Bayesian Inference in the Time Varying Cointegration Model

There are both theoretical and empirical reasons for believing that the parameters of macroeconomic models may vary over time. However, work with time-varying parameter models has largely involved Vector autoregressions (VARs), ignoring cointegration. This is despite the fact that cointegration plays an important role in informing macroeconomists on a range of issues. In this paper we develop time varying parameter models which permit cointegration. Time-varying parameter VARs (TVP-VARs) typically use state space representations to model the evolution of parameters. In this paper, we show that it is not sensible to use straightforward extensions of TVP-VARs when allowing for cointegration. Instead we develop a specification which allows for the cointegrating space to evolve over time in a manner comparable to the random walk variation used with TVP-VARs. The properties of our approach are investigated before developing a method of posterior simulation. We use our methods in an empirical investigation involving a permanent/transitory variance decomposition for inflation.

Simultaneous LC-MS/MS quantification of P-glycoprotein and cytochrome P450 probe substrates and their metabolites in DBS and plasma.

BACKGROUND: An LC-MS/MS method has been developed for the simultaneous quantification of P-glycoprotein (P-gp) and cytochrome P450 (CYP) probe substrates and their Phase I metabolites in DBS and plasma. P-gp (fexofenadine) and CYP-specific substrates (caffeine for CYP1A2, bupropion for CYP2B6, flurbiprofen for CYP2C9, omeprazole for CYP2C19, dextromethorphan for CYP2D6 and midazolam for CYP3A4) and their metabolites were extracted from DBS (10 µl) using methanol. Analytes were separated on a reversed-phase LC column followed by SRM detection within a 6 min run time. RESULTS: The method was fully validated over the expected clinical concentration range for all substances tested, in both DBS and plasma. The method has been successfully applied to a PK study where healthy male volunteers received a low dose cocktail of the here described P-gp and CYP probes. Good correlation was observed between capillary DBS and venous plasma drug concentrations. CONCLUSION: Due to its low-invasiveness, simple sample collection and minimal sample preparation, DBS represents a suitable method to simultaneously monitor in vivo activities of P-gp and CYP.

Simultaneous CD8+ T cell responses to multiple tumor antigen epitopes in a multipeptide melanoma vaccine.

The recent identification and molecular characterization of tumor-associated antigens recognized by tumor-reactive CD8+ T lymphocytes has led to the development of antigen-specific immunotherapy of cancer. Among other approaches, clinical studies have been initiated to assess the in vivo immunogenicity of tumor antigen-derived peptides in cancer patients. In this study, we have analyzed the CD8+ T cell response of an ocular melanoma patient to a vaccine composed of four different tumor antigen-derived peptides administered simultaneously in incomplete Freund's adjuvant (IFA). Peptide NY-ESO-1(157-165) was remarkably immunogenic and induced a CD8+ T cell response detectable ex vivo at an early time point of the vaccination protocol. A CD8+ T cell response to the peptide analog Melan-A(26-35 A27L) was also detectable ex vivo at a later time point, whereas CD8+ T cells specific for peptide tyrosinase(368-376) were detected only after in vitro peptide stimulation. No detectable CD8+ T cell response to peptide gp100(457-466) was observed. Vaccine-induced CD8+ T cell responses declined rapidly after the initial response but increased again after further peptide injections. In addition, tumor antigen-specific CD8+ T cells were isolated from a vaccine injection site biopsy sample. Importantly, vaccine-induced CD8+ T cells specifically lysed tumor cells expressing the corresponding antigen. Together, these data demonstrate that simultaneous immunization with multiple tumor antigen-derived peptides can result in the elicitation of multiepitope-directed CD8+ T cell responses that are reactive against antigen-expressing tumors and able to infiltrate antigen-containing peripheral sites.

The simultaneous conjugacy problem in groups of piecewise linear functions

Guba and Sapir asked, in their joint paper [8], if the simultaneous conjugacy problem was solvable in Diagram Groups or, at least, for Thompson's group F. We give an elementary proof for the solution of the latter question. This relies purely on the description of F as the group of piecewise linear orientation-preserving homeomorphisms of the unit. The techniques we develop allow us also to solve the ordinary conjugacy problem as well, and we can compute roots and centralizers. Moreover, these techniques can be generalized to solve the same questions in larger groups of piecewise-linear homeomorphisms.

A Data mining approach to indirect inference

Consider a model with parameter phi, and an auxiliary model with parameter theta. Let phi be a randomly sampled from a given density over the known parameter space. Monte Carlo methods can be used to draw simulated data and compute the corresponding estimate of theta, say theta_tilde. A large set of tuples (phi, theta_tilde) can be generated in this manner. Nonparametric methods may be use to fit the function E(phi|theta_tilde=a), using these tuples. It is proposed to estimate phi using the fitted E(phi|theta_tilde=theta_hat), where theta_hat is the auxiliary estimate, using the real sample data. This is a consistent and asymptotically normally distributed estimator, under certain assumptions. Monte Carlo results for dynamic panel data and vector autoregressions show that this estimator can have very attractive small sample properties. Confidence intervals can be constructed using the quantiles of the phi for which theta_tilde is close to theta_hat. Such confidence intervals are found to have very accurate coverage.

