Practical applications of Bayesian nonparametric (BNP) models have been limited, due to their high computational complexity and poor scaling on large data. (videolectures.net)
This paper designs a Bayesian nonparametric covariance estimator by integrating the pooling method, vector moving average adjustment and synchronization with data augmentation. (risk.net)
Simulation studies confirm the Bayesian nonparametric covariance estimator is very competitive with existing estimators, and empirical applications show that the proposed covariance measure enhances the economic value of volatility timing. (risk.net)
This paper introduces a Bayesian nonparametric method to estimate the ex post covariance matrix from high-frequency data. (risk.net)
Monte Carlo simulation shows that the Bayesian nonparametric method provides more precise covariance estimates in both ideal and realistic settings. (risk.net)
Modern Bayesian nonparametric analysis was introduced by Ferguson with the development of the Dirichlet process in the 1970s. (newton.ac.uk)
The outstanding challenges include the construction and properties, such as consistency issues, of Bayesian nonparametric regression models. (newton.ac.uk)
Bayesian nonparametric inference relies on the construction of an infinite dimensional probability distribution on function spaces. (newton.ac.uk)
Another strand of Bayesian nonparametric inference involves the construction of regression functions. (newton.ac.uk)
Recent attempts have been made to strengthen the connections between these two area of Bayesian nonparametric research. (newton.ac.uk)
The ultimate goal is obtain suitable classes of fully Bayesian nonparametric regression models. (newton.ac.uk)
Leading international researchers in Bayesian nonparametric methods, both theory and applied, will be brought together to discuss and tackle this outstanding issue in Bayesian research. (newton.ac.uk)
This thesis illustrates how restrictive assumptions (e.g., unimodality) common to most road safety studies can be relaxed employing Bayesian nonparametric Dirichlet process mixture models. (uwaterloo.ca)
We present a sequence of unsupervised, nonparametric Bayesian models for clustering complex linguistic objects. (nips.cc)
A recent work brought out a peculiar property of the popular completely random measures (CRMs) as prior models in Bayesian nonparametric (BNP) inference for the unseen-features problem: for fixed prior's parameters, they all lead to a Poisson posterior distribution for the number of unseen features, which depends on the sampling information only through the sample size. (arxiv.org)
In order to deal with this, we propose nonparametric Bayesian methods for variable selection and calculating variable importance. (kdiss.org)
By incorporating these nonparametric Bayesian methods for variable selection and quantifying variable importance, we aim to enhance the modeling and analysis of complex dependency structures in copula. (kdiss.org)
We investigate the use of Bayesian nonparametric (BNP) models coupled with Markov random fields (MRF) in a risk mappring context, to build partitions of the risk into homogeneous spatial regions. (hal.science)
Semiparametric and Nonparametric Methods2
Assessing risk prediction models in case-control studies using semiparametric and nonparametric methods. (nih.gov)
Here, we present semiparametric and nonparametric methods for evaluating a continuous risk prediction marker that accommodates case-control data. (nih.gov)
Estimation5
Nonparametric estimation of a structural cointegrating regression model is studied. (econometricsociety.org)
In nonparametric estimation problems, joint dependence is known to be a major complication that affects identification, induces bias in conventional kernel estimates, and frequently leads to ill‐posed inverse problems. (econometricsociety.org)
Instead, simple nonparametric kernel estimation of a structural nonparametric cointegrating regression is consistent and the limit distribution theory is mixed normal, giving straightforward asymptotics that are useable in practical work. (econometricsociety.org)
We consider nonparametric estimation of a regression function that is identified byrequiring a specified quantile of the regression "error" conditional on an instrumentalvariable to be zero. (cemmap.ac.uk)
We propose a nonparametric correction method for the estimation of regression coefficients, and show that the estimators are asymptotically consistent for the true parameters. (nih.gov)
Inference1
The results provide a convenient basis for inference in structural nonparametric regression with nonstationary time series when there is a single integrated or near‐integrated regressor. (econometricsociety.org)
Afni3
I would really appreciate any suggestions on some up-to-date afni nonparametric approaches. (nih.gov)
