to exponential family nonlinear models. We show that the adjustment term has a simple compact form that can be easily implemented from standard statistical software. The adjusted statistic is approximately distributed as X(2) with high degree of accuracy. It is applicable in wide generality since it allows both the parameter of interest and the nuisance parameter to be vector-valued. Unlike the modified profile likelihood ratio statistic obtained from Cox and Reid [Cox, D.R., Reid, N., 1987. Parameter orthogonality and approximate conditional inference. journal of the Royal Statistical Society B49, 1-39], the adjusted statistic proposed here does not require an orthogonal parameterization. Numerical comparison of likelihood-based tests of varying dispersion favors the test we propose and a Bartlett-corrected version of the modified profile likelihood ratio test recently obtained by Cysneiros and Ferrari [Cysneiros, A.H.M.A., Ferrari, S.L.P., 2006. An improved likelihood ratio test for varying dispersion in exponential family nonlinear models. Statistics and Probability Letters 76 (3), 255-265]. (C) 2008 Elsevier B.V. All rights reserved.

A SURVEY OF THE LIKELIHOOD APPROACH TO BIOEQUIVALENCE TRIALS

Bioequivalence trials are abbreviated clinical trials whereby a generic drug or new formulation is evaluated to determine if it is "equivalent" to a corresponding previously approved brand-name drug or formulation. In this manuscript, we survey the process of testing bioequivalence and advocate the likelihood paradigm for representing the resulting data as evidence. We emphasize the unique conflicts between hypothesis testing and confidence intervals in this area - which we believe are indicative of the existence of the systemic defects in the frequentist approach - that the likelihood paradigm avoids. We suggest the direct use of profile likelihoods for evaluating bioequivalence and examine the main properties of profile likelihoods and estimated likelihoods under simulation. This simulation study shows that profile likelihoods are a reasonable alternative to the (unknown) true likelihood for a range of parameters commensurate with bioequivalence research. Our study also shows that the standard methods in the current practice of bioequivalence trials offers only weak evidence from the evidential point of view.

Statistical methods for ordered categorical data based on a constrained odds model

The proportional odds model provides a powerful tool for analysing ordered categorical data and setting sample size, although for many clinical trials its validity is questionable. The purpose of this paper is to present a new class of constrained odds models which includes the proportional odds model. The efficient score and Fisher's information are derived from the profile likelihood for the constrained odds model. These results are new even for the special case of proportional odds where the resulting statistics define the Mann-Whitney test. A strategy is described involving selecting one of these models in advance, requiring assumptions as strong as those underlying proportional odds, but allowing a choice of such models. The accuracy of the new procedure and its power are evaluated.

A comparison of the active surveillance of scrapie in the European Union

The abattoir and the fallen stock surveys constitute the active surveillance component aimed at improving the detection of scrapie across the European Union. Previous studies have suggested the occurrence of significant differences in the operation of the surveys across the EU. In the present study we assessed the standardisation of the surveys throughout time across the EU and identified clusters of countries with similar underlying characteristics allowing comparisons between them. In the absence of sufficient covariate information to explain the observed variability across countries, we modelled the unobserved heterogeneity by means of non-parametric distributions on the risk ratios of the fallen stock over the abattoir survey. More specifically, we used the profile likelihood method on 2003, 2004 and 2005 active surveillance data for 18 European countries on classical scrapie, and on 2004 and 2005 data for atypical scrapie separately. We extended our analyses to include the limited covariate information available, more specifically, the proportion of the adult sheep population sampled by the fallen stock survey every year. Our results show that the between-country heterogeneity dropped in 2004 and 2005 relative to that of 2003 for classical scrapie. As a consequence, the number of clusters in the last two years was also reduced indicating the gradual standardisation of the surveillance efforts across the EU. The crude analyses of the atypical data grouped all the countries in one cluster and showed non-significant gain in the detection of this type of scrapie by any of the two sources. The proportion of the population sampled by the fallen stock appeared significantly associated with our risk ratio for both types of scrapie, although in opposite directions: negative for classical and positive for atypical. The initial justification for the fallen stock, targeting a high-risk population to increase the likelihood of case finding, appears compromised for both types of scrapie in some countries.

