Some statistical and computational challenges, and opportunities in astronomy

The data complexity and volume of astronomical findings have increased in recent decades due to major technological improvements in instrumentation and data collection methods. The contemporary astronomer is flooded with terabytes of raw data that produce enormous multidimensional catalogs of objects (stars, galaxies, quasars, etc.) numbering in the billions, with hundreds of measured numbers for each object. The astronomical community thus faces a key task: to enable efficient and objective scientific exploitation of enormous multifaceted data sets and the complex links between data and astrophysical theory. In recognition of this task, the National Virtual Observatory (NVO) initiative recently emerged to federate numerous large digital sky archives, and to develop tools to explore and understand these vast volumes of data. The effective use of such integrated massive data sets presents a variety of new challenging statistical and algorithmic problems that require methodological advances. An interdisciplinary team of statisticians, astronomers and computer scientists from The Pennsylvania State University, California Institute of Technology and Carnegie Mellon University is developing statistical methodology for the NVO. A brief glimpse into the Virtual Observatory and the work of the Penn State-led team is provided here

Coating thickness and elastic modulus measurement using ultrasonic bulk wave resonance

Measurement of the resonant through thickness ultrasonic modes of a homogeneous plate using a fast Fourier transform of the temporal data can be used to calculate plate thickness very accurately. We describe an extension of this principle to two-layer systems, examining a thin coating on a substrate of known properties. The resonant behavior of these systems is predicted and we explain how this approach is used to measure coating thickness and elastic modulus. Noncontact electromagnetic acoustic transducers are used for ultrasonic measurement, as they do not significantly affect the resonant response of the system, unlike alternative contact transducers

The New Keynesian Phillips Curve and Lagged Inflation: A Case of Spurious Correlation?

The New Keynesian Phillips Curve (NKPC) specifies a relationship between inflation and a forcing variable and the current period’s expectation of future inflation. Most empirical estimates of the NKPC, typically based on Generalized Method of Moments (GMM) estimation, have found a significant role for lagged inflation, producing a “hybrid” NKPC. Using U.S. quarterly data, this paper examines whether the role of lagged inflation in the NKPC might be due to the spurious outcome of specification biases. Like previous investigators, we employ GMM estimation and, like those investigators, we find a significant effect for lagged inflation. We also use time varying-coefficient (TVC) estimation, a procedure that allows us to directly confront specification biases and spurious relationships. Using three separate measures of expected inflation, we find strong support for the view that, under TVC estimation, the coefficient on expected inflation is near unity and that the role of lagged inflation in the NKPC is spurious.New Keynesian Phillips Curve; time-varying coefficients; spurious relationships

A Portfolio Balance Approach to Euro-Area Money Demand in a Time-Varying Environment

As part of its monetary policy strategy, the European Central Bank has formulated a reference value for M3 growth. A pre-requisite for the use of a reference value for M3 growth is the existence of a stable demand function for that aggregate. However, a large empirical literature has emerged showing that, beginning in 2001, essentially all euro area M3 demand functions have exhibited instability. This paper argues that a proper understanding of the determination of money requires a portfolio analysis where the demand for broad money is seen as just one element in the wealth portfolio. Under this framework, wealth is the variable that constitutes the total budget constraint on the holdings of assets, including money, and changes in equity prices are a key transmission channel of monetary policy. Understanding money behaviour thus requires good data on euro area wealth which at present do not exist. Our basic premise is that there is a stable demand-for-money function but that the models that have been used until now to estimate euro area money-demand are not well-specified because they do not include a measure of wealth. Using two empirical methodologies - - a co-integrated vector equilibrium correction (VEC) approach and a time-varying coefficient (TVC) approach - - we find that a demand-for-money function that includes wealth is stable. The upshot of our findings is that M3 behaviour continues to provide useful information about medium-term developments on inflation.Money demand; VEC, time varying coefficient estimation; Euro area

Aerodynamic shape optimization of arbitrary hypersonic vehicles

A new method was developed to optimize, in terms of aerodynamic wave drag minimization, arbitrary (nonaxisymmetric) hypersonic vehicles in modified Newtonian flow, while maintaining the initial volume and length of the vehicle. This new method uses either a surface fitted Fourier series to represent the vehicle's geometry or an independent point motion algorithm. In either case, the coefficients of the Fourier series or the spatial locations of the points defining each cross section were varied and a numerical optimization algorithm based on a quasi-Newton gradient search concept was used to determine the new optimal configuration. Results indicate a significant decrease in aerodynamic wave drag for simple and complex geometries at relatively low CPU costs. In the case of a cone, the results agreed well with known analytical optimum ogive shapes. The procedure is capable of accepting more complex flow field analysis codes

Fruit production forecasting by neuro-fuzzy techniques

Neuro-fuzzy techniques are finding a practical application in many fields such as in model identification and forecasting of linear and non-linear systems. This paper presents a neuro-fuzzy model for forecasting the fruit production of some agriculture products (olives, lemons, oranges, cherries and pistachios). The model utilizes a time series of yearly data. The fruit forecasting is based on Adaptive Neural Fuzzy Inference System (ANFIS). ANFIS uses a combination of the least-squares method and the backprobagation gradient descent method to estimate the optimal food forecast parameters for each year. The results are compared to those of an Autoregressive (AR) model and an Autoregressive Moving Average model (ARMA).Fruit forecasting, neuro-fuzzy, ANFIS, AR, ARMA, forecasting, fruit production, Agricultural Finance, Crop Production/Industries,

Estimation of Parameters in the Presence of Model misspecification and Measurement Error

Misspecifications of econometric models can lead to biased coefficients and error terms, which in turn can lead to incorrect inference and incorrect models. There are specific techniques such as instrumental variables which attempt to deal with some individual forms of model misspecification. However these can typically only address one problem at a time. This paper proposes a general method for estimating underlying parameters in the presence of a range of unknown model misspecifications. It is argued that this method can consistently estimate the direct effect of an independent variable on a dependent variable with all of its other determinants held constant even in the presence of a misspecified functional form, measurement error and omitted variables.Misspecified model; Correct interpretation of coefficients; Appropriate assumption; Time-varying coefficient model; Coefficient driver

Analysis of a unidirectional composite containing broken fibers and matrix damage

An analytical solution is developed for the determination of the stresses and displacements in a unidirectional fiber-reinforced composite containing an arbitrary number of broken fibers as well as longitudinal yielding and splitting of the matrix. The solution is developed using a materials-modeling approach which is based on a shear-lag stress transfer mechanism. The equilibrium equation in the axial direction gives a pair of integral equations which are solved numerically. Excellent agreement is shown to exist between the solution and experimental results for notched unidirectional boron/aluminum laminates without splitting. For brittle matrix composites (i.e. epoxy) equally good results are indicated for both matrix yielding and splitting. For yielding without splitting the fracture strength depends on crack length while for large splitting it is crack length independent