Multiplex ultra-performance liquid chromatography-tandem mass spectrometry method for simultaneous quantification in human plasma of fluconazole, itraconazole, hydroxyitraconazole, posaconazole, voriconazole, voriconazole-N-oxide, anidulafungin, and caspofungin.

Therapeutic drug monitoring (TDM) may contribute to optimizing the efficacy and safety of antifungal therapy because of the large variability in drug pharmacokinetics. Rapid, sensitive, and selective laboratory methods are needed for efficient TDM. Quantification of several antifungals in a single analytical run may best fulfill these requirements. We therefore developed a multiplex ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method requiring 100 μl of plasma for simultaneous quantification within 7 min of fluconazole, itraconazole, hydroxyitraconazole, posaconazole, voriconazole, voriconazole-N-oxide, caspofungin, and anidulafungin. Protein precipitation with acetonitrile was used in a single extraction procedure for eight analytes. After reverse-phase chromatographic separation, antifungals were quantified by electrospray ionization-triple-quadrupole mass spectrometry by selected reaction monitoring detection using the positive mode. Deuterated isotopic compounds of azole antifungals were used as internal standards. The method was validated based on FDA recommendations, including assessment of extraction yields, matrix effect variability (<9.2%), and analytical recovery (80.1 to 107%). The method is sensitive (lower limits of azole quantification, 0.01 to 0.1 μg/ml; those of echinocandin quantification, 0.06 to 0.1 μg/ml), accurate (intra- and interassay biases of -9.9 to +5% and -4.0 to +8.8%, respectively), and precise (intra- and interassay coefficients of variation of 1.2 to 11.1% and 1.2 to 8.9%, respectively) over clinical concentration ranges (upper limits of quantification, 5 to 50 μg/ml). Thus, we developed a simple, rapid, and robust multiplex UPLC-MS/MS assay for simultaneous quantification of plasma concentrations of six antifungals and two metabolites. This offers, by optimized and cost-effective lab resource utilization, an efficient tool for daily routine TDM aimed at maximizing the real-time efficacy and safety of different recommended single-drug antifungal regimens and combination salvage therapies, as well as a tool for clinical research.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.

Simultaneous Evaluation of Intact Glucosinolates and Phenolic Compounds by UPLC-DAD-MS/MS in Brassica oleracea L. var. botrytis

A method for the simultaneous determination of intact glucosinolates and main phenolic compounds (flavonoids and sinapic acid derivatives) in Brassica oleracea L. var. botrytis was proposed. A simplified sample extraction procedure and a UPLC separation were carried out to reduce the total time of analysis. Brassica oleracea samples were added with internal standards (glucotropaeolin and rutin), and extracted with boiling methanol. Crude extracts were evaporated under nitrogen, redissolved in mobile phase and analyzed by UPLC with double detection (ESI--MRM for glucosinolates and flavonoids, and DAD for main sinapic acid derivatives). The proposed method allowed a satisfactory quantification of main native sinapic acid derivatives, flavonoids and glucosinolates with a reduced time of analysis.

A new, automated, four-colour interphase FISH approach for the simultaneous detection of specific aneuploidies of diagnosis and prognosis significance in high hyperdiploid ALL

In hyperdiploid acute lymphoblastic leukaemia (ALL), the simultaneous occurrence of specific aneuploidies confers a more favourable outcome than hyperdiploidy alone. Interphase (I) FISH complements conventional cytogenetics (CC) through its sensitivity and ability to detect chromosome aberrations in non-dividing cells. To overcome the limits of manual I-FISH, we developed an automated four-colour I-FISH approach and assessed its ability to detect concurrent aneuploidies in ALL. I-FISH was performed using centromeric probes for chromosomes 4, 6, 10 and 17. Parameters established for automatic nucleus selection and signal detection were evaluated (3 controls). Cut-off values were determined (10 controls, 1000 nuclei/case). Combinations of aneuploidies were considered relevant when each aneuploidy was individually significant. Results obtained in 10 ALL patients (1500 nuclei/patient) were compared with those by CC. Various combinations of aneuploidies were identified. All clones detected by CC were observed by I-FISH. I-FISH revealed numerous additional abnormal clones, ranging between 0.1 % and 31.6%, based on the large number of nuclei evaluated. Four-colour automated I-FISH permits the identification of concurrent aneuploidies of prognostic significance in hyperdiploid ALL. Large numbers of cells can be analysed rapidly by this method. Owing to its high sensitivity, the method provides a powerful tool for the detection of small abnormal clones at diagnosis and during follow up. Compared to CC, it generates a more detailed cytogenetic picture, the biological and clinical significance of which merits further evaluation. Once optimised for a given set of probes, the system can be easily adapted for other probe combinations.