A comparison of three different models for estimating relative risk in meta-analysis of clinical trials under unobserved heterogeneity

We focus on the comparison of three statistical models used to estimate the treatment effect in metaanalysis when individually pooled data are available. The models are two conventional models, namely a multi-level and a model based upon an approximate likelihood, and a newly developed model, the profile likelihood model which might be viewed as an extension of the Mantel-Haenszel approach. To exemplify these methods, we use results from a meta-analysis of 22 trials to prevent respiratory tract infections. We show that by using the multi-level approach, in the case of baseline heterogeneity, the number of clusters or components is considerably over-estimated. The approximate and profile likelihood method showed nearly the same pattern for the treatment effect distribution. To provide more evidence two simulation studies are accomplished. The profile likelihood can be considered as a clear alternative to the approximate likelihood model. In the case of strong baseline heterogeneity, the profile likelihood method shows superior behaviour when compared with the multi-level model. Copyright (C) 2006 John Wiley & Sons, Ltd.

Transformed generalized linear models

The estimation of data transformation is very useful to yield response variables satisfying closely a normal linear model, Generalized linear models enable the fitting of models to a wide range of data types. These models are based on exponential dispersion models. We propose a new class of transformed generalized linear models to extend the Box and Cox models and the generalized linear models. We use the generalized linear model framework to fit these models and discuss maximum likelihood estimation and inference. We give a simple formula to estimate the parameter that index the transformation of the response variable for a subclass of models. We also give a simple formula to estimate the rth moment of the original dependent variable. We explore the possibility of using these models to time series data to extend the generalized autoregressive moving average models discussed by Benjamin er al. [Generalized autoregressive moving average models. J. Amer. Statist. Assoc. 98, 214-223]. The usefulness of these models is illustrated in a Simulation study and in applications to three real data sets. (C) 2009 Elsevier B.V. All rights reserved.

Inference for a skew extension of the Grubbs model

In this paper, we discuss inferential aspects for the Grubbs model when the unknown quantity x (latent response) follows a skew-normal distribution, extending early results given in Arellano-Valle et al. (J Multivar Anal 96:265-281, 2005b). Maximum likelihood parameter estimates are computed via the EM-algorithm. Wald and likelihood ratio type statistics are used for hypothesis testing and we explain the apparent failure of the Wald statistics in detecting skewness via the profile likelihood function. The results and methods developed in this paper are illustrated with a numerical example.

Improved testing inference in mixed linear models

Mixed linear models are commonly used in repeated measures studies. They account for the dependence amongst observations obtained from the same experimental unit. Often, the number of observations is small, and it is thus important to use inference strategies that incorporate small sample corrections. In this paper, we develop modified versions of the likelihood ratio test for fixed effects inference in mixed linear models. In particular, we derive a Bartlett correction to such a test, and also to a test obtained from a modified profile likelihood function. Our results generalize those in [Zucker, D.M., Lieberman, O., Manor, O., 2000. Improved small sample inference in the mixed linear model: Bartlett correction and adjusted likelihood. Journal of the Royal Statistical Society B, 62,827-838] by allowing the parameter of interest to be vector-valued. Additionally, our Bartlett corrections allow for random effects nonlinear covariance matrix structure. We report simulation results which show that the proposed tests display superior finite sample behavior relative to the standard likelihood ratio test. An application is also presented and discussed. (C) 2008 Elsevier B.V. All rights reserved.

Supersymmetry searches in dilepton final states with the ATLAS experiment

One of the main goals of the ATLAS experiment at the Large Hadron Collider (LHC) at CERN in Geneva is the search for new physics beyond the Standard Model. In 2011, proton-proton collisions were performed at the LHC at a center of mass energy of 7 TeV and an integrated luminosity of 4.7 fb^{-1} was recorded. This dataset can be tested for one of the most promising theories beyond limits achieved thus far: supersymmetry. Final states in supersymmetry events at the LHC contain highly energetic jets and sizeable missing transverse energy. The additional requirement of events with highly energetic leptons simplifies the control of the backgrounds. This work presents results of a search for supersymmetry in the inclusive dilepton channel. Special emphasis is put on the search within the Gauge-Mediated Symmetry Breaking (GMSB) scenario in which the supersymmetry breaking is mediated via gauge fields. Statistically independent Control Regionsrnfor the dominant Standard Model backgrounds as well as Signal Regions for a discovery of a possible supersymmetry signal are defined and optimized. A simultaneous fit of the background normalizations in the Control Regions via the profile likelihood method allows for a precise prediction of the backgrounds in the Signal Regions and thus increases the sensitivity to several supersymmetry models. Systematic uncertainties on the background prediction are constrained via the jet multiplicity distribution in the Control Regions driven by data. The observed data are consistent with the Standard Model expectation. New limits within the GMSB and the minimal Supergravity (mSUGRA) scenario as well as for several simplified supersymmetry models are set or extended.