scholarly journals A Novel Nonparametric Estimation for Conditional Copula Functions Based on Bayes Theorem

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 186182-186192
Author(s):  
Xinyao Li ◽  
Weihong Zhang ◽  
Liangli He
Keyword(s):  
Nonparametric Estimation ◽  
Bayes Theorem ◽  
Copula Functions ◽  
Conditional Copula

scholarly journals Trading strategies with copulas

2013 ◽  
Vol 6 (1) ◽  
pp. 83-108 ◽  
Author(s):  
Yolanda Stander ◽  
Daniël Marais ◽  
Ilse Botha
Keyword(s):  
Confidence Level ◽  
Tail Dependence ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Dependence Structure ◽  
Pairs Trading ◽  
Copula Functions ◽  
Conditional Copula ◽  
Historical Relationship ◽  
Bivariate Dependence

A new approach is proposed to identify trading opportunities in the equity market by using the information contained in the bivariate dependence structure of two equities. The relationships between the equity pairs are modelled with bivariate copulas and the fitted copula structures are utilised to identify the trading opportunities. Two trading strategies are considered that take advantage of the relative mispricing between a pair of correlated stocks and involve taking a position on the stocks when they diverge from their historical relationship. The position is then reversed when the two stocks revert to their historical relationship. Only stock-pairs with relatively high correlations are considered. The dependence structures of the chosen stock-pairs very often exhibited both upper- and lower-tail dependence, which implies that copulas with the correct characteristics should be more effective than the more traditional approaches typically applied. To identify trading opportunities, the conditional copula functions are used to derive confidence intervals for the two stocks. It is shown that the number of trading opportunities is highly dependent on the confidence level and it is argued that the chosen confidence level should take the strength of the dependence between the two stocks into account. The backtest results of the pairs-trading strategy are disappointing in that even though the strategy leads to profits in most cases, the profits are largely consumed by the trading costs. The second trading strategy entails using single stock futures and it is shown to have more potential as a statistical arbitrage approach to construct a portfolio.

Bayesian Applications in Auditory Research

2019 ◽  
Vol 62 (3) ◽  
pp. 577-586 ◽  
Author(s):  
Garnett P. McMillan ◽  
John B. Cannon
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Bayesian Methods ◽  
Prior Distribution ◽  
Interim Analysis ◽  
Iterative Process ◽  
Bayes Theorem ◽  
Sound Therapy ◽  
The Many ◽  
Auditory Research

Purpose This article presents a basic exploration of Bayesian inference to inform researchers unfamiliar to this type of analysis of the many advantages this readily available approach provides. Method First, we demonstrate the development of Bayes' theorem, the cornerstone of Bayesian statistics, into an iterative process of updating priors. Working with a few assumptions, including normalcy and conjugacy of prior distribution, we express how one would calculate the posterior distribution using the prior distribution and the likelihood of the parameter. Next, we move to an example in auditory research by considering the effect of sound therapy for reducing the perceived loudness of tinnitus. In this case, as well as most real-world settings, we turn to Markov chain simulations because the assumptions allowing for easy calculations no longer hold. Using Markov chain Monte Carlo methods, we can illustrate several analysis solutions given by a straightforward Bayesian approach. Conclusion Bayesian methods are widely applicable and can help scientists overcome analysis problems, including how to include existing information, run interim analysis, achieve consensus through measurement, and, most importantly, interpret results correctly. Supplemental Material https://doi.org/10.23641/asha.7822592

Outcome – Studien im Zusammenhang mit Laboranalysen

2015 ◽  
Vol 72 (2) ◽  
pp. 93-98
Author(s):  
Andreas R. Huber
Keyword(s):  
Bayes Theorem ◽  
Wird Eine ◽  
Psa Test