My understanding is that with afni, we can use 3dClustSim to correct the cluster threshold and I also read a bit about afni BML… I would really appreciate any suggestions on some up-to-date afni nonparametric approaches. (nih.gov)
There are some options within AFNI for nonparametric statistics, see here . (nih.gov)
20222
Nonparametric Analysis of Heterogeneous Multidimensional Fairness ," Working Papers ECARES 2022-29, ULB -- Universite Libre de Bruxelles. (repec.org)
Aveek, Mahmudur Rahman, "Sustain Water Conservation Behaviors Using Nonparametric Ranking and Social Marketing" (2022). (usu.edu)
Volatility2
We use the k-th order nonparametric causality test at monthly frequency over the period of 1984:1 to 2015:12 to analyze whether aggregate country risk, and its components (economic, financial and political) can predict movements in stock returns and volatility of eighty-three developed and developing economies. (repec.org)
When we apply the nonparametric test, we find that, while there is no evidence of predictability of squared stock returns barring one case, at times, there are nearly 50 percent of the countries where the aggregate risks and its components tend to predict stock returns and realized volatility. (repec.org)
Regression model1
where if density functions are modeled nonparametrically, the regression function is modeled parametrically, and if the regression model is nonparametric then the density function which carries it is modeled parametrically. (newton.ac.uk)
Methods2
Empirical comparisons in short-term interest rate models using nonparametric methods. (uni-muenchen.de)
The semiparametric methods are substantially more efficient than their nonparametric counterparts under a correctly specified model. (nih.gov)
Approach7
A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks ," Journal of Finance , American Finance Association, vol. 49(3), pages 851-889, July. (repec.org)
A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks ," NBER Working Papers 4718, National Bureau of Economic Research, Inc. (repec.org)
One natural approach for detecting such changes is to use nonparametric two-sample statistical tests. (projecteuclid.org)
This paper presents a nonparametric approach to the analysis of areas under correlated ROC curves, by using the theory on generalized U-statistics to generate an estimated covariance matrix. (nih.gov)
The nonparametric approach controls for the existing misspecification of a linear framework of causality, and hence, the weak evidence of causality obtained under the standard Granger tests cannot be relied upon. (repec.org)
The approach applied is the nonparametric item analysis using the Test graft computer program (Ramsay, 1991). (bvsalud.org)
We discuss our findings within the context of the potential use of the Qualitative Scoring System and of the nonparametric item analysis approach in the psychometric research. (bvsalud.org)
Curves1
Nonparametric Engel curves are used to estimate expansion paths and provide a stochastic structure within which to examine the consistency of household level data and revealed preference theory. (repec.org)
Statistical analysis4
This paper applies revealed preference theory to the nonparametric statistical analysis of consumer demand. (repec.org)
There are two reasons why one might prefer to use a nonparametric statistical analysis: 1) The population in question may differ significantly from the normal distribution. (nih.gov)
2) Nonparametric statistical analysis techniques are usually less sensitive to the presence of "outliers", i.e., they are more robust. (nih.gov)
This set of programs is intended to provide the capability to perfom nonparametric statistical analysis of FMRI data, roughly corresponding to the present (* well, note that "present" here is in the 90s ) capability to perform parametric statistical analysis. (nih.gov)
Models2
He is interested in nonparametric statistics, categorical choice models, and statistics in finance. (edu.au)
The nonparametric maximum likelihood estimator (NPMLE) of a distribution function F in biased sampling models have been studied by Cox (1969), Vardi (1982, 1985), and Gill, Vardi, and Wellner (1988). (umd.edu)
Approaches2
Gaussian Process Regression and Kernel Ridge Regression are popular nonparametric regression approaches. (mlr.press)
0 Conference Paper %T Data-driven confidence bands for distributed nonparametric regression %A Valeriy Avanesov %B Proceedings of Thirty Third Conference on Learning Theory %C Proceedings of Machine Learning Research %D 2020 %E Jacob Abernethy %E Shivani Agarwal %F pmlr-v125-avanesov20a %I PMLR %P 300--322 %U https://proceedings.mlr.press/v125/avanesov20a.html %V 125 %X Gaussian Process Regression and Kernel Ridge Regression are popular nonparametric regression approaches. (mlr.press)
Estimator2
They include the nonparametric maximum likelihood estimator, a kernel estimator, and a modified histogram type estimator. (umd.edu)
