Generalized factor models: a bayesian approach

There is recent interest in the generalization of classical factor models in which the idiosyncratic factors are assumed to be orthogonal and there are identification restrictions on cross-sectional and time dimensions. In this study, we describe and implement a Bayesian approach to generalized factor models. A flexible framework is developed to determine the variations attributed to common and idiosyncratic factors. We also propose a unique methodology to select the (generalized) factor model that best fits a given set of data. Applying the proposed methodology to the simulated data and the foreign exchange rate data, we provide a comparative analysis between the classical and generalized factor models. We find that when there is a shift from classical to generalized, there are significant changes in the estimates of the structures of the covariance and correlation matrices while there are less dramatic changes in the estimates of the factor loadings and the variation attributed to common factors.

Compositional ideas in the bayesian analysis of categorical data with application to dose finding clinical trials

Compositional random vectors are fundamental tools in the Bayesian analysis of categorical data.Many of the issues that are discussed with reference to the statistical analysis of compositionaldata have a natural counterpart in the construction of a Bayesian statistical model for categoricaldata.This note builds on the idea of cross-fertilization of the two areas recommended by Aitchison (1986)in his seminal book on compositional data. Particular emphasis is put on the problem of whatparameterization to use

Bayesian tools for zero counts in compositional data

The log-ratio methodology makes available powerful tools for analyzing compositionaldata. Nevertheless, the use of this methodology is only possible for those data setswithout null values. Consequently, in those data sets where the zeros are present, aprevious treatment becomes necessary. Last advances in the treatment of compositionalzeros have been centered especially in the zeros of structural nature and in the roundedzeros. These tools do not contemplate the particular case of count compositional datasets with null values. In this work we deal with \count zeros" and we introduce atreatment based on a mixed Bayesian-multiplicative estimation. We use the Dirichletprobability distribution as a prior and we estimate the posterior probabilities. Then weapply a multiplicative modi¯cation for the non-zero values. We present a case studywhere this new methodology is applied.Key words: count data, multiplicative replacement, composition, log-ratio analysis

Bayesian nash equilibrium in ''linear'' cournot models with private information about costs

Calculating explicit closed form solutions of Cournot models where firms have private information about their costs is, in general, very cumbersome. Most authors consider therefore linear demands and constant marginal costs. However, within this framework, the nonnegativity constraint on prices (and quantities) has been ignored or not properly dealt with and the correct calculation of all Bayesian Nash equilibria is more complicated than expected. Moreover, multiple symmetric and interior Bayesianf equilibria may exist for an open set of parameters. The reason for this is that linear demand is not really linear, since there is a kink at zero price: the general ''linear'' inverse demand function is P (Q) = max{a - bQ, 0} rather than P (Q) = a - bQ.

Estimating Bayesian decision problems with heterogeneous priors

In many areas of economics there is a growing interest in how expertise andpreferences drive individual and group decision making under uncertainty. Increasingly, we wish to estimate such models to quantify which of these drive decisionmaking. In this paper we propose a new channel through which we can empirically identify expertise and preference parameters by using variation in decisionsover heterogeneous priors. Relative to existing estimation approaches, our \Prior-Based Identification" extends the possible environments which can be estimated,and also substantially improves the accuracy and precision of estimates in thoseenvironments which can be estimated using existing methods.

Forecasting and turning point predictions in a Bayesian panel VAR model

We provide methods for forecasting variables and predicting turning points in panel Bayesian VARs. We specify a flexible model which accounts for both interdependencies in the cross section and time variations in the parameters. Posterior distributions for the parameters are obtained for a particular type of diffuse, for Minnesota-type and for hierarchical priors. Formulas for multistep, multiunit point and average forecasts are provided. An application to the problem of forecasting the growth rate of output and of predicting turning points in the G-7 illustrates the approach. A comparison with alternative forecasting methods is also provided.

Fixed and random effects in Classical and Bayesian regression

This paper proposes a common and tractable framework for analyzingdifferent definitions of fixed and random effects in a contant-slopevariable-intercept model. It is shown that, regardless of whethereffects (i) are treated as parameters or as an error term, (ii) areestimated in different stages of a hierarchical model, or whether (iii)correlation between effects and regressors is allowed, when the sameinformation on effects is introduced into all estimation methods, theresulting slope estimator is also the same across methods. If differentmethods produce different results, it is ultimately because differentinformation is being used for each methods.

A fast Bayesian reconstruction algorithm for emission tomography with entropy prior converging to feasible images

The development and tests of an iterative reconstruction algorithm for emission tomography based on Bayesian statistical concepts are described. The algorithm uses the entropy of the generated image as a prior distribution, can be accelerated by the choice of an exponent, and converges uniformly to feasible images by the choice of one adjustable parameter. A feasible image has been defined as one that is consistent with the initial data (i.e. it is an image that, if truly a source of radiation in a patient, could have generated the initial data by the Poisson process that governs radioactive disintegration). The fundamental ideas of Bayesian reconstruction are discussed, along with the use of an entropy prior with an adjustable contrast parameter, the use of likelihood with data increment parameters as conditional probability, and the development of the new fast maximum a posteriori with entropy (FMAPE) Algorithm by the successive substitution method. It is shown that in the maximum likelihood estimator (MLE) and FMAPE algorithms, the only correct choice of initial image for the iterative procedure in the absence of a priori knowledge about the image configuration is a uniform field.

