Search

This paper contributes to nonparametric forecasting techniques by developing three local nonparametric forecasting methods for the nonparametric exclusion-from-core inflation persistence model that are capable of utilizing revised real-time personal consumption expenditure and core personal consumption expenditure for 62 vintages. Local nonparametric forecasting provides forecasters with a way of parsing the data by permitting a low inflation measure to be included in other low inflationary time periods and vice versa. Furthermore, when examining real-time data, policy-makers can use the nonparametric models to help identify outliers and potential abnormal economic events and problems with the data such as an underlying change in volatility. The most efficient nonparametric forecasting method is the third model, which uses the flexibility of nonparametrics by making forecasts conditional on the forecasted value, which can be used for counterfactual analysis. Keywords Inflation persistence * Real-time data * Monetary policy * Nonparametrics * Forecasting JEL Classification E52 * C14 * C53, Introduction Financial data and some economic time series data, such as inflation, tend to be from fat-tailed distributions, which should be taken into account in forecasting. Using the kernel-weighted least [...]

Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 x [10.sup.6] [km.sup.2] of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 x 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 [km.sup.2] and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org). Key words: nonparametric method, remote sensing, boreal forest, stand attribute, biomass, composition, national baseline inventory. L'Inventaire forestier national (IFN) du Canada est un programme d'echantillonnage concu pour appuyer la production de rapports sur les forets a l'echelle nationale. D'autre part, les cartes continues d'attributs forestiers sont necessaires pour appuyer les analyses strategiques des problematiques regionales de politique et d'amenagement. Nous avons donc produit des cartes couvrant 4,03 x [10.sup.6] [km.sup.2] de surface forestiere inventoriee en 2001 a partir d'observations normalisees provenant des placettes photos de l'IFN utilisees comme donnees de reference. Nous avons utilise la methode des k plus proches voisins (kNN) avec 26 couches de donnees geospatiales, y compris les donnees spectrales MODIS et les variables climatiques et topographiques, pour produire les cartes de 127 attributs forestiers a une resolution de 250 x 250 m. Les attributs a l'echelle du peuplement comprennent le type de couverture terrestre, la structure et l'abondance relative des especes d'arbres. Dans cet article, nous rapportons seulement la biomasse totale aerienne et vivante des arbres. Tous les autres attributs abordes sont presentes dans la section materiel supplementaire (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr- 2013-0401). En general, les ecarts des valeurs predites a l'echelle du pixel par rapport a celles des placettes photos de validation, sont plus importants dans les regions montagneuses et dans les zones ou, soit la biomasse, soit le taux d'echantillonnage de placettes photos est faible. Les valeurs predites a l'echelle du pixel sont surestimees lorsque les valeurs observees sont petites et sous-estimees lorsqu'elles sont grandes. Les mesures de precision sont ameliorees grace a l'agregation spatiale des pixels sur 1 [km.sup.2] et plus. Dans l'ensemble, ces nouveaux produits fournissent des informations de base uniques pour les analyses strategiques des forets (https://nfi.nfis.org). [Traduit par la Redaction] Mots-cles: methode non parametrique, teledetection, foret boreale, attribut de peuplement, biomasse, composition, inventaire national de reference., Introduction Canada's forests and wooded lands cover 40% of its territory and constitute one-tenth of the world's forest area (National Forest Inventory 2010). Provincial and territorial governments are responsible for [...]

We introduce a semiparametric procedure for more efficient prediction of a strictly stationary process admitting an ARMA representation. The procedure is based on the estimation of the ARMA representation, followed by a nonparametric regression where the ARMA residuals are used as explanatory variables. Compared to standard nonparametric regression methods, the number of explanatory variables can be reduced because our approach exploits the linear dependence of the process. We establish consistency and asymptotic normality results for our estimator. Numerical experiments show that significant gains can be achieved with our approach. All the supplemental materials used by this article are available online. KEY WORDS: ARMA representation; Noisy data; Nonparametric regression; Optimal prediction.

