The Experts below are selected from a list of 17823 Experts worldwide ranked by ideXlab platform

Frank P. A. Coolen - One of the best experts on this subject based on the ideXlab platform.

Tahani Coolen-maturi - One of the best experts on this subject based on the ideXlab platform.

  • Nonparametric Predictive Inference for American option pricing based on the binomial tree model
    Communications in Statistics - Theory and Methods, 2020
    Co-Authors: Frank P. A. Coolen, Tahani Coolen-maturi
    Abstract:

    In this article, we present the American option pricing procedure based on the binomial tree from an imprecise statistical aspect. Nonparametric Predictive Inference (NPI) is implemented to infer i...

  • On nonparametric Predictive Inference for asset and European option trading in the binomial tree model
    Journal of the Operational Research Society, 2019
    Co-Authors: Junbin Chen, Frank P. A. Coolen, Tahani Coolen-maturi
    Abstract:

    This article introduces a novel method for asset and option trading in a binomial scenario. This method uses nonparametric Predictive Inference (NPI), a statistical methodology within imprecise pro...

  • Introducing Nonparametric Predictive Inference Methods for Reproducibility of Likelihood Ratio Tests
    Journal of Statistical Theory and Practice, 2018
    Co-Authors: Filipe J. Marques, Frank P. A. Coolen, Tahani Coolen-maturi
    Abstract:

    This paper introduces the nonparametric Predictive Inference approach for reproducibility of likelihood ratio tests. The general idea of this approach is outlined for tests between two simple hypotheses, followed by an investigation of reproducibility for tests between two beta distributions. The paper reports on the first steps of a wider research programme towards tests involving composite hypotheses and substantial computational challenges.

  • Non‐parametric Predictive Inference for the validation of credit rating systems
    Journal of the Royal Statistical Society: Series A (Statistics in Society), 2018
    Co-Authors: Tahani Coolen-maturi, Frank P. A. Coolen
    Abstract:

    Credit rating or credit scoring systems are important tools for estimating the obligor's creditworthiness and for providing an indication of the obligor's future status. The discriminatory power of a credit rating or credit scoring system refers to its ex ante ability to distinguish between two or more classes of borrowers. One of the most popular tools for the validation of the power of credit rating or credit scoring models to distinguish between two (or more) classes of borrowers is the receiver operating characteristic (ROC) curve (hypersurface) and its widely used overall summary, the area (hypervolume) under the curve (hypersurface). As the end goal of building such models is to predict and quantify uncertainty about future loans, prediction methods are especially valuable in this context. For this, non‐parametric Predictive Inference is a promising candidate for such Inference as it is a frequentist statistical method that is explicitly aimed at using few modelling assumptions, enabled through the use of lower and upper probabilities to quantify uncertainty. The aim of the paper is to introduce non‐parametric Predictive Inference for ROC analysis within a banking context, for which novel results on ROC hypersurfaces for more than three groups are presented. Examples are provided to illustrate the method.

  • Nonparametric Predictive Inference for future order statistics.
    Communications in Statistics - Theory and Methods, 2017
    Co-Authors: Frank P. A. Coolen, Tahani Coolen-maturi, Hana N. Alqifari
    Abstract:

    This paper presents nonparametric Predictive Inference for future order statistics. Given data consisting of n real-valued observations, m future observations are considered and Predictive probabilities are presented for the r-th ordered future observation. In addition, joint and conditional probabilities for events involving multiple future order statistics are presented. The paper further presents the use of such Predictive probabilities for order statistics in statistical Inference, in particular considering pairwise and multiple comparisons based on two or more independent groups of data.

Tahani Coolenmaturi - One of the best experts on this subject based on the ideXlab platform.

  • nonparametric Predictive Inference for diagnostic test thresholds
    Communications in Statistics-theory and Methods, 2020
    Co-Authors: Tahani Coolenmaturi, Frank P. A. Coolen, Manal H Alabdulhadi
    Abstract:

    Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine, machine learning and credit scoring. The receiver operating characteristic (ROC) curve and surfac...

