74 research outputs found

    A bizonyosságfüggvények elméletének alkalmazása a pénzügyi kimutatások ellenőrzésében (The application of the theory of belief functions in the audit of financial statements)

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    A könyvvizsgálati kockázat a téves auditjelentés kiadásának kockázata olyan esetekben, amikor a beszámoló lényeges hibás állítást tartalmaz. Ez a kockázat indirekt módon a hitelintézetek és pénzügyi vállalkozások működésében is megjelenik azokban az esetekben, amikor a lényeges hibás állítást a finanszírozott vállalkozás auditált beszámolója tartalmazza, amelynek az alapján finanszírozási döntést hoznak, vagy a finanszírozás folytatásáról a beszámolóban szereplő, hibás információkból számított hitelkovenánsok alapján döntenek. A könyvvizsgálat kockázatában a vizsgált gazdálkodó üzleti kockázatai tükröződnek vissza, ezért a kockázat felmérése és az ellenőrzés ennek alapján való megtervezése, majd végrehajtása kulcsfontosságú. Jelen tanulmány – kapcsolódva a Hitelintézeti Szemle 2011. évi 4. számához – szintén a kockázat és bizonytalanság témakörét tárgyalja, pontosabban ennek egy gyakorlati vetületét: a bizonyosságfüggvények (belief functions) alkalmazását a könyvvizsgálatban; mindezt a teljesség és a tankönyvszerű rendszerfelépítés igénye nélkül. A módszer ugyanis hazánkban szinte ismeretlen, nemzetközi viszonylatban viszont empirikus kutatásban is rámutattak már az alkalmazás lehetséges előnyeire a hagyományos valószínűségelméleten alapuló számszerű kockázatbecslésekkel szemben. Eszerint a bizonyosságfüggvények jobban reprezentálják a könyvvizsgálóknak a kockázatról alkotott képét, mint a valószínűségek, mert – szemben a hagyományos modellel – nem két, hanem három állapotot kezelnek: a pozitív bizonyíték létezését, a negatív bizonyíték létezését és a bizonyíték hiányának esetét. _______ Audit risk is the risk that the auditor expresses an inappropriate audit opinion when the fi nancial statements are materially misstated. This kind of risk indirectly appears in the fi nancial statements of fi nancial institutions, when the material misstatement is in the fi nanced entity’s statements that serve as a basis for lending decisions or when the decision is made based upon credit covenants calculated from misstated information. The risks of the audit process refl ect the business risks of the auditee, so the assessment of risks, and further the planning and performance of the audit based on it is of key importance. The current study – connecting to No 4 2011 of Hitelintézeti Szemle – also discusses the topic of risk and uncertainty, or to be more precise a practical implementation of the aforementioned: the application of belief functions in the fi eld of external audit. All this without the aim of achieving completeness or textbook-like scrutiny in building up the theory. While the formalism is virtually unknown in Hungary, on the international scene empirical studies pointed out the possible advantages of the application of the method in contrast to risk assessments based on the traditional theory of probability. Accordingly, belief functions provide a better representation of auditors’ perception of risk, as in contrast to the traditional model, belief functions deal with three rather than two states: the existence of supportive evidence, that of negative evidence and the lack of evidence

    Bayesian and Belief-Functions Formulas for Auditor Independence Risk Assessment

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    This is the authors final draft. The publisher's official version is available electronically from: .This paper illustrates two formulas for assessing independence risk based on the Bayesian and belief-functions frameworks. These formulas can be used to assess the role of threats to auditor independence as well as the role of threat-mitigating safeguards. Also, these formulas provide a basis for evaluation of an audit firm’s independence risk and a framework to educate stakeholders about the threats faced by the audit firm and the role of effective safeguards in mitigating these risks. The formulas also provide a means for regulators and lawmakers to evaluate whether they have effective safeguards in place given the existence of threats and for auditors to signal to various stakeholders that they have identified significant threats and have effective safeguards in place. To show the potential usefulness of these analytical models, several illustrations addressing increased transparency and the potential impact of regulations are presented