We consider the generalized additive model when responses from the same cluster are correlated. Incorporating correlation in the estimation of nonparametric components for the generalized additive model is important because it improves estimation efficiency and increases statistical power for model selection. In our setting, there is no specified likelihood function for the generalized additive model, because the outcomes could be nonnormal and discrete, which makes estimation and model selection very challenging problems. We propose consistent estimation and model selection that incorporate the correlation structure. We establish an asymptotic property with [L.sub.2]-norm consistency for the nonparametric components, which achieves the optimal rate of convergence. In addition, the proposed model selection strategy is able to select the correct generalized additive model consistently. That is, with probability approaching to 1, the estimators for the zero function components converge to 0 almost surely. We illustrate our method using numerical studies with both continuous and binary responses, along with a real data application of binary periodontal data. Supplemental materials including technical details are available online. KEY WORDS: [L.sub.2]-norm consistency; Model selection; Nonparametric function; Oracle properly; Polynomial spline; Quadratic inference function; SCAD.

In this paper we develop a nonparametric approach to clustering very high-dimensional data, designed particularly for problems where the mixture nature of a population is expressed through multimodality of its density. Therefore, a technique based implicitly on mode testing can be particularly effective. In principle, several alternative approaches could be used to assess the extent of multimodality, but in the present problem the excess mass method has important advantages. We show that the resulting methodology for determining clusters is particularly effective in cases where the data are relatively heavy tailed or show a moderate to high degree of correlation, or when the number of important components is relatively small. Conversely, in the case of light-tailed, almost-independent components when there are many clusters, clustering in terms of modality can be less reliable than more conventional approaches. This article has supplementary material online. KEY WORDS: Bandwidth test; Bootstrap; Density estimation; Excess mass; Mode test; Multimodality.

As an alternative to the local partial likelihood method of Tibshirani and Hastie and Fan, Gijbels, and King, a global partial likelihood method is proposed to estimate the covariate effect in a nonparametric proportional hazards model, [gamma](t|x) = exp{[psi](x)}[[gamma].sub.0](t). The estimator, [^.[psi]](x), reduces to the Cox partial likelihood estimator if the covariate is discrete. The estimator is shown to be consistent and semiparametrically efficient for linear functionals of [psi](x). Moreover, Breslow-type estimation of the cumulative baseline hazard function, using the proposed estimator [^.[psi]](x), is proved to be efficient. The asymptotic bias and variance are derived under regularity conditions. Computation of the estimator involves an iterative but simple algorithm. Extensive simulation studies provide evidence supporting the theory. The method is illustrated with the Stanford heart transplant data set. The proposed global approach is also extended to a partially linear proportional hazards model and found to provide efficient estimation of the slope parameter. This article has the supplementary materials online. KEY WORDS: Cox model; Local linear smoothing; Local partial likelihood; Semiparametric efficiency.

We consider a local post-stratification approach to analyze the capture-recapture dual system Accuracy and Coverage Evaluation (A.C.E.) data associated with the 2000 U.S. Census. The local post-stratification is carried out via a nonparametric regression estimation of the census enumeration and the correct enumeration functions. We propose a nonparametric population size estimator that is designed to accommodate some key aspects of the A.C.E: missing values, erroneous enumerations, and extra covariates affecting the missingness and correct enumeration. The resulting estimates are compared with estimates from a conventional post-stratification and a logistic regression approach in an analysis on the 2000 Census A.C.E. data. KEY WORDS: Capture-recapture: Erroneous enumeration; Kernel smoothing; Missing values; Population size estimation.

In this paper, we develop a new framework for face recognition based on nonparametric discriminant analysis (NDA) and multiclassifier integration. Traditional LDA-based methods surfer a fundamental limitation originating from the parametric nature of scatter matrices, which are based on the Gaussian distribution assumption. The performance of these methods notably degrades when the actual distribution is non-Gaussian. To address this problem, we propose a new formulation of scatter matrices to extend the two-class NDA to multiclass cases. Then, in order to exploit the discriminant information in both the principal space and the null space of the intraclass scatter matrix, we develop two improved multiclass NDA-based algorithms (NSA and NFA) with each one having two complementary methods that are based on the principal space and the null space of the intraclass scatter matrix, respectively. Comparing to the NSA, the NFA is more effective in the utilization of the classification boundary information, in order to exploit the complementary nature of the two kinds of NFA (PNFA and NNFA), we finally develop a dual NFA-based multiclassifier fusion framework by employing the overcomplete Gabor representation for face images to boost the recognition performance. We show the improvements of the developed new algorithms over the traditional subspace methods through comparative experiments on two challenging face databases, the Purdue AR database and the XM2VTS database. Index Terms--Face recognition, classifier design and evaluation, nonparametric discriminant analysis (NDA), multiclassifier fusion.