Biochemische Erkenntnisse und das Wissen über Stoffwechselvorgänge bis zum einzelnen Molekül und die rasche Entwicklung von neuen, sehr leistungsfähigen Methoden haben es erlaubt, dass die Labormedizin als wichtiger Bestandteil von Diagnose, Ausschluss, Therapie, Monitoring und als prädiktiver Test Einzug in die Medizin erhalten hat. Wichtig ist nicht nur die Qualität des Assays, sondern auch das Fachwissen um den Test, d. h. dass ein Test für die richtige Fragestellung eingesetzt wird und die Wertigkeit dem Kliniker bekannt ist. Hinzu kommen das Einhalten der präanalytischen Bedingungen, Kenntnisse über statistische Fakten wie Bayes Theorem und der gekonnte Miteinbezug anderer Resultate, klinischer Gegebenheiten. So lässt sich Berechnung oder wenigstens Abschätzung einer postanalytischen Wahrscheinlichkeit erheben. Der gleiche Test wird eine sehr unterschiedliche Performance haben je nach dem gewählten Einsatzort, resp. dem gewählten Patientenkollektiv. So macht z. B. ein erstmaliger PSA-Test bei einem 70-jährigen Patienten wenig Sinn. Weiter spielen auch die Qualitäten des Tests eine entscheidende Rolle. Es kann davon ausgegangen werden, dass gerade in der Labormedizin weitere Outcome-Studien folgen werden. Der Wert dieser nimmt zu, da die Tests in der Regel nicht bis wenig invasiv, relativ günstig und rasch erhältlich sind.

Effects of Observer Variation on Performance in Probabilistic Diagnosis of Jaundice

1981 ◽  
Vol 20 (03) ◽  
pp. 163-168 ◽  
Author(s):  
G. Llndberg
Keyword(s):  
Physical Examination ◽  
System Performance ◽  
Transition Matrix ◽  
Bayes Theorem ◽  
Observer Variation ◽  
Kappa Statistics ◽  
Diagnostic Categories ◽  
Number Of Patients ◽  
Probability Transition Matrix ◽  
Probabilistic Diagnosis

A system for probabilistic diagnosis of jaundice has been used for studying the effects of taking into account the unreliability of diagnostic data caused by observer variation. Fourteen features from history and physical examination were studied. Bayes’ theorem was used for calculating the probabilities of a patient’s belonging to each of four diagnostic categories.The construction sample consisted of 61 patients. An equal number of patients were tested in the evaluation sample. Observer variation on the fourteen features had been assessed in two previous studies. The use of kappa-statistics for measuring observer variation allowed the construction of a probability transition matrix for each feature. Diagnostic probabilities could then be calculated with and without the inclusion of weights for observer variation. Tests of system performance revealed that discriminatory power remained unchanged. However, the predictions rendered by the variation-weighted system were diffident. It is concluded that taking observer variation into account may weaken the sharpness of probabilistic diagnosis but it may also help to explain the value of probabilistic diagnosis in future applications.

Bayesian Diagnostic Probabilities without Assuming Independence of Symptoms

1991 ◽  
Vol 30 (01) ◽  
pp. 15-22 ◽  
Author(s):  
A. Gammerman ◽  
A. R. Thatcher
Keyword(s):  
Abdominal Pain ◽  
Acute Abdominal Pain ◽  
Final Diagnosis ◽  
Bayes Theorem ◽  
Bayes Method ◽  
Hospital Records ◽  
Computer Diagnosis ◽  
Bayes Model ◽  
Chi Squared ◽  
Past Data

The paper describes an application of Bayes’ Theorem to the problem of estimating from past data the probabilities that patients have certain diseases, given their symptoms. The data consist of hospital records of patients who suffered acute abdominal pain. For each patient the records showed a large number of symptoms and the final diagnosis, to one of nine diseases or diagnostic groups. Most current methods of computer diagnosis use the “Simple Bayes” model in which the symptoms are assumed to be independent, but the present paper does not make this assumption. Those symptoms (or lack of symptoms) which are most relevant to the diagnosis of each disease are identified by a sequence of chi-squared tests. The computer diagnoses obtained as a result of the implementation of this approach are compared with those given by the “Simple Bayes” method, by the method of classification trees (CART), and also with the preliminary and final diagnoses made by physicians.

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