			Home About us Contact

Nonparametric Approach (nonparametric + approach)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	60%
Mathematics and Statistics	40%

Selected Abstracts

A New Nonparametric Approach for Baseline Covariate Adjustment for Two-Group Comparative Studies

BIOMETRICS, Issue 4 2008
Alexander Schacht
Summary We consider two-armed clinical trials in which the response and/or the covariates are observed on either a binary, ordinal, or continuous scale. A new general nonparametric (NP) approach for covariate adjustment is presented using the notion of a relative effect to describe treatment effects. The relative effect is defined by the probability of observing a higher response in the experimental than in the control arm. The notion is invariant under monotone transformations of the data and is therefore especially suitable for ordinal data. For a normal or binary distributed response the relative effect is the transformed effect size or the difference of response probability, respectively. An unbiased and consistent NP estimator for the relative effect is presented. Further, we suggest a NP procedure for correcting the relative effect for covariate imbalance and random covariate imbalance, yielding a consistent estimator for the adjusted relative effect. Asymptotic theory has been developed to derive test statistics and confidence intervals. The test statistic is based on the joint behavior of the estimated relative effect for the response and the covariates. It is shown that the test statistic can be used to evaluate the treatment effect in the presence of (random) covariate imbalance. Approximations for small sample sizes are considered as well. The sampling behavior of the estimator of the adjusted relative effect is examined. We also compare the probability of a type I error and the power of our approach to standard covariate adjustment methods by means of a simulation study. Finally, our approach is illustrated on three studies involving ordinal responses and covariates. [source]

A Global View on Parametric and Nonparametric Approaches to the Analysis of Ordered Categorical Data

BIOMETRICAL JOURNAL, Issue 1 2004
Ullrich Munzel
Abstract Rank approaches are very common in the analysis of ordered categorical data but can only be interpreted on an experiment-wise level. Therefore, parametric tests from linear models, although based on metric structures, are used frequently to analyze this type of data. So the questions arise 1. what parametric tests measure in this context and 2. whether the rank approach could be modified to achieve a global level of interpretation. A possible solution to question 2. offers the so called ridit approach, which is based on known reference distributions. In this paper we discuss a global view that shows how rank analysis and ridit analyses are related and how parametric procedures fit into the same framework. The use of the uniform distribution as a reference in the ridit approach gives an explanation to question 1. The asymptotic multivariate normality of the effect estimators is shown and robust test statistics are discussed. Type I and type II error rates are examined in simulation studies and the approach is applied to a toxicological example. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

The economic value of technical trading rules: a nonparametric utility-based approach

INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, Issue 1 2005
Hans Dewachter
Abstract We adapt Brandt's (1999) nonparametric approach to determine the optimal portfolio choice of a risk averse foreign exchange investor who uses moving average trading signals as the information instrument for investment opportunities. Additionally, we assess the economic value of the estimated optimal trading rules based on the investor's preferences. The approach consists of a conditional generalized method of moments (GMM) applied to the conditional Euler optimality conditions. The method presents two main advantages: (i) it avoids ad hoc specifications of statistical models used to explain return predictability; and (ii) it implicitly incorporates all return moments in the investor's expected utility maximization problem. We apply the procedure to different moving average trading rules for the German mark,US dollar exchange rate for the period 1973,2001. We find that technical trading rules are partially recovered and that the estimated optimal trading rules represent a significant economic value for the investor. Copyright © 2005 John Wiley & Sons, Ltd. [source]

Alternative tilts for nonparametric option pricing

THE JOURNAL OF FUTURES MARKETS, Issue 10 2010
M. Ryan Haley
This study generalizes the nonparametric approach to option pricing of Stutzer, M. (1996) by demonstrating that the canonical valuation methodology introduced therein is one member of the Cressie,Read family of divergence measures. Alhough the limiting distribution of the alternative measures is identical to the canonical measure, the finite sample properties are quite different. We assess the ability of the alternative divergence measures to price European call options by approximating the risk-neutral, equivalent martingale measure from an empirical distribution of the underlying asset. A simulation study of the finite sample properties of the alternative measure changes reveals that the optimal divergence measure depends upon how accurately the empirical distribution of the underlying asset is estimated. In a simple Black,Scholes model, the optimal measure change is contingent upon the number of outliers observed, whereas the optimal measure change is a function of time to expiration in the stochastic volatility model of Heston, S. L. (1993). Our extension of Stutzer's technique preserves the clean analytic structure of imposing moment restrictions to price options, yet demonstrates that the nonparametric approach is even more general in pricing options than originally believed. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:983,1006, 2010 [source]