Nonparametric varying-coefficient models are commonly used for analyzing data measured repeatedly over time, including longitudinal and functional response data. Although many procedures have been developed for estimating varying coefficients, the problem of variable selection for such models has not been addressed to date. In this article we present a regularized estimation procedure for variable selection that combines basis function approximations and the smoothly clipped absolute deviation penalty. The proposed procedure simultaneously selects significant variables with time-varying effects and estimates the nonzero smooth coefficient functions. Under suitable conditions, we establish the theoretical properties of our procedure, including consistency in variable selection and the oracle property in estimation. Here the oracle property means that the asymptotic distribution of an estimated coefficient function is the same as that when it is known a priori which variables are in the model. The method is illustrated with simulations and two real data examples, one for identifying risk factors in the study of AIDS and one using microarray time-course gene expression data to identify the transcription factors related to the yeast cell-cycle process. KEY WORDS: Functional response; Longitudinal data; Nonparametric function estimation: Oracle property: Regularized estimation.

Common 3D acquisition techniques, such as laser scanning and stereo capture, are realistically only 2.5D in nature. Here, we consider the automated completion of hidden or missing portions in 3D scenes originally acquired from 2.5D (or 3D) capture. We propose an approach based on the nonparametric propagation of available scene knowledge from the known (visible) scene areas to these unknown (invisible) 3D regions in conjunction with an initial underlying geometric surface completion. Index Terms--Image processing, occlusion, range data, surface fitting, texture.

Recently, Nonparametric (NP) Windows has been proposed to estimate the statistics of real 1D and 2D signals. NP Windows is accurate because it is equivalent to sampling images at a high (infinite) resolution for an assumed interpolation model. This paper extends the proposed approach to consider joint distributions of image pairs. Second, Green's Theorem is used to simplify the previous NP Windows algorithm. Finally, a resolution-aware NP Windows algorithm is proposed to improve robustness to relative scaling between an image pair. Comparative testing of 2D image registration was performed using translation only and affine transformations. Although it is more expensive than other methods, NP Windows frequently demonstrated superior performance for bias (distance between ground truth and global maximum) and frequency of convergence. Unlike other methods, the number of samples and the number of bins have little effect on NP Windows and the prior selection of a kernel is not required. Index Terms--Mutual information, joint image statistics, registration, sampling.

Recent development and growth accounting studies have established that total factor productivity is an important source of cross-country differences in income levels and growth rates. This paper makes two contributions. First, it examines the sensitivity of the development accounting results to the Cobb-Douglas specification of the production function. Second, within the Cobb-Douglas framework, it weighs evidence of the two alternative explanations of total factor productivity differences: the inefficiency view and the appropriate technology view. To accomplish these tasks, the world production frontier is estimated using a nonparametric deterministic approach known as data envelopment analysis. I find that the fraction of income differences explained by physical and human capital increases from 32% to 55% when departing from the Cobb-Douglas assumption. There is also evidence consistent with the appropriate technology view; countries with an inadequate mix of inputs are unable to access the most productive technologies. Moreover, the world technology frontier appears to be shifting out faster at input combinations close to that of the R&D leader. However, inefficiency appears to be the main explanation for low incomes throughout the world; it explains 43% of output variation in 1995, and its importance has increased over time. JEL classification: E23; O11; O33; O47 Keywords: TFP differences; Development accounting; Efficiency; Appropriate technology

The article introduces an algorithm for the correction of pileup phenomenon in gamma spectrometry. The pileup phenomenon is inverted in order to give a nonparametric estimator of the individual energy distribution.

Active-contours or snakes usually require parameters to determine the form of the external force and the nonparametric formulation is examined in the article.

We present a novel space-time patch-based method for image sequence restoration. We propose an adaptive statistical estimation framework based on the local analysis of the bias-variance trade-off. At each pixel, the space-time neighborhood is adapted to improve the performance of the proposed patch-based estimator. The proposed method is unsupervised and requires no motion estimation. Nevertheless, it can also be combined with motion estimation to cope with very large displacements due to camera motion. Experiments show that this method is able to drastically improve the quality of highly corrupted image sequences. Quantitative evaluations on standard artificially noise-corrupted image sequences demonstrate that our method outperforms other recent competitive methods. We also report convincing results on real noisy image sequences. Index Terms--Image sequence restoration, denoising, nonparametric estimation, nonlinear filtering, bias-variance trade-off.

