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Given observations on a stationary economie vector time series process we show that the best % periods ahead forecast (best in the sense of having minimal forecast error variance) of one of the variables can be consistently estimated by nonparametric regression on an ARMA memory index. Our approach is based on a combination of the ARMA memory index modeling approach of Bierens (1986a) with a mo...

of a known kernel k(ω, λ). The problem is difficult in part because the integral operator K : Γ 7→ G is smoothing, making the “inverse problem” K−1 : G 7→ Γ ill-posed in the sense that small changes in G may be associated with large changes in Γ. The most common approaches to solving Equation (1) for the unknown Γ begin by approximating this infinite-dimensional continuous problem with the fini...

Motivated by the problems in genomics, astronomy and some other emerging fields, multiple hypothesis testing has come to the forefront of statistical research in the recent years. In the context of multiple testing, new error measures such as the false discovery rate (FDR) occupy important roles comparable to the role of type I error in classical hypothesis testing. Assuming that a random mecha...

Recent literature has started to explore the use of nonparametric methods to estimate alphas and betas in the conditional CAPM and conditional multifactor models. This paper explores two of the most recent contributions and proposes a third method. Nonparametric estimation of factor modeling involves choosing techniques which are different both technically and in application, but common in the ...

Data Envelopment Analysis (DEA) is known as a nonparametric mathematical programming approach to productive efficiency analysis. In this paper we show that DEA can be alternatively interpreted as nonparametric least squares regression subject to shape constraints on frontier and sign constraints on residuals. This reinterpretation reveals the classic parametric programming model by Aigner and C...

Requiring only minimal assumptions for validity, nonparametric permutation testing provides a flexible and intuitive methodology for the statistical analysis of data from functional neuroimaging experiments, at some computational expense. Introduced into the functional neuroimaging literature by Holmes et al. ([1996]: J Cereb Blood Flow Metab 16:7-22), the permutation approach readily accounts ...

This paper compares various nonparametric models for the estimation of farm specific marginal costs function in the dairy sector. Specifically, locally weighted regression approaches using theory-consistent cost function frameworks as polynomials in the nonparametric approach are applied. A comparison of average marginal cost levels as well as marginal cost distributions across farms illustrate...

In the statistical literature, the conditional density model specification is commonly used to study regression effects. One attractive model is the semiparametric density ratio model, under which the conditional density function is the product of an unknown baseline density function and a known parametric function containing the covariate information. This model has a natural connection with g...

the frequency response analysis (fra) test has been recognized as one of the sensitive tools available for detecting electrical and mechanical faults inside power transformers. however, there is still no universally systematic interpretation technique for these tests. many research efforts have employed different statistical criteria in order to aid the interpretative capability of the fra, but...