We develop uncertainty measures for point forecasts from surveys such as the Survey of Professional Forecasters, Blue Chip, or the Federal Open Market Committee’s Summary of Economic Projections. At a given point of time, these surveys provide forecasts for macroeconomic variables at multiple horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-hor...

Many decisions in both the public and private sectors are based on expectations of future population change. Planning for schools, hospitals, shopping centers, housing developments, electric power plants, and many other projects is strongly influenced by expected population growth or decline. The distribution of government funds and the granting of various types of licenses and permits may be a...

This paper presents a framework for designing a hidden Markov model (HMM)-based audio-visual automatic speech recognition (ASR) system based on minimum classification error training. Audio/visual HMM parameters are optimized with the generalized probabilistic descent (GPD) method, and their likelihoods are combined using model-dependent stream weights which are also estimated with the GPD metho...

Gastric precancerous diseases (GPD) may deteriorate into early gastric cancer if misdiagnosed, so it is important to help doctors recognize GPD accurately and quickly. In this paper, we realize the classification of 3-class GPD, namely, polyp, erosion, and ulcer using convolutional neural networks (CNN) with a concise model called the Gastric Precancerous Disease Network (GPDNet). GPDNet introd...

This paper presents an application of Artificial Neural Network (ANN) to forecast inflation in India during the period 1994-2009. The study presents four different ANN models on the basis of inflation (WPI), economic growth (IIP), and money supply (MS). The first model is a univariate model based on past WPI only. The other three are multivariate models based on WPI and IIP, WPI and MS, WPI, an...

(a) To provide an accurate, efficient, computational model (two-scale approximation, TSA) for the 4-wave interactions, in operational wave forecast models, suitable for global, basin and coastal scale applications, and able to transition seamlessly from deep to shallow water. (b) Fully test TSA with respect to exact codes for the full Boltzmann integral (FBI), for durationlimited, fetch-limited...

In this paper we propose an additive mixture model, where one component is the Generalized Pareto distribution (GPD) that allows us to estimate extreme quantiles. GPD plays an important role in modeling extreme quantiles for the wide class of distributions belonging to the maximum domain of attraction of an extreme value model. One of the main diffi culty with this modeling approach is the choi...

Despite their popularity as proxies of expected returns, the implied cost of capital’s (ICC) measurement error properties are relatively unknown. Through an in-depth analysis of a popular implementation of ICCs by Gebhardt, Lee, and Swaminathan (2001) (GLS), I show that ICC measurement errors can be not only nonrandom and persistent, but can also be associated with firms’ risk or growth charact...

A series of observing system simulation experiments has been performed to assess the potential impact of marine surface wind data on numerical weather prediction. Care was taken to duplicate the spatial coverage and error characteristics of conventional surface, radiosonde, ship, and aircraft reports. These observations, suitably degraded to account for instrument and sampling errors, were used...

Analytic properties of hard exclusive processes described by Generalized Parton Distributions (GPD’s) are considered. The analytic continuation of GPD is provided by Generalized Distribution Amplitudes (GDA). The GDA’s for the production of two ρ−mesons may give an access to four-quark exotic states. The crucial role in the proof of analyticity is played by the Cavalieri conditions (polynomiali...