  • non parametric Predictive Inference for the validation of credit rating systems
    Journal of The Royal Statistical Society Series A-statistics in Society, 2019
    Co-Authors: Tahani Coolenmaturi, Frank P. A. Coolen
    Abstract:

    Credit rating or credit scoring systems are important tools for estimating the obligor's creditworthiness and for providing an indication of the obligor's future status. The discriminatory power of a credit rating or credit scoring system refers to its ex ante ability to distinguish between two or more classes of borrowers. One of the most popular tools for the validation of the power of credit rating or credit scoring models to distinguish between two (or more) classes of borrowers is the receiver operating characteristic (ROC) curve (hypersurface) and its widely used overall summary, the area (hypervolume) under the curve (hypersurface). As the end goal of building such models is to predict and quantify uncertainty about future loans, prediction methods are especially valuable in this context. For this, non‐parametric Predictive Inference is a promising candidate for such Inference as it is a frequentist statistical method that is explicitly aimed at using few modelling assumptions, enabled through the use of lower and upper probabilities to quantify uncertainty. The aim of the paper is to introduce non‐parametric Predictive Inference for ROC analysis within a banking context, for which novel results on ROC hypersurfaces for more than three groups are presented. Examples are provided to illustrate the method.

  • nonparametric Predictive Inference for european option pricing based on the binomial tree model
    Journal of the Operational Research Society, 2019
    Co-Authors: Frank P. A. Coolen, Tahani Coolenmaturi
    Abstract:

    In finance, option pricing is one of the main topics. A basic model for option pricing is the Binomial Tree Model, proposed by Cox, Ross, and Rubinstein in 1979 (CRR). This model assumes that the underlying asset price follows a binomial distribution with a constant upward probability, the so-called risk-neutral probability. In this paper, we propose a novel method based on the binomial tree. Rather than using the risk-neutral probability, we apply Nonparametric Predictive Inference (NPI) to infer imprecise probabilities of movements, reflecting more uncertainty while learning from data. To study its performance, we price the same European options utilizing both the NPI method and the CRR model and compare the results in two different scenarios, firstly where the CRR assumptions are right, and secondly where the CRR model assumptions deviate from the real market. It turns out that our NPI method, as expected, cannot perform better than the CRR in the first scenario, but can do better in the second scenario.

  • nonparametric Predictive Inference for stock returns
    Journal of Applied Statistics, 2017
    Co-Authors: Rebecca M Baker, Tahani Coolenmaturi, Frank P. A. Coolen
    Abstract:

    In finance, Inferences about future asset returns are typically quantified with the use of parametric distributions and single-valued probabilities. It is attractive to use less restrictive inferential methods, including nonparametric methods which do not require distributional assumptions about variables, and imprecise probability methods which generalize the classical concept of probability to set-valued quantities. Main attractions include the flexibility of the Inferences to adapt to the available data and that the level of imprecision in Inferences can reflect the amount of data on which these are based. This paper introduces nonparametric Predictive Inference (NPI) for stock returns. NPI is a statistical approach based on few assumptions, with Inferences strongly based on data and with uncertainty quantified via lower and upper probabilities. NPI is presented for Inference about future stock returns, as a measure for risk and uncertainty, and for pairwise comparison of two stocks based on their futu...

  • Predictive Inference for best linear combination of biomarkers subject to limits of detection
    Statistics in Medicine, 2017
    Co-Authors: Tahani Coolenmaturi
    Abstract:

    Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine, machine learning and credit scoring. The receiver operating characteristic (ROC) curve is a useful tool to assess the ability of a diagnostic test to discriminate between two classes or groups. In practice, multiple diagnostic tests or biomarkers are combined to improve diagnostic accuracy. Often, biomarker measurements are undetectable either below or above the so-called limits of detection (LoD). In this paper, nonparametric Predictive Inference (NPI) for best linear combination of two or more biomarkers subject to limits of detection is presented. NPI is a frequentist statistical method that is explicitly aimed at using few modelling assumptions, enabled through the use of lower and upper probabilities to quantify uncertainty. The NPI lower and upper bounds for the ROC curve subject to limits of detection are derived, where the objective function to maximize is the area under the ROC curve. In addition, the paper discusses the effect of restriction on the linear combination's coefficients on the analysis. Examples are provided to illustrate the proposed method. Copyright © 2017 John Wiley & Sons, Ltd.

