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We develop a framework for price-mediated contagion in financial systems where banks are forced to liquidate assets to satisfy a risk-weight based capital adequacy requirement. In constructing this modeling framework, we introduce a two-tier pricing structure: the volume weighted average price that is obtained by any bank liquidating assets and the terminal mark-to-market price used to account for all assets held at the end of the clearing process. We consider the case of multiple illiquid assets and develop conditions for the existence and uniqueness of clearing prices. We provide a closed-form representation for the sensitivity of these clearing prices to the system parameters, and use this result to quantify: (1) the cost of regulation, in stress scenarios, faced by the system as a whole and the individual banks, and (2) the value of providing bailouts to consider when such notions are financially advisable. Numerical case studies are provided to study the application of this model to data.

We propose a model-based predictive estimator of the finite population proportion of a misclassified binary response, when information on the auxiliary variable(s) is available for all units in the population. Asymptotic properties of the misclassification-adjusted predictive estimator are also explored. We propose a computationally efficient bootstrap variance estimator that exhibits better performance compared to usual analytical variance estimator. The performance of the proposed estimator is compared with other commonly used design-based estimators through extensive simulation studies. The results are supplemented by an empirical study based on literacy data.

Over the last twenty five years, advances in the collection and analysis of functional magnetic resonance imaging (fMRI) data have enabled new insights into the brain basis of human health and disease. Individual behavioral variation can now be visualized at a neural level as patterns of connectivity among brain regions. As such, functional brain imaging is enhancing our understanding of clinical psychiatric disorders by revealing ties between regional and network abnormalities and psychiatric symptoms. Initial success in this arena has recently motivated collection of larger datasets which are needed to leverage fMRI to generate brain-based biomarkers to support the development of precision medicines. Despite numerous methodological advances and enhanced computational power, evaluating the quality of fMRI scans remains a critical step in the analytical framework. Before analysis can be performed, expert reviewers visually inspect individual raw scans and preprocessed derivatives to determine viability of the data. This Quality Control (QC) process is labor intensive, and the inability to adequately automate at large scale has proven to be a limiting factor in clinical neuroscience fMRI research. In this paper, we present a novel method for automating the QC of fMRI scans. We train machine learning classifiers using features derived from brain MR images to predict the “quality” of those images, which is based on the ground truth of an expert’s opinion. Specifically, we emphasize the importance of these classifiers’ ability to generalize their predictions across data collected in different studies. To address this, we propose a novel approach entitled “FMRI preprocessing Log mining for Automated, Generalizable Quality Control” (FLAG-QC), in which features derived from mining runtime logs are used to train the classifier. We show that classifiers trained on FLAG-QC features perform much better (AUC = 0.79) than previously proposed feature sets (AUC = 0.56) when testing their ability to generalize across studies.

Rail track derailment has been proven to be the cause of most of the rail accidents in recent years. High-temperature strains in railways caused by rail traction and thermal variation are the main causes of derailment, which lead to buckling. The likelihood of passenger deaths and maintenance costs will be reduced if thermal strains and failure in rails are detected early. This research attempts to provide a preventative strategy for detecting thermal strains and deformation caused by temperature fluctuations at railways. Using the electromechanical impedance (EMI) technique, piezoelectric sensors were used to acquire piezo coupled structural signatures for lab-sized rail samples, i.e. plain rail and rail joints, for gradual temperature escalation and repeat heat cycle. The experimental conductance signatures were obtained for the incremental rise in temperature (30–80 °C) in ambient conditions, and repetitive thermal cycles. To better diagnose structural problems induced only by the effect of heat on the host structure, thermal compensation is proposed. The piezo–coupled signatures for thermal changes in rail and rail junctions (weld and bolt) were found to be particularly effective in detecting incipient structural alterations for both steady and cyclic temperature variations. Statistical damage index was used to quantify the damage for all types of rail-joint bar caused due to temperature variation. Overall, this study has paved an experimental technique that can be used to detect early damage in rail and rail joints due to thermal loading.

A statistical model description of heavy ion induced fusion-fission reactions is presented where shell effects, collective enhancement of level density, tilting away effect of compound nuclear spin and dissipation are included. It is shown that the inclusion of all these effects provides a consistent picture of fission where fission hindrance is required to explain the experimental values of both pre-scission neutron multiplicities and evaporation residue cross-sections in contrast to some of the earlier works where a fission hindrance is required for pre-scission neutrons but a fission enhancement for evaporation residue cross-sections., 14 pages, 2 figures

Evaporation residue cross sections for the $^{16}\mathrm{O}+^{203,205}\mathrm{Tl}$ reactions were measured at laboratory beam energies in the range of 82--113 MeV using a gas-filled separator. Transmission efficiency of the separator was estimated using a calibration reaction $^{16}\mathrm{O}+^{197}\mathrm{Au}$ and by simulating the evaporation residues angular distributions. Statistical model calculations were performed for both the measured systems. These calculations overestimate the experimental evaporation residue cross sections. This could be attributed to the presence of noncompound nuclear fission. An estimation of noncompound nuclear fission contribution was carried out. Comparison with neighboring systems shows that a slight change in the entrance channel or the compound nucleus properties makes a large difference in evaporation residue cross sections.