On estimation and identifiability issues of sex-linked inheritance with a case study of pigmentation in Swiss barn owl (Tyto alba)

Genetic evaluation using animal models or pedigree-based models generally assume only autosomal inheritance. Bayesian animal models provide a flexible framework for genetic evaluation, and we show how the model readily can accommodate situations where the trait of interest is influenced by both autosomal and sex-linked inheritance. This allows for simultaneous calculation of autosomal and sex-chromosomal additive genetic effects. Inferences were performed using integrated nested Laplace approximations (INLA), a nonsampling-based Bayesian inference methodology. We provide a detailed description of how to calculate the inverse of the X- or Z-chromosomal additive genetic relationship matrix, needed for inference. The case study of eumelanic spot diameter in a Swiss barn owl (Tyto alba) population shows that this trait is substantially influenced by variation in genes on the Z-chromosome (sigma(2)(z) = 0.2719 and sigma(2)(a) = 0.4405). Further, a simulation study for this study system shows that the animal model accounting for both autosomal and sex-chromosome-linked inheritance is identifiable, that is, the two effects can be distinguished, and provides accurate inference on the variance components.

Simultaneous cell morphometry and refractive index measurement with dual-wavelength digital holographic microscopy and dye-enhanced dispersion of perfusion medium.

Digital holographic microscopy (DHM) allows optical-path-difference (OPD) measurements with nanometric accuracy. OPD induced by transparent cells depends on both the refractive index (RI) of cells and their morphology. This Letter presents a dual-wavelength DHM that allows us to separately measure both the RI and the cellular thickness by exploiting an enhanced dispersion of the perfusion medium achieved by the utilization of an extracellular dye. The two wavelengths are chosen in the vicinity of the absorption peak of the dye, where the absorption is accompanied by a significant variation of the RI as a function of the wavelength.

Multimodel inference and multimodel averaging in empirical modeling of occupational exposure levels.

Empirical modeling of exposure levels has been popular for identifying exposure determinants in occupational hygiene. Traditional data-driven methods used to choose a model on which to base inferences have typically not accounted for the uncertainty linked to the process of selecting the final model. Several new approaches propose making statistical inferences from a set of plausible models rather than from a single model regarded as 'best'. This paper introduces the multimodel averaging approach described in the monograph by Burnham and Anderson. In their approach, a set of plausible models are defined a priori by taking into account the sample size and previous knowledge of variables influent on exposure levels. The Akaike information criterion is then calculated to evaluate the relative support of the data for each model, expressed as Akaike weight, to be interpreted as the probability of the model being the best approximating model given the model set. The model weights can then be used to rank models, quantify the evidence favoring one over another, perform multimodel prediction, estimate the relative influence of the potential predictors and estimate multimodel-averaged effects of determinants. The whole approach is illustrated with the analysis of a data set of 1500 volatile organic compound exposure levels collected by the Institute for work and health (Lausanne, Switzerland) over 20 years, each concentration having been divided by the relevant Swiss occupational exposure limit and log-transformed before analysis. Multimodel inference represents a promising procedure for modeling exposure levels that incorporates the notion that several models can be supported by the data and permits to evaluate to a certain extent model selection uncertainty, which is seldom mentioned in current practice.

Simultaneous Nash bargaining with consistent beliefs

We propose and analyze a new solution concept, the R solution, for three-person, transferable utility, cooperative games. In the spirit of the Nash Bargaining Solution, our concept is founded on the predicted outcomes of simultaneous, two-party negotiations that would be the alternative to the grand coalition. These possibly probabilistic predictions are based on consistent beliefs. We analyze the properties of the R solution and compare it with the Shapley value and other concepts. The R solution exists and is unique. It belongs to the bargaining set and to the core whenever the latter is not empty. In fact, when the grand coalition can simply execute one of the three possible bilateral trades, the R solution is the most egalitarian selection of the bargaining set. Finally, we discuss how the R solution changes important conclusions of several well known Industrial Organization models.

Indirect likelihood inference

Given a sample from a fully specified parametric model, let Zn be a given finite-dimensional statistic - for example, an initial estimator or a set of sample moments. We propose to (re-)estimate the parameters of the model by maximizing the likelihood of Zn. We call this the maximum indirect likelihood (MIL) estimator. We also propose a computationally tractable Bayesian version of the estimator which we refer to as a Bayesian Indirect Likelihood (BIL) estimator. In most cases, the density of the statistic will be of unknown form, and we develop simulated versions of the MIL and BIL estimators. We show that the indirect likelihood estimators are consistent and asymptotically normally distributed, with the same asymptotic variance as that of the corresponding efficient two-step GMM estimator based on the same statistic. However, our likelihood-based estimators, by taking into account the full finite-sample distribution of the statistic, are higher order efficient relative to GMM-type estimators. Furthermore, in many cases they enjoy a bias reduction property similar to that of the indirect inference estimator. Monte Carlo results for a number of applications including dynamic and nonlinear panel data models, a structural auction model and two DSGE models show that the proposed estimators indeed have attractive finite sample properties.