Sulafah Bin Himd - One of the best experts on this subject based on the ideXlab platform.

Joseph Sedransk - One of the best experts on this subject based on the ideXlab platform.

  • Bayesian Predictive Inference For Finite Population Quantities Under Informative Sampling
    arXiv: Methodology, 2018
    Co-Authors: Joseph Sedransk, B. Nandram, Lu Chen
    Abstract:

    We investigate Bayesian Predictive Inference for finite population quantities when there are unequal probabilities of selection. Only limited information about the sample design is available; i.e., only the first-order selection probabilities corresponding to the sample units are known. Our methodology, unlike that of Chambers, Dorfman and Wang (1998), can be used to make Inference for finite population quantities and provides measures of precision and intervals. Moreover, our methodology, using Markov chain Monte Carlo methods, avoids the necessity of using asymptotic closed form approximations, necessary for the other approaches that have been proposed. A set of simulated examples shows that the informative model provides improved precision over a standard ignorable model, and corrects for the selection bias.

  • Bayesian Predictive Inference for the proportion of eroded land in a small area in Iowa
    Environmental and Ecological Statistics, 2002
    Co-Authors: B. Nandram, Joseph Sedransk
    Abstract:

    There is an increasing interest in the quality of soil, especially for small geographical areas. We present a method to estimate the percent of the area in a county or hydrological basin that is eroded. There are sample data (for several counties in eastern Iowa) from the National Resources Inventory and population data on land use, land capability class, rainfall and slope length and steepness. Using the Gibbs sampler we perform Bayesian Predictive Inference to obtain estimates for the non-sampled units. These estimates, together with the sample data, provide an estimate of the proportion of the total area that is eroded. We assess the quality of fit of our model using two cross-validation exercises and graphical methods.

  • Bayesian Predictive Inference for Longitudinal Sample Surveys
    Biometrics, 1993
    Co-Authors: B. Nandram, Joseph Sedransk
    Abstract:

    We describe and apply methodology for Bayesian Predictive Inference that is appropriate for many multistage, longitudinal sample surveys. The Pattern of Care Studies, two-stage cluster samples of cancer patients conducted on two occasions, are emphasized. For the application we fit a super-population model to the survey data, and provide posterior Inference for the desired finite population parameters.

  • Bayesian Predictive Inference for Small Areas for Binary Variables in the National Health Interview Survey
    Case Studies in Bayesian Statistics, 1993
    Co-Authors: Donald Malec, Joseph Sedransk, Linda Tompkins
    Abstract:

    The National Health Interview Survey is designed to produce precise estimates for the entire United States, but not for individual states. We use Bayesian Predictive Inference to provide point estimates and measures of variability for the desired finite population quantities. Our investigation concerns binary random variables such as the occurrence of at least one doctor visit within the past twelve months.

  • Bayesian Predictive Inference for a Finite Population Proportion: Two‐Stage Cluster Sampling
    Journal of the Royal Statistical Society: Series B (Methodological), 1993
    Co-Authors: B. Nandram, Joseph Sedransk
    Abstract:

    SUMMARY Given binary data from a two-stage cluster sample, we present a method to carry out Bayesian Predictive Inference for a finite population proportion. Our probabilistic specification should be useful for many surveys of this type and yields simple analytical expressions for the prior and posterior mean and variance. Within cluster k, we assume that the Yki are a random sample from the Bernoulli distribution with probability Ok. Conditional on ,B and T, 01, . . ., ON are a random sample from a beta distribution. Finally, 13 has a discrete distribution with specified probabilities. We use data from the National Health Interview Survey to illustrate the methodology and to show how to choose values for the parameters in the prior distribution.