Hedging and value at risk: A semi-parametric approach

THE JOURNAL OF FUTURES MARKETS, Issue 8 2010
Zhiguang Cao
The non-normality of financial asset returns has important implications for hedging. In particular, in contrast with the unambiguous effect that minimum-variance hedging has on the standard deviation, it can actually increase the negative skewness and kurtosis of hedge portfolio returns. Thus, the reduction in Value at Risk (VaR) and Conditional Value at Risk (CVaR) that minimum-variance hedging generates can be significantly lower than the reduction in standard deviation. In this study, we provide a new, semi-parametric method of estimating minimum-VaR and minimum-CVaR hedge ratios based on the Cornish-Fisher expansion of the quantile of the hedged portfolio return distribution. Using spot and futures returns for the FTSE 100, FTSE 250, and FTSE Small Cap equity indices, the Euro/US Dollar exchange rate, and Brent crude oil, we find that the semiparametric approach is superior to the standard minimum-variance approach, and to the nonparametric approach of Harris and Shen (2006). In particular, it provides a greater reduction in both negative skewness and excess kurtosis, and consequently generates hedge portfolios that in most cases have lower VaR and CVaR. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:780,794, 2010 [source]

Estimating financial risk measures for futures positions: A nonparametric approach

THE JOURNAL OF FUTURES MARKETS, Issue 7 2010
John Cotter
This study presents nonparametric estimates of spectral risk measures (SRM) applied to long and short positions in five prominent equity futures contracts. It also compares these to estimates of two popular alternative measures, the Value-at-Risk and Expected Shortfall. The SRMs are conditioned on the coefficient of absolute risk aversion, and the latter two are conditioned on the confidence level. Our findings indicate that all risk measures increase dramatically and their estimators deteriorate in precision when their respective conditioning parameter increases. Results also suggest that estimates of SRMs and their precision levels are of comparable orders of magnitude as those of more conventional risk measures. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:689,703, 2010 [source]

Pricing American options by canonical least-squares Monte Carlo

THE JOURNAL OF FUTURES MARKETS, Issue 2 2010
Qiang Liu
Options pricing and hedging under canonical valuation have recently been demonstrated to be quite effective, but unfortunately are only applicable to European options. This study proposes an approach called canonical least-squares Monte Carlo (CLM) to price American options. CLM proceeds in three stages. First, given a set of historical gross returns (or price ratios) of the underlying asset for a chosen time interval, a discrete risk-neutral distribution is obtained via the canonical approach. Second, from this canonical distribution independent random samples of gross returns are taken to simulate future price paths for the underlying. Third, to those paths the least-squares Monte Carlo algorithm is then applied to obtain early exercise strategies for American options. Numerical results from simulation-generated gross returns under geometric Brownian motions show that the proposed method yields reasonably accurate prices for American puts. The CLM method turns out to be quite similar to the nonparametric approach of Alcock and Carmichael and simulations done with CLM provide additional support for their recent findings. CLM can therefore be viewed as an alternative for pricing American options, and perhaps could even be utilized in cases when the nature of the underlying process is not known. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:175,187, 2010 [source]

Testing the martingale hypothesis for futures prices: Implications for hedgers

THE JOURNAL OF FUTURES MARKETS, Issue 11 2008
Cédric de Ville de Goyet
The martingale hypothesis for futures prices is investigated using a nonparametric approach where it is assumed that the expected futures returns depend (nonparametrically) on a linear combination of predictors. We first collapse the predictors into a single-index variable where the weights are identified up to scale, using the average derivative estimator proposed by T. Stoker (1986). We then use the Nadaraya,Watson kernel estimator to calculate (and visually depict) the relationship between the estimated index and the expected futures returns. We discuss implications of this finding for a noninfinitely risk-averse hedger. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:1040,1065, 2008 [source]

Detecting Genomic Aberrations Using Products in a Multiscale Analysis

BIOMETRICS, Issue 3 2010
Xuesong Yu
Summary Genomic instability, such as copy-number losses and gains, occurs in many genetic diseases. Recent technology developments enable researchers to measure copy numbers at tens of thousands of markers simultaneously. In this article, we propose a nonparametric approach for detecting the locations of copy-number changes and provide a measure of significance for each change point. The proposed test is based on seeking scale-based changes in the sequence of copy numbers, which is ordered by the marker locations along the chromosome. The method leads to a natural way to estimate the null distribution for the test of a change point and adjusted,p -values for the significance of a change point using a step-down maxT permutation algorithm to control the family-wise error rate. A simulation study investigates the finite sample performance of the proposed method and compares it with a more standard sequential testing method. The method is illustrated using two real data sets. [source]

Adjustment for Missingness Using Auxiliary Information in Semiparametric Regression

BIOMETRICS, Issue 1 2010
Donglin Zeng
Summary In this article, we study the estimation of mean response and regression coefficient in semiparametric regression problems when response variable is subject to nonrandom missingness. When the missingness is independent of the response conditional on high-dimensional auxiliary information, the parametric approach may misspecify the relationship between covariates and response while the nonparametric approach is infeasible because of the curse of dimensionality. To overcome this, we study a model-based approach to condense the auxiliary information and estimate the parameters of interest nonparametrically on the condensed covariate space. Our estimators possess the double robustness property, i.e., they are consistent whenever the model for the response given auxiliary covariates or the model for the missingness given auxiliary covariate is correct. We conduct a number of simulations to compare the numerical performance between our estimators and other existing estimators in the current missing data literature, including the propensity score approach and the inverse probability weighted estimating equation. A set of real data is used to illustrate our approach. [source]