In this article we study a class of semiparametric transformation models with random effects for the intensity function of the counting process. These models provide considerable flexibility in formulating the effects of possibly time-dependent covariates on the developments of recurrent events while accounting for the dependence of the recurrent event times within the same subject. We show that the nonparametric maximum likelihood estimators (NPMLEs) for the parameters of these models are consistent and asymptotically normal. The limiting covariance matrices for the estimators of the regression parameters achieve the semiparametric efficiency bounds and can be consistently estimated. The limiting covariance function for the estimator of any smooth functional of the cumulative intensity function also can be consistently estimated. We develop a simple and stable EM algorithm to compute the NPMLEs as well as the variance and covariance estimators. Simulation studies demonstrate that the proposed methods perform well in practical situations. Two medical studies are provided for illustrations. KEY WORDS: Box-Cox transformation; Counting process; EM algorithm; Intensity function; Nonparametric likelihood; Semiparametric efficiency.

This article considers a wide class of semiparametric regression models in which interest focuses on population-level quantities that combine both the parametric and the nonparametric parts of the model. Special cases in this approach include generalized partially linear models, generalized partially linear single-index models, structural measurement error models, and many others. For estimating the parametric part of the model efficiently, profile likelihood kernel estimation methods are well established in the literature. Here our focus is on estimating general population-level quantities that combine the parametric and nonparametric parts of the model (e.g., population mean, probabilities, etc.), We place this problem in a general context, provide a general kernel-based methodology, and derive the asymptotic distributions of estimates of these population-level quantities, showing that in many cases the estimates are semiparametric efficient. For estimating the population mean with no missing data, we show that the sample mean is semiparametric efficient for canonical exponential families, but not in general. We apply the methods to a problem in nutritional epidemiology, where estimating the distribution of usual intake is of primary interest and semiparametric methods are not available. Extensions to the case of missing response data are also discussed. KEY WORDS: Generalized estimating equations; Kernel methods; Measurement error; Missing data; Nonparametric regression; Nutrition; Partially linear model; Profile method; Semiparametric efficient score; Semiparametric information bound; Single-index models.

This paper proposes a new approach to handle nonparametric stochastic frontier (SF) models. It is based on local maximum likelihood techniques. The model is presented as encompassing some anchorage parametric model in a nonparametric way. First, we derive asymptotic properties of the estimator for the general case (local linear approximations). Then the results are tailored to a SF model where the convoluted error term (efficiency plus noise) is the sum of a half normal and a normal random variable. The parametric anchorage model is a linear production function with a homoscedastic error term. The local approximation is linear for both the production function and the parameters of the error terms. The performance of our estimator is then established in finite samples using simulated data sets as well as with a cross-sectional data on US commercial banks. The methods appear to be robust, numerically stable and particularly useful for investigating a production process and the derived efficiency scores. JEL classification: C13; C14 Keywords: Stochastic cost frontier; Banking; Nonparametric; Local maximum likelihood

We investigate India's widening skill wage gap and narrowing gender wage differential during the two decades that coincide with the economic liberalization in the country. Using the nonparametric methodology developed by Katz and Murphy [Katz, L. F., & Murphy, K. M. (1992). Changes in relative wages, 1963-87: supply and demand factors. Quarterly Journal of Economics, 107(1), 35-78], we find that relative demand shifts contributed to relative wage shifts and that increases in the demand for skilled labor were mostly due to skill upgrading within industries. In assessing the contribution of external sector reforms to demand for skilled labor, we find that international trade-in manufactures benefited skilled men but hurt skilled women, whereas outsourcing of services generated a demand for both female and male college graduates. Key words--trade and labor market interactions, education, wages, Asia, India

A study evaluates mutual funds by making a frontier analysis and using a nonparametric approach to the subject. Economies of scale is considered in the study.

