scholarly journals Prediction of record values by using quantile regression curves and distortion functions

Metrika ◽  
2021 ◽  
Author(s):  
Jorge Navarro
Keyword(s):  
Record Values ◽  
Confidence Bands ◽  
Parametric Models ◽  
Dependence Structure ◽  
Data Set ◽  
Conditional Quantile ◽  
Regression Curves ◽  
Distortion Functions ◽  
Distorted Distributions ◽  
Non Parametric

AbstractThe purpose of the paper is to provide a general method based on conditional quantile curves to predict record values from preceding records. The predictions are based on conditional median (or median regression) curves. Moreover, conditional quantiles curves are used to provide confidence bands for these predictions. The method is based on the recently introduced concept of multivariate distorted distributions that are used instead of copulas to represent the dependence structure. This concept allows us to compute the conditional quantile curves in a simple way. The theoretical findings are illustrated with a non-parametric model (standard uniform), two parametric models (exponential and Pareto), and a non-parametric procedure for the general case. A real data set and a simulated case study in reliability are analysed.

scholarly journals A unified genealogy of modern and ancient genomes

2021 ◽  
Author(s):  
Anthony Wilder Wohns ◽  
Yan Wong ◽  
Ben Jeffery ◽  
Ali Akbari ◽  
Swapan Mallick ◽  
...  
Keyword(s):  
Geographical Location ◽  
Genomic Variation ◽  
Parametric Models ◽  
Compact Representation ◽  
Human History ◽  
Data Set ◽  
Reduction Techniques ◽  
Multiple Datasets ◽  
Key Events ◽  
Non Parametric

AbstractThe sequencing of modern and ancient genomes from around the world has revolutionised our understanding of human history and evolution1,2. However, the general problem of how best to characterise the full complexity of ancestral relationships from the totality of human genomic variation remains unsolved. Patterns of variation in each data set are typically analysed independently, and often using parametric models or data reduction techniques that cannot capture the full complexity of human ancestry3,4. Moreover, variation in sequencing technology5,6, data quality7and in silico processing8,9, coupled with complexities of data scale10, limit the ability to integrate data sources. Here, we introduce a non-parametric approach to inferring human genealogical history that overcomes many of these challenges and enables us to build the largest genealogy of both modern and ancient humans yet constructed. The genealogy provides a lossless and compact representation of multiple datasets, addresses the challenges of missing and erroneous data, and benefits from using ancient samples to constrain and date relationships. Using simulations and empirical analyses, we demonstrate the power of the method to recover relationships between individuals and populations, as well as to identify descendants of ancient samples. Finally, we show how applying a simple non-parametric estimator of ancestor geographical location to the inferred genealogy recapitulates key events in human history. Our results demonstrate that whole-genome genealogies are a powerful means of synthesising genetic data and provide rich insights into human evolution.

scholarly journals Modeling Local BES Indicators by Copula-Based Bayesian Networks

2020 ◽  
Author(s):  
Pierpaolo D’Urso ◽  
Vincenzina Vitale
Keyword(s):  
Bayesian Networks ◽  
Urban Areas ◽  
Well Being ◽  
Composite Indicators ◽  
Parametric Models ◽  
Dependence Structure ◽  
Proposed Model ◽  
Similarities And Differences ◽  
Normal Copula ◽  
Non Parametric

Abstract In Italy, the measure of the Equitable and Sustainable Well-being is provided by the Italian Institute of Statistics by means of a dashboard of basic and composite indicators. To investigate the dependence structure between the different domains of well-being, we propose the use of Non-Parametric Bayesian Networks based on the normal copula distribution, that allow to explore the conditional independence relationships between the composite indicators. The main advantage of the non-parametric models is that, as opposed to the parametric approach, they do not require any assumption on the marginal distributions of the variables. The proposed model is applied to the Equitable and Sustainable Well-being indicators measured at the provincial level and enriches the analysis of well-being by inspecting similarities and differences between Italian urban areas and territories.