Background: Despite remarkable success of the statistical model (SM) in describing decay of excited compound nuclei (CN), reproduction of the observables from heavy ion-induced fusion-fission reactions is often quite challenging. Ambiguities in choosing the input parameters, lack of clarity about inclusion of various physical effects in the model, and contradictory requirements of input parameters while describing different observables from similar reactions are among the major difficulties of modeling decay of fissile CN.Purpose: This work attempts to overcome the existing inconsistencies by inclusion of important physical effects in the model and through a systematic analysis of a large set of data over a wide range of CN mass (${A}_{\mathrm{CN}}$).Method: The model includes the shell effect in the level density (LD) parameter, shell correction in the fission barrier (${B}_{\mathrm{f}}$), the effect of the orientation degree of freedom of the CN spin (${K}_{\mathrm{or}}$), collective enhancement of level density (CELD), and dissipation in fission. Input parameters are not tuned to reproduce observables from specific reaction(s) and the reduced dissipation coefficient ($\ensuremath{\beta}$) is treated as the only adjustable parameter. Calculated evaporation residue (ER) cross sections (${\ensuremath{\sigma}}_{\mathrm{ER}}$), fission cross sections (${\ensuremath{\sigma}}_{\mathrm{fiss}}$), and particle, i.e., neutron, proton, and $\ensuremath{\alpha}$ particle, multiplicities are compared with data covering ${A}_{\mathrm{CN}}=156$--248.Results: The model produces reasonable fits to ER and fission excitation functions for all the reactions considered in this work. Pre-scission neutron multiplicities (${\ensuremath{\nu}}_{\mathrm{pre}}$) are underestimated by the calculation beyond ${A}_{\mathrm{CN}}\ensuremath{\sim}200$. An increasingly higher value of $\ensuremath{\beta}$, in the range of 2--$4\ifmmode\times\else\texttimes\fi{}{10}^{21}\phantom{\rule{4pt}{0ex}}{\mathrm{s}}^{\ensuremath{-}1}$, is required to reproduce the data with increasing ${A}_{\mathrm{CN}}$. Proton and $\ensuremath{\alpha}$-particle multiplicities, measured in coincidence with both ERs and fission fragments, are in qualitative agreement with model predictions.Conclusions: The present work mitigates the existing inconsistencies in modeling statistical decay of the fissile CN to a large extent. Contradictory requirements of fission enhancement---obtained by scaling down the fission barrier to reproduce ${\ensuremath{\sigma}}_{\mathrm{ER}}$ or ${\ensuremath{\sigma}}_{\mathrm{fiss}}$ and fission suppression realized by introducing dissipation in the fission channel to reproduce ${\ensuremath{\nu}}_{\mathrm{pre}}$, for similar reactions---have now become redundant. There are scopes for further refinement of the model, as is evident from the mismatch between measured and calculated particle multiplicities in a few cases.

In this paper we study the implications of contingent payments on the clearing wealth in a network model of financial contagion. We consider an extension of the Eisenberg–Noe financial contagion model in which the nominal interbank obligations depend on the wealth of the firms in the network. We first consider the problem in a static framework and develop conditions for existence and uniqueness of solutions as long as no firm is speculating on the failure of other firms. In order to achieve existence and uniqueness under more general conditions, we introduce a dynamic framework. We demonstrate how this dynamic framework can be applied to problems that were ill-defined in the static framework.

Valuing Debt and Equity in Interbank Networks Valuation adjustments to account for, for example, counterparty risk, have become an important part of derivative valuation by any bank since the 2007–2009 financial crisis. As evidenced by that crisis, considering the risk of a single firm alone can cause gross misspecification of firm health. In “Pricing of Debt and Equity in a Financial Network with Comonotonic Endowments,” Banerjee and Feinstein construct an analytical formulation for a valuation adjustment that takes the entire network of counterparty obligations into account when all institutions have comonotonic endowments. From the perspective of stress testing, such a setting corresponds to a systematic shock to all firms. This comonotonic setting is then shown to provide computationally tractable upper and lower bounds to the general network valuation problem. The authors demonstrate that the comonotonic endowment setting arises endogenously in a system of equity maximizing firms.