Forecast intervals generalize point forecasts to represent and incorporate uncertainty. Forecast intervals calculated from dynamic models typically sidestep the issue of parameter estimation. This paper shows how to construct asymptotic forecast intervals which incorporate the uncertainty due to parameter estimation. Our proposed solution is a simple proportional adjustment to the interval endpoints, the adjustment factor depending on the asymptotic variance of the interval estimates. Our analysis is in the context of a forecasting equation with an error independent of the forecasting variables but with unknown distribution. The methods are illustrated with a simulation experiment and an application to the US monthly unemployment rate. JEL classification: C53 Keywords: Forecast intervals; Nonparametric; Quantile; Estimation

Nonparametric approaches for estimating the number of unique sequences in a biological tissue, cDNA library or amplified cDNA are considered and by introducing moments of species' abundance in a population, the relative abundance of species is analyzed. It is found that the lower bound estimator yields a big bias when applied to gene expression data, which has relatively large coefficients of variation and small sample sizes.

Usage of nonparametric model to study over rejection observed in Granger non-causality is presented.

A study makes an empirical analysis of per capita carbon dioxide emissions and per capita gross domestic product. A nonparametric panel approach is used in the study.

A study on forecasting exchange rates of Canadian and United States dollars, using non-parametric method, is examined.

The problem of detecting changes in the distribution of alarmed sensors is considered and several detection and estimation algorithms are presented based on the Vapnik-Chervonenkis (VC) theory, using a nonparametric change detection framework. Recursive algorithms for the efficient computation of test statistics are derived, and the estimation problem is also considered in which, after detection is made, the location with maximum distribution change is estimated.

A nonparametric method is developed for evaluating the form of natural selection on a quantitative trait using mark-recapture data; survival is investigated in relation to body mass. It is found that lighter individuals showed a reduction in survival, however, survival function was not symmetric, and indicating that body mass might not be under stabilizing selection.

The paper analyzes a number of competing approaches to modeling efficiency in panel studies. The specifications considered include the fixed effects stochastic frontier, the random effects stochastic frontier, the Hausman-Taylor random effects stochastic frontier, and the random and fixed effects stochastic frontier with an AR(1) error. I have summarized the foundations and properties of estimators that have appeared elsewhere and have described the model assumptions under which each of the estimators have been developed. I discuss parametric and nonparametric treatments of time varying efficiency including the Battese Coelli estimator and linear programming approaches to efficiency measurement. Monte Carlo simulation is used to compare the various estimators and to assess their relative performances under a variety of misspecified settings. A brief illustration of the estimators is conducted using U.S. banking data. JEL classification: C14; D24 Keywords: Nonparametric methods; Productivity; Frontier production

In recent years, a lot of econometric literature has been devoted to estimating time varying coefficients in regression models. Here, a new method based oil smoothers is proposed, which is able to introduce shape restrictions over tile coefficients. The statistical properties of the estimator are obtained for very general situations, including locally stationary regressors. In particular, tile procedure provides consistent results for time varying autoregressive models. The practical problem of implementation is also addressed. A data-driven method for selecting the control parameters is provided, together with an algorithm that reduces the computational cost. A simulation study and an application to real data supports the theoretical results. JEL classification: c14; c22 Keywords: Nonparametric regression; Kernel estimators: Time varying coefficients; Seasonality: Local stationarity

We propose a hybrid estimation procedure that combines the least squares and nonparametric methods to estimate change points of volatility in time series models. Its main advantage is that it does not require any specific form of marginal or transitional densities of tile process. We also establish tile asymptotic properties of the estimators when the regression and conditional volatility functions are not known. The proposed tests for change points of volatility are shown to be consistent and more powerful than the nonparametric ones in the literature. Finally, we provide simulations and empirical results using the Hong Kong stock market index (HS1) series. JEL classification: C1; C22; C5 Keywords: Change points in volatility; Least squares; Nonparametric estimation: Asymptotic properties

The maximal achievable level of output for a given level of inputs defines the production frontier that can serve as benchmark to evaluate individual firm efficiencies. Nonparametric envelopment estimators (free disposal hull, data envelopment analysis) have been mostly used because they rely on very few assumptions, whereas parametric forms for the frontier allow for richer economic interpretation. Most of the parametric approaches rely on standard regression fitting the shape of the center of the cloud of points. In this paper, we investigated a new approach, which captures the shape of the cloud points near its boundary. It offers parametric approximations of nonparametric frontiers. We provide the statistical theory (asymptotic). Some simulated examples show the advantages of our method compared with the usual regression-type estimators. JEL classification: C13: C14: D20 Keywords: Production function; Frontier: Nonparametric estimation

The authors create an algorithm for obtaining weights of signal filtering and extration which can replace actual observation and measurement.