Rupert Miller says, in comparison with Weibull, "…the asymptotic efficiencies [ratio of variances] of the Kaplan-Meier estimator [nonparametric, right-censored] are low, especially for high censoring proportions or for surviving fractions that are close to one or zero. (accendoreliability.com)
Nonparametric analysis of heterogeneous multidimensional fairness ," Working Papers 621, ECINEQ, Society for the Study of Economic Inequality. (repec.org)
In this paper, we propose a new nonparametric method for Testing Remodeling under Heterogeneity (TRUH) that can accurately detect changes in the infected samples compared to possibly heterogeneous uninfected samples. (projecteuclid.org)
NPMLE1
These nonparametric maximum likelihood and least squares estimates (npmle and nplse) have more likelihood and less SSE than Weibull estimates (figure 1). (accendoreliability.com)
Methodology2
We implement a nonparametric methodology to capture short-term stable time-varying relationships of otherwise long-term unstable relationships between numerous macroeconomic variables and style returns. (springer.com)
We find that a nonparametric forecasting methodology produces positive performance after allowing for transaction costs, while the equivalent parametric forecasts are negative. (springer.com)
Statistics5
In statistics and probability theory, the nonparametric skew is a statistic occasionally used with random variables that take real values. (wikipedia.org)
Many such mappings have been introduced in the literature in recent decades and this is subject to intense research in, among other, nonparametric statistics and functional data analysis. (r-bloggers.com)
Published in: Journal of Nonparametric Statistics No. 13 (2001): pp. 815-831. (uni-muenchen.de)
Miller] Nonparametric statistics make no unwarranted assumptions and preserve all information in data. (accendoreliability.com)
Nonparametric statistics for the behavioral sciences / Sidney Siegel. (who.int)
Paper2
In this paper, we consider dependent nonparametric trees (DNTs), a powerful infinite model that captures time-evolving hierarchies, and develop a large-scale distributed training system. (videolectures.net)
From a nonparametric perspective, this paper studies how well overparametrized neural networks can recover the true target function in the presence of random noises. (mlr.press)
Tests2
Knowledge of expansion paths is shown to improve the power of nonparametric tests of revealed preference. (repec.org)
As a result, viruses often infect these different subpopulations at different rates, in which case the traditional nonparametric two-sample tests for checking similarity in distributions are no longer conservative. (projecteuclid.org)
Propose3
We propose a nonparametric VAR measure that incorporates economic valuation according to the state-price density associated with the underlying price processes. (repec.org)
We propose a nonparametric extension to the factor analysis problem using a beta process prior. (videolectures.net)
In this thesis we propose a nonparametric method of variable selection that works in two stages. (unisa.it)
Analysis2
In addition to using 3dMVM in the 2nd level analysis for a two-way mixed design, I wish to continue with an 'equivalent' nonparametric test. (nih.gov)
Nonparametric regression consists of learning an arbitrary mapping f: \X to \Y from a data set of (X,Y) pairs in which the Y values are corrupted by noise of mean zero. (wias-berlin.de)
Some nonparametric regressors perform better in such situations as their risks depend only on the "intrinsic dimension" of the data, appropriately formalized. (wias-berlin.de)
To cope with the imperfect detection of individuals inherent to monitoring in the wild, we develop a nonparametric method for evaluating the form of natural selection on a quantitative trait using mark-recapture data. (bioone.org)
Alternative extrapolations of nonparametric failure rates from data subsets quantify uncertainty. (accendoreliability.com)
This thesis aims at proposing a new method of solving the nonparametric and non-additive regression problem in presence of ultra-high dimensional data. (unisa.it)
Estimates1
Alternation is apparent in the naive and nonparametric estimates: especially in figures 2 and 3. (accendoreliability.com)
Papers1
Nonparametric Risk Management and Implied Risk Aversion ," NBER Working Papers 6130, National Bureau of Economic Research, Inc. (repec.org)
Distribution2
Its calculation does not require any knowledge of the form of the underlying distribution-hence the name nonparametric. (wikipedia.org)
The nonparametric skew is one third of the Pearson 2 skewness coefficient and lies between −1 and +1 for any distribution. (wikipedia.org)
Risk1
Nonparametric risk management and implied risk aversion ," Journal of Econometrics , Elsevier, vol. 94(1-2), pages 9-51. (repec.org)