Scientists, engineers and the like are a strange lot. Unperturbed by societal norms,
they direct their energies to finding better alternatives to existing theories and concocting
solutions to unsolved problems. Driven by an insatiable curiosity, they record
their observations and crunch the numbers. This tome is about the science of crunching.
It’s about digging out something of value from the detritus that others tend to
leave behind. The described approaches involve constructing models to process the
Our model identiﬁes new explanations for why large and small ﬁrms make different insurance offer
decisions; they are based on turnover rates and within-ﬁrm and between-ﬁrm heterogeneities. Large
ﬁrms tend to have greater within-ﬁrm heterogeneity than small ones, and so they are more likely to have
some employees who strongly desire health insurance and less likely to attract only workers who do not
ﬁnd health insurance attractive.
Next, we can modify our model to account for different ﬁrm sizes. For notational convenience and
ease of exposition, we have used a continuum model. A ﬁrm hires a unit mass of consumers. The size of
the ﬁrm then becomes a normalization and hence has no bearing on the dynamics and steady-state
properties. In practice, ﬁrms hire a ﬁnite number of workers, and the law of large number becomes a
poor approximation when the ﬁrm is small. Even when a small ﬁrm draws from the same work force as
any other ﬁrm, the variance of workers’ health-care cost may be larger.
Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Estimation of prediction error variances via Monte Carlo sampling methods using different formulations of the prediction error variance
Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: Prediction error variance and expected response to selection, when selection is based on the best predictor for Gaussian and threshold characters, traits following a Poisson mixed model and survival traits
Neural Networks as Nonlinear Adaptive Filters
Neural networks, in particular recurrent neural networks, are cast into the framework of nonlinear adaptive ﬁlters. In this context, the relation between recurrent neural networks and polynomial ﬁlters is ﬁrst established. Learning strategies and algorithms are then developed for neural adaptive system identiﬁers and predictors. Finally, issues concerning the choice of a neural architecture with respect to the bias and variance of the prediction performance are discussed....
Bayesian Estimation Theory: Basic Definitions Bayesian Estimation The Estimate–Maximise Method Cramer–Rao Bound on the Minimum Estimator Variance Design of Mixture Gaussian Models Bayesian Classification Modeling the Space of a Random Process Summary
ayesian estimation is a framework for the formulation of statistical inference problems. In the prediction or estimation of a random process from a related observation signal, the Bayesian philosophy is based on combining the evidence contained in the signal with prior knowledge of the probability distribution of the process.
So far, economic ﬂuctuations have been predicted almost exclusively through the aggregate infor-
mation conveyed either by i) macro variables (labor market conditions, money, credit, lagged growth),
ii) ﬁnancial indicators (aggregate stock market returns and variances, slope of the yield curve, credit
spreads) or iii) conﬁdence (households or business) indicators.