scholarly journals Multi-criteria classification for pricing European options

2015 ◽  
Vol 0 (0) ◽  
Author(s):  
Nikola Gradojevic
Keyword(s):  
Implied Volatility ◽  
Parametric Models ◽  
European Options ◽  
Data Set ◽  
Classification Framework ◽  
Pricing Errors ◽  
Financial Options ◽  
Out Of Sample ◽  
Parametric Classification ◽  
Non Parametric

AbstractThis paper builds a novel multi-criteria, non-parametric classification framework in order to improve the accuracy of pricing European options. The proposed approach is based on classifying financial options according to their implied volatility, time to maturity and moneyness. Using a recent data set for the daily S&P 500 index call options, the multi-criteria modular neural network model demonstrates its superior out-of-sample pricing performance relative to competing parametric and non-parametric models. By observing the model’s pricing errors across various option types, the analysis provides additional insights into pricing biases and stresses the importance of selecting appropriate classification criteria.

Has tourism driven house prices in Germany? Time-varying evidence since 1870

2021 ◽  
pp. 135481662110088
Author(s):  
Sefa Awaworyi Churchill ◽  
John Inekwe ◽  
Kris Ivanovski
Keyword(s):  
World War Ii ◽  
Historical Data ◽  
House Prices ◽  
Housing Prices ◽  
Time Varying ◽  
Data Set ◽  
Time Series Modelling ◽  
The Impact ◽  
Tourism Flows ◽  
Non Parametric

Using a historical data set and recent advances in non-parametric time series modelling, we investigate the nexus between tourism flows and house prices in Germany over nearly 150 years. We use time-varying non-parametric techniques given that historical data tend to exhibit abrupt changes and other forms of non-linearities. Our findings show evidence of a time-varying effect of tourism flows on house prices, although with mixed effects. The pre-World War II time-varying estimates of tourism show both positive and negative effects on house prices. While changes in tourism flows contribute to increasing housing prices over the post-1950 period, this is short-lived, and the effect declines until the mid-1990s. However, we find a positive and significant relationship after 2000, where the impact of tourism on house prices becomes more pronounced in recent years.

scholarly journals Penalized Averaging of Parametric and Non-Parametric Quantile Forecasts

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Jan G. De Gooijer ◽  
Dawit Zerom
Keyword(s):  
Risk Premium ◽  
Empirical Studies ◽  
Forecast Accuracy ◽  
Equity Premium ◽  
Adaptive Lasso ◽  
Large Set ◽  
Usual Practice ◽  
Conditional Quantile ◽  
Equity Risk ◽  
Non Parametric

Abstract We propose a hybrid penalized averaging for combining parametric and non-parametric quantile forecasts when faced with a large number of predictors. This approach goes beyond the usual practice of combining conditional mean forecasts from parametric time series models with only a few predictors. The hybrid methodology adopts the adaptive LASSO regularization to simultaneously reduce predictor dimension and obtain quantile forecasts. Several recent empirical studies have considered a large set of macroeconomic predictors and technical indicators with the goal of forecasting the S&P 500 equity risk premium. To illustrate the merit of the proposed approach, we extend the mean-based equity premium forecasting into the conditional quantile context. The application offers three main findings. First, combining parametric and non-parametric approaches adds quantile forecast accuracy over and above the constituent methods. Second, a handful of macroeconomic predictors are found to have systematic forecasting power. Third, different predictors are identified as important when considering lower, central and upper quantiles of the equity premium distribution.