UVBY-Beta Photometry for 67 Stars in the Region of Alpha-Persei

UVBY-Beta photometric data are presented for 67 stars in the region of alpha-Persei and their relationship to the cluster is analyzed. These data allowed us to confirm at least 16 new members of the alpha-Persei open cluster.

Bayesian image reconstruction with space-variant noise suppression

In this paper we present a Bayesian image reconstruction algorithm with entropy prior (FMAPE) that uses a space-variant hyperparameter. The spatial variation of the hyperparameter allows different degrees of resolution in areas of different statistical characteristics, thus avoiding the large residuals resulting from algorithms that use a constant hyperparameter. In the first implementation of the algorithm, we begin by segmenting a Maximum Likelihood Estimator (MLE) reconstruction. The segmentation method is based on using a wavelet decomposition and a self-organizing neural network. The result is a predetermined number of extended regions plus a small region for each star or bright object. To assign a different value of the hyperparameter to each extended region and star, we use either feasibility tests or cross-validation methods. Once the set of hyperparameters is obtained, we carried out the final Bayesian reconstruction, leading to a reconstruction with decreased bias and excellent visual characteristics. The method has been applied to data from the non-refurbished Hubble Space Telescope. The method can be also applied to ground-based images.

Analysis of IRAS stellar sources in Alpha Persei region

The present study is an analysis of IR sources in the Alpha Persei open cluster region from the IRAS Point Source Catalog and from ground-based photometric observations. Cross-identification between stars in the region and IRAS Point Source Catalog was performed and nine new associations were found. BVRI Johnson photometry for 24 of the matched objects have been carried out. Physical identity of visual and IRAS sources and relationship to the Alpha Persei open cluster are discussed.

Image restoration in astronomy: a Bayesian perspective

When preparing an article on image restoration in astronomy, it is obvious that some topics have to be dropped to keep the work at reasonable length. We have decided to concentrate on image and noise models and on the algorithms to find the restoration. Topics like parameter estimation and stopping rules are also commented on. We start by describing the Bayesian paradigm and then proceed to study the noise and blur models used by the astronomical community. Then the prior models used to restore astronomical images are examined. We describe the algorithms used to find the restoration for the most common combinations of degradation and image models. Then we comment on important issues such as acceleration of algorithms, stopping rules, and parameter estimation. We also comment on the huge amount of information available to, and made available by, the astronomical community.

Defective TNF-alpha-mediated hepatocellular apoptosis and liver damage in acidic sphingomyelinase knockout mice

This study addressed the contribution of acidic sphingomyelinase (ASMase) in TNF-alpha-mediated hepatocellular apoptosis. Cultured hepatocytes depleted of mitochondrial glutathione (mGSH) became sensitive to TNF-alpha, undergoing a time-dependent apoptotic cell death preceded by mitochondrial membrane depolarization, cytochrome c release, and caspase activation. Cyclosporin A treatment rescued mGSH-depleted hepatocytes from TNF-alpha-induced cell death. In contrast, mGSH-depleted hepatocytes deficient in ASMase were resistant to TNF-alpha-mediated cell death but sensitive to exogenous ASMase. Furthermore, although in vivo administration of TNF-alpha or LPS to galactosamine-pretreated ASMase(+/+) mice caused liver damage, ASMase(-/-) mice exhibited minimal hepatocellular injury. To analyze the requirement of ASMase, we assessed the effect of glucosylceramide synthetase inhibition on TNF-alpha-mediated apoptosis. This approach, which blunted glycosphingolipid generation by TNF-alpha, protected mGSH-depleted ASMase(+/+) hepatocytes from TNF-alpha despite enhancement of TNF-alpha-stimulated ceramide formation. To further test the involvement of glycosphingolipids, we focused on ganglioside GD3 (GD3) because of its emerging role in apoptosis through interaction with mitochondria. Analysis of the cellular redistribution of GD3 by laser scanning confocal microscopy revealed the targeting of GD3 to mitochondria in ASMase(+/+) but not in ASMase(-/-) hepatocytes. However, treatment of ASMase(-/-) hepatocytes with exogenous ASMase induced the colocalization of GD3 and mitochondria. Thus, ASMase contributes to TNF-alpha-induced hepatocellular apoptosis by promoting the mitochondrial targeting of glycosphingolipids.

Immunoelectron microscopic localization of transforming growth factor alpha in rat colon

Transforming growth factor alpha (TGF alpha) is a polypeptide, which binds to the epidermal growth factor receptor to carry out its function related to cell proliferation and differentiation. The ultrastructural localisation of TGF alpha was studied in both the proximal and the distal colon. The columnar cells, lining the surface epithelium of the proximal colon, showed a strong immunoreactivity in the polyribosomes and in the interdigitations of the lateral membrane. The columnar cells of the crypts and the goblet cells in both the proximal and the distal colon showed the immunostaining in the cis and trans cisternae of the Golgi apparatus. TGF alpha seems to be processed differently in the surface columnar cells and in the crypt columnar cells and goblet cells. Moreover, it probably has different roles in proliferation and differentiation.