Academic literature on the topic 'Conditional parametric re finement'

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Journal articles on the topic "Conditional parametric re finement"

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Chen, Ting-Li, Hsieh Fushing, and Elizabeth P. Chou. "Learned Practical Guidelines for Evaluating Conditional Entropy and Mutual Information in Discovering Major Factors of Response-vs.-Covariate Dynamics." Entropy 24, no. 10 (2022): 1382. http://dx.doi.org/10.3390/e24101382.

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We reformulate and reframe a series of increasingly complex parametric statistical topics into a framework of response-vs.-covariate (Re-Co) dynamics that is described without any explicit functional structures. Then we resolve these topics’ data analysis tasks by discovering major factors underlying such Re-Co dynamics by only making use of data’s categorical nature. The major factor selection protocol at the heart of Categorical Exploratory Data Analysis (CEDA) paradigm is illustrated and carried out by employing Shannon’s conditional entropy (CE) and mutual information (I[Re;Co]) as the two
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NE, Gyamfi, Kyei KA, and Gill R. "African Stock Markets and Return Predictability." Journal of Economics and Behavioral Studies 8, no. 5(J) (2016): 91–99. http://dx.doi.org/10.22610/jebs.v8i5(j).1434.

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This article re-examines the return predictability of eight African stock markets. When returns of stocks are predictable, arbitrageurs make abnormal gains from analyzing prices. The study uses a non-parametric Generalised Spectral (GS) test in a rolling window approach. The rolling window approach tracts the periods of efficiency over time. The GS test is robust to conditional heteroscedasticity and it detects the presence of linear and nonlinear dependencies in a stationary time series. Our results support the Adaptive Market Hypothesis (AMH). This is because, indices whose returns were obse
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Rezaei, Ashkan, Rizal Fathony, Omid Memarrast, and Brian Ziebart. "Fairness for Robust Log Loss Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5511–18. http://dx.doi.org/10.1609/aaai.v34i04.6002.

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Developing classification methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in social applications. Many existing methods enforce fairness constraints on a selected classifier (e.g., logistic regression) by directly forming constrained optimizations. We instead re-derive a new classifier from the first principles of distributional robustness that incorporates fairness criteria into a worst-case logarithmic loss minimization. This construction takes the form of a minimax game and produces a parametri
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Dobrovolskiy, Vladimir. "Optimization of Portfolio of federal loan bonds and RE-PO trades." Economics and the Mathematical Methods 58, no. 3 (2022): 129. http://dx.doi.org/10.31857/s042473880018212-2.

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Within the framework of the considered model, the Investor can make transactions for the buying and sailing of federal loan bonds (OFZ), as well as direct and reverse REPO deals secured by OFZ. Transactions are made for liquidity management and increasing interest income. This paper discusses the problem of constructing an optimal portfolio of such transactions. The paper considers the approach for the generation of scenarios for OFZ price changes, the mathematical formulation of the optimization problem, the assessment of its dimension depending on the number of assets and the number of scena
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Dohi, Tadashi, Hiroyuki Okamura, and Cun Hua Qian. "Statistical software fault management based on bootstrap confidence intervals." International Journal of Quality & Reliability Management 37, no. 6/7 (2020): 905–23. http://dx.doi.org/10.1108/ijqrm-10-2019-0326.

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PurposeIn this paper, the authors propose two construction methods to estimate confidence intervals of the time-based optimal software rejuvenation policy and its associated maximum system availability via a parametric bootstrap method. Through simulation experiments the authors investigate their asymptotic behaviors and statistical properties.Design/methodology/approachThe present paper is the first challenge to derive the confidence intervals of the optimal software rejuvenation schedule, which maximizes the system availability in the sense of long run. In other words, the authors concern th
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Stupfler, Gilles, and Fan Yang. "ANALYZING AND PREDICTING CAT BOND PREMIUMS: A FINANCIAL LOSS PREMIUM PRINCIPLE AND EXTREME VALUE MODELING." ASTIN Bulletin 48, no. 1 (2017): 375–411. http://dx.doi.org/10.1017/asb.2017.32.

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AbstractCAT bonds play an important role in transferring insurance risks to the capital market. It has been observed that typical CAT bond premiums have changed since the recent financial crisis, which has been attributed to market participants being increasingly risk averse. In this work, we first propose a new premium principle, the financial loss premium principle, which includes a term measuring losses in the financial market that we represent here by the Conditional Tail Expectation (CTE) of the negative daily log-return of the S&P 500 index. Our analysis of empirical evidence suggest
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Finkelstein, T. "A new isothermal theory for Stirling machine analysis and a volume optimization using the concept of ‘ancillary’ and ‘tidal’ domains." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 212, no. 3 (1998): 225–36. http://dx.doi.org/10.1243/0954406981521178.

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Theoretical studies of Stirling cycle machines have always utilized a topological system view that goes back to Schmidt's isothermal analysis, where the process is analysed by reference to the expansion space volume variations. Due to this idiosyncrasy in the formulation, it has been difficult to deduce meaningful design criteria from the results. In this paper an alternative visualization is presented, using the newly introduced concepts of a ‘tidal phase angle’ and overlapping ‘tidal’ and ‘ancillary’ domains. With vectorial parameters and a centralized reference basis, a non-dimensional para
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Kulinkina, Alexandra V., Andrea Farnham, Nana-Kwadwo Biritwum, Jürg Utzinger, and Yvonne Walz. "How do disease control measures impact spatial predictions of schistosomiasis and hookworm? The example of predicting school-based prevalence before and after preventive chemotherapy in Ghana." PLOS Neglected Tropical Diseases 17, no. 6 (2023): e0011424. http://dx.doi.org/10.1371/journal.pntd.0011424.

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Background Schistosomiasis and soil-transmitted helminth infections are among the neglected tropical diseases (NTDs) affecting primarily marginalized communities in low- and middle-income countries. Surveillance data for NTDs are typically sparse, and hence, geospatial predictive modeling based on remotely sensed (RS) environmental data is widely used to characterize disease transmission and treatment needs. However, as large-scale preventive chemotherapy has become a widespread practice, resulting in reduced prevalence and intensity of infection, the validity and relevance of these models sho
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Dissertations / Theses on the topic "Conditional parametric re finement"

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Haque, Inzemamul. "Verification of a Generative Separation Kernel." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4571.

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A Separation Kernel is a small specialized microkernel that provides a sand-boxed execution environment for a given set of processes (also called \subjects"). The subjects may communicate only via declared memory channels, and are otherwise isolated from each other. A generative separation kernel is one in which a specialized separation kernel is generated for each system con figuration. Separation kernels are typically used in safety-critical and security-critical systems in the avionics and military domains, and it is of utmost importance to have a high level of assurance regarding the
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