Use of Basu-Dhar bivariate geometric distribution in the analysis of the reliability of component series systems

2019 ◽  
Vol 36 (4) ◽  
pp. 569-586
Author(s):  
Ricardo Puziol Oliveira ◽  
Jorge Alberto Achcar
Keyword(s):  
Bayesian Approach ◽  
Geometric Distribution ◽  
Bivariate Distribution ◽  
Dependence Structure ◽  
Series System ◽  
Engineering Applications ◽  
Data Set ◽  
Content Type ◽  
Bivariate Geometric Distribution ◽  
Series Systems

Purpose The purpose of this paper is to provide a new method to estimate the reliability of series system by using a discrete bivariate distribution. This problem is of great interest in industrial and engineering applications. Design/methodology/approach The authors considered the Basu–Dhar bivariate geometric distribution and a Bayesian approach with application to a simulated data set and an engineering data set. Findings From the obtained results of this study, the authors observe that the discrete Basu–Dhar bivariate probability distribution could be a good alternative in the analysis of series system structures with accurate inference results for the reliability of the system under a Bayesian approach. Originality/value System reliability studies usually assume independent lifetimes for the components (series, parallel or complex system structures) in the estimation of the reliability of the system. This assumption in general is not reasonable in many engineering applications, since it is possible that the presence of some dependence structure between the lifetimes of the components could affect the evaluation of the reliability of the system.

Minimal clustering and species delimitation based on multi-locus alignments vs SNPs: the case of the Seriphium plumosum L. complex (Gnaphalieae: Asteraceae)

2021 ◽  
Author(s):  
Zaynab Shaik ◽  
Nicola Georgina Bergh ◽  
Bengt Oxelman ◽  
Anthony George Verboom
Keyword(s):  
Species Delimitation ◽  
Bayes Factor ◽  
Cape Floristic Region ◽  
Parametric Models ◽  
Western Cape ◽  
Nucleotide Polymorphisms ◽  
Sequence Alignments ◽  
Separate Species ◽  
Multiple Sequence ◽  
Data Set

We applied species delimitation methods based on the Multi-Species Coalescent (MSC) model to 500+ loci derived from genotyping-by-sequencing on the South African Seriphium plumosum (Asteraceae) species complex. The loci were represented either as multiple sequence alignments or single nucleotide polymorphisms (SNPs), and analysed by the STACEY and Bayes Factor Delimitation (BFD)/SNAPP methods, respectively. Both methods supported species taxonomies where virtually all of the 32 sampled individuals, each representing its own geographical population, were identified as separate species. Computational efforts required to achieve adequate mixing of MCMC chains were considerable, and the species/minimal cluster trees identified similar strongly supported clades in replicate runs. The resolution was, however, higher in the STACEY trees than in the SNAPP trees, which is consistent with the higher information content of full sequences. The computational efficiency, measured as effective sample sizes of likelihood and posterior estimates per time unit, was consistently higher for STACEY. A random subset of 56 alignments had similar resolution to the 524-locus SNP data set. The STRUCTURE-like sparse Non-negative Matrix Factorisation (sNMF) method was applied to six individuals from each of 48 geographical populations and 28023 SNPs. Significantly fewer (13) clusters were identified as optimal by this analysis compared to the MSC methods. The sNMF clusters correspond closely to clades consistently supported by MSC methods, and showed evidence of admixture, especially in the western Cape Floristic Region. We discuss the significance of these findings, and conclude that it is important to a priori consider the kind of species one wants to identify when using genome-scale data, the assumptions behind the parametric models applied, and the potential consequences of model violations may have.

A Non Parametric Model for Magneto Rheological Dampers

1999 ◽  
Author(s):  
Mehdi Ahmadian ◽  
Xubin Song
Keyword(s):  
Test Data ◽  
Mr Damper ◽  
Parametric Model ◽  
Parametric Models ◽  
Magnetic Saturation ◽  
Mr Dampers ◽  
Electro Magnetic ◽  
Magneto Rheological ◽  
Force Characteristics ◽  
Non Parametric

Abstract A non-parametric model for magneto-rheological (MR) dampers is presented. After discussing the merits of parametric and non-parametric models for MR dampers, the test data for a MR damper is used to develop a non-parametric model. The results of the model are compared with the test data to illustrate the accuracy of the model. The comparison shows that the non-parametric model is able to accurately predict the damper force characteristics, including the damper non-linearity and electro-magnetic saturation. It is further shown that the parametric model can be numerically solved more efficiently than the parametric models.

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