A positive Lyapunov exponent is one practical definition of chaos. We develop a formal test for chaos in a noisy system based on the consistent standard errors of the nonparametric Lyapunov exponent estimators. For international real output series, the hypothesis of the positive Lyapunov exponent is significantly rejected in many cases. One possible interpretation of this result is that the traditional exogenous models are better able to explain business cycle fluctuations than is the chaotic endogenous approach. However, our results are subject to a number of caveats.

This paper considers a semi-nonparametric cointegration test. The test uses the LM-testing principle. The score function needed for the LM-test is estimated from the data using an expansion of the density around a Student t distribution. In this way, we capture both the possible fat-tailedness and the skewness of the innovation process. Using a Monte Carlo experiment, we show that the semi-nonparametric cointegration test has good size and power properties over a broad class of distributions for the innovation process. We also investigate the effect of order selection of the underlying VAR on inference. The complete methodology is illustrated using an interest rate example. JEL classification: C32; C14; C12 Keywords: Cointegration; Semi-nonparametric estimation; Fat tails; Skewness; Term structure

Most nonparametric methods for estimating production frontiers (data envelopment analysis and free disposal hall (FDH)) are based on envelopment techniques. Statistical inference based on these estimators is available but, by construction, they are very sensitive to extreme values or outliers. We propose a nonparametric estimator, which is more robust to these extreme values. It is based on a concept of expected minimum input function (or expected maximal output function). We show how this function is related to the efficient frontier itself. The resulting estimator is related to the FDH estimator but it will not envelop all the data. The asymptotic theory is provided. Our approach includes the multiple input and multiple output cases. JEL classification: C13; C14; D20 Keywords: Production function; Cost function; Efficiency analysis; Expected maximum production function; Expected minimum cost frontier; Nonparametric estimation

A non-parametric estimate is used to define a new performance index for mutual funds. This estimate controls for scale economies and provides benchmarks, and it is applicable to all but income funds.

A new use of the nonparametric statistic, referred to as the "Kruskal and Wallis rank test", is proposed in this study. The nonparametric statistic examines whether or not any frontier shift occurs among observed periods. To document its practicality, the proposed statistic is incorporated into the framework of Window Malmquist Analysis (WMA) that is structured by combining Data Envelopment Analysis (DEA) window analysis with the Malmquist index approach. As an important case study, this research applies the new technique to examine the performance of Japanese postal services from 1983 to 1997. Two policy implications are derived from the empirical study.

This paper introduces a nonparametric Granger-causality test for covariance stationary linear processes under, possibly, the presence of long-range dependence. We show that the test is consistent and has power against contiguous alternatives converging to the parametric rate T(super -1/2). Since the test is based on estimates of the parameters of the representation of a VAR model as a, possibly, two-sided infinite distributed lag model, we first show that a modification of Hannan's (1963, 1967) estimator is root-T consistent and asymptotically normal for the coefficients of such a representation. When the data are long-range dependent, this method of estimation becomes more attractive than least squares, since the latter can be neither root-T consistent nor asymptotically normal as is the case with short-range dependent data.

Many researchers use categorical data analysis to recover individual consumption preferences, but the standard discrete choice models require restrictive assumptions. To improve the flexibility of discrete choice data analysis, we propose a nonparametric multiple choice model that applies the penalized likelihood method within the random utility framework. We show that the deterministic component of the random utility function in the model is a cubic smoothing spline function. The method subsumes the conventional conditional logit model (McFadden, 1973, in: Zarembka, P., (Ed.), Frontiers in Econometrics) as a special case. In this paper, we present the model, describe the estimator, provide the computational algorithm of the model, and demonstrate the model by applying it to nonmarket valuation of recreation sites.

A method for the selection of flexible manufacturing systems is proposed, based on combined application of data envelopment analysis and nonparametric statistical techniques.

This paper proposes a general approach and a computationally convenient estimation procedure for the structural analysis of auction data. Considering first-price sealed-bid auction models within the independent private value paradigm, we show that the underlying distribution of bidders' private values is identified from observed bids and the number of actual bidders without any parametric assumptions. Using the theory of minimax, we establish the best rate of uniform convergence at which the latent density of private values can be estimated nonparametrically from available data. We then propose a two-step kernel-based estimator that converges at the optimal rate.