We studied the competition between fusion and quasifission by populating the compound nucleus (CN) $^{210}$Rn through different entrance channels - $^{16}$O+$^{194}$Pt and $^{30}$Si+$^{180}$Hf, at energies around the Coulomb barrier. The larger widths of the fission fragment mass distribution in the $^{30}$Si+$^{180}$Hf reaction which could not be explained by using transition state models, indicated the onset of quasifission in this reaction. Further, the evaporation residues (ERs) produced in these reactions were measured using the gas-filled recoil mass separator HYRA at IUAC. The measurements showed reduced ER cross sections for the $^{30}$Si+$^{180}$Hf reaction when compared with that of $^{16}$O+$^{194}$Pt at similar excitation energies, confirming the presence of quasifission in the $^{30}$Si+$^{180}$Hf reaction. The experimental results are analysed using the dinuclear system (DNS) and statistical models to understand the possible influence of potential energy surfaces (PES) and different entrance channel conditions in heavy ion fusion reactions.

We use compactifications of C*-algebras to introduce noncommutative coarse geometry. We transfer a noncommutative coarse structure on a C*-algebra with an action of a locally compact Abelian group by translations to Rieffel deformations and prove that the resulting noncommutative coarse spaces are coarsely equivalent. We construct a noncommutative coarse structure from a cocompact continuously square-integrable action of a group and show that this is coarsely equivalent to the standard coarse structure on the group in question. We define noncommutative coarse maps through certain completely positive maps that induce *-homomorphisms on the boundaries of the compactifications. We lift *-homomorphisms between separable, nuclear boundaries to noncommutative coarse maps and prove an analogous lifting theorem for maps between the metrisable boundaries of ordinary locally compact spaces.

Mass angle distribution measurements for 19 F+238 U reaction were carried out around the sub barrier energies. Mass angle correlation has not been observed at above and below the fusion barrier in present reaction. This infer the minimal presence of non compound like events at these bombarding energies range.

A model-based predictive estimator is proposed for the population proportions of a polychotomous response variable, based on a sample from the population and on auxiliary variables, whose values are known for the entire population. The responses for the non-sample units are predicted using a multinomial logit model, which is a parametric function of the auxiliary variables. A bootstrap estimator is proposed for the variance of the predictive estimator, its consistency is proved and its small sample performance is compared with that of an analytical estimator. The proposed predictive estimator is compared with other available estimators, including model-assisted ones, both in a simulation study involving different sampling designs and model mis-specification, and using real data from an opinion survey. The results indicate that the prediction approach appears to use auxiliary information more efficiently than the model-assisted approach.

For sustainable and eco-friendly construction, more attention is being paid to the concept of 'green buildings'. The search for environmentally friendly materials in the concrete construction involves inquisition of natural material which could effectively substitute cement and aggregates. Moreover, disposal of agricultural waste materials such as rice husk, groundnut husk and coconut shell have constituted an environmental challenge, hence, there is a need to convert these naturally available waste materials into useful materials. The paper investigates about coconut shell and coconut fibre as an aggregate is a potential construction material. This paper experimentally investigates to replace fine aggregates with coconut fibres and coarse aggregates with coconut shell to improve the mechanical strength and bonding properties of the concrete mixture. Further, the compressive strength split tensile strength, of the concrete mixture, has been determined for mix concrete. The results shows the replaced natural material has significant potential for lightweight aggregates.

Fission-fragment mass distributions were measured for 225,227Pa nuclei formed in fusion reactions of 19F + 206,208Pb around fusion barrier energies. Mass-angle correlations do not indicate any quasi-fission like events in this bombarding energy range. Mass distributions were fitted by Gaussian distribution and mass variance extracted. At below-barrier energies, the mass variance was found to increase with decrease in energy for both nuclei. Results from present work were compared with existing data for induced fission of 224,226Th and 228U around barrier energies. Enhancement in mass variance of 225,227Pa nuclei at below-barrier energies shows evidence for presence of asymmetric fission events mixed with symmetric fission events. This is in agreement with the results of mass distributions of nearby nuclei 224,226Th and 228U where two-mode fission process was observed. Two-mode feature of fission arises due to the shell effects changing the landscape of the potential-energy surfaces at low excitation energies. The excitation-energy dependence of the mass variance gives strong evidence for survival of microscopic shell effects in fission of light actinide nuclei 225,227Pa with initial excitation energy ∼30–50 MeV.

International audience; Barrier distribution for the ^28Si+^154Sm system has been extracted from large angle quasielastic scattering measurement to investigate the role of various channel couplings on fusion dynamics. The coupled channel calculations, including the collective excitation of the target and projectile, are observed to reproduce the experimental BD rather well. It seems that the role of neutron transfer, relative to collective excitation, is in fact weak in the ^28Si+^154Sm system even though it has positive Q-value for neutron transfer channels.

We propose a penalized splines (P-splines) based method to predict the pathological stage of localized prostate cancer. A combination of prostate-specific antigen, Gleason histological score, and clinical stage from a cohort study of 834 prostate cancer patients are used to build the P-splines model. It turns out that the proposed methodology results in improved prediction of pathological stage compared to usual logistic regression after removing a few outliers. The improvement is shown to be statistically significant. Receiver-operating characteristic (ROC) curve is drawn and we show that the increase in area under the ROC curve over the commonly used logistic regression based classification method is also statistically significant.

Two-way nested design with mixed effects model arises in many practical situations. In the classical analysis of variance set-up, a test for the absence of the random effects is obtained under the assumption that the random effects and the errors are normally distributed. The present paper avoids this assumption and provides an asymptotically distribution-free test procedure for the above problem. The asymptotic null distribution of the test statistic is obtained. Actual implementation of the test is straight forward given the prior information on quantiles of the intra-block differences of observations. In the absence of such information, working test procedures are proposed. The performances of these tests are compared with the classical analysis of variance test through simulations. The tests are then illustrated by some real data sets.

This paper focuses on the question of specification of measurement error distribution and the distribution of true predictors in generalized linear models when the predictors are subject to measurement errors. The standard measurement error model typically assumes that the measurement error distribution and the distribution of covariates unobservable in the main study are normal. To make the model flexible enough we, instead, assume that the measurement error distribution is multivariate t and the distribution of true covariates is a finite mixture of normal densities. Likelihood–based method is developed to estimate the regression parameters. However, direct maximization of the marginal likelihood is numerically difficult. Thus as an alternative to it we apply the EM algorithm. This makes the computation of likelihood estimates feasible. The performance of the proposed model is investigated by simulation study.

We develop a method for testing the equality of two or more distributions of random vectors with categorical components. We define a function that gives a distance between any two data vectors. Each observed data vector is linked with its nearest neighbor(s). The test statistic is the number of edges linking observations from different distributions. Inference is conditional on the number of observations from each distribution and the number of times each of the data vectors is observed in the pooled sample. Permutation testing and asymptotics are used to estimate the observed significance level.

Multiple counts may occur in each cell of an a × b two-way layout (balanced or unbalanced) of two fixed factors A and B. Standard log-linear model analysis based on a Poisson distribution assumption of the cell counts is not applicable here, because of the unbalanced nature of the table or because the Poisson distribution assumption is not valid. We develop C(α) tests for interaction and main effects assuming data to be Poisson distributed and also assuming that data within the cells have extra (over/under) dispersion beyond that explained by a Poisson distribution. For this we consider an extended negative binominal distribution and a semiparametric model using the quasi-likelihood. We show that in all situations the C(α) tests for interaction are of very simple forms. For C(α) tests for the main effect in presence of no interaction, such simplification is possible only under certain conditions. A score test for detecting extra dispersion in presence of interaction is also obtained and is of sim...

We consider cDNA microarray experiments when the cell populations have a factorial structure, and investigate the problem of their optimal designing under a baseline parametrization where the objects of interest differ from those under the more common orthogonal parametrization. First, analytical results are given for the $2\times 2$ factorial. Since practical applications often involve a more complex factorial structure, we next explore general factorials and obtain a collection of optimal designs in the saturated, that is, most economic, case. This, in turn, is seen to yield an approach for finding optimal or efficient designs in the practically more important nearly saturated cases. Thereafter, the findings are extended to the more intricate situation where the underlying model incorporates dye-coloring effects, and the role of dye-swapping is critically examined., Published in at http://dx.doi.org/10.1214/07-AOAS144 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Shoutir Kishore Chatterjee was born in Ranchi, a small hill station in India, on November 6, 1934. He received his B.Sc. in statistics from the Presidency College, Calcutta, in 1954, and M.Sc. and Ph.D. degrees in statistics from the University of Calcutta in 1956 and 1962, respectively. He was appointed a lecturer in the Department of Statistics, University of Calcutta, in 1960 and was a member of its faculty until his retirement as a professor in 1997. Indeed, from the 1970s he steered the teaching and research activities of the department for the next three decades. Professor Chatterjee was the National Lecturer in Statistics (1985--1986) of the University Grants Commission, India, the President of the Section of Statistics of the Indian Science Congress (1989) and an Emeritus Scientist (1997--2000) of the Council of Scientific and Industrial Research, India. Professor Chatterjee, affectionately known as SKC to his students and admirers, is a truly exceptional person who embodies the spirit of eternal India. He firmly believes that ``fulfillment in man's life does not come from amassing a lot of money, after the threshold of what is required for achieving a decent living is crossed. It does not come even from peer recognition for intellectual achievements. Of course, one has to work and toil a lot before one realizes these facts.'', Comment: Published in at http://dx.doi.org/10.1214/088342306000000565 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

(2006). Exploring Multivariate Data With the Forward Search. Journal of the American Statistical Association: Vol. 101, No. 473, pp. 398-398.