    A Toolkit for uncertainty reasoning and representation using fuzzy set theory in PROLOG expert systems

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    This thesis examines the issue of uncertainty reasoning and representation in expert systems. Uncertainty and expert systems are defined. The value of uncertainty in expert systems as an approximation of human reasoning is stressed. Five alternative methods of dealing with uncertainty are explored. These include Bayesian probabilities, Mycin confirmation theory, fuzzy set theory, Dempster-Shafer\u27s theory of evidence and a theory of endorsements. A toolkit to apply uncertainty processing in PROLOG expert systems is developed using fuzzy set theory as the basis for uncertainty reasoning and representation. The concepts of fuzzy logic and approximate reasoning are utilized in the implementation. The toolkit is written in C-PROLOG for the PYRAMID UNIX system at the Rochester Institute of Technology

    Method of Classification for Multisource Data in Remote Sensing Based on Interval-VaIued Probabilities

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    This work was supported by NASA Grant No. NAGW-925 “Earth Observation Research - Using Multistage EOS-Iike Data” (Principal lnvestigators: David A. Landgrebe and Chris Johannsen). The Anderson River SAR/MSS data set was acquired, preprocessed, and loaned to us by the Canada Centre for Remote Sensing, Department of Energy Mines, and Resources, of the Government of Canada. The importance of utilizing multisource data in ground-cover^ classification lies in the fact that improvements in classification accuracy can be achieved at the expense of additional independent features provided by separate sensors. However, it should be recognized that information and knowledge from most available data sources in the real world are neither certain nor complete. We refer to such a body of uncertain, incomplete, and sometimes inconsistent information as “evidential information.” The objective of this research is to develop a mathematical framework within which various applications can be made with multisource data in remote sensing and geographic information systems. The methodology described in this report has evolved from “evidential reasoning,” where each data source is considered as providing a body of evidence with a certain degree of belief. The degrees of belief based on the body of evidence are represented by “interval-valued (IV) probabilities” rather than by conventional point-valued probabilities so that uncertainty can be embedded in the measures. There are three fundamental problems in the muItisource data analysis based on IV probabilities: (1) how to represent bodies of evidence by IV probabilities, (2) how to combine IV probabilities to give an overall assessment of the combined body of evidence, and (3) how to make a decision when the statistical evidence is given by IV probabilities. This report first introduces an axiomatic approach to IV probabilities, where the IV probability is defined by a pair of set-theoretic functions which satisfy some pre-specified axioms. On the basis of this approach the report focuses on representation of statistical evidence by IV probabilities and combination of multiple bodies of evidence. Although IV probabilities provide an innovative means for the representation and combination of evidential information, they make the decision process rather complicated. It entails more intelligent strategies for making decisions. This report also focuses on the development of decision rules over IV probabilities from the viewpoint of statistical pattern recognition The proposed method, so called “evidential reasoning” method, is applied to the ground-cover classification of a multisource data set consisting of Multispectral Scanner (MSS) data* Synthetic Aperture Radar (SAR) data, and digital terrain data such as elevation, slope, and aspect. By treating the data sources separately, the method is able to capture both parametric and nonparametric information and to combine them. Then the method is applied to two separate cases of classifying multiband data obtained by a single sensor, in each case, a set of multiple sources is obtained by dividing the dimensionally huge data into smaller and more manageable pieces based on the global statistical correlation information. By a Divide-and-Combine process, the method is able to utilize more features than the conventional Maximum Likelihood method

    Uncertainty assessment in climate change scenarios: a methodological proposal for management of forest ecosystem services

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    The work introduces a method to quantify potential impact of climate change on cultural ecosystem services in forests. The technique of Hesitant Fuzzy Linguistic Term Set is applied to face with the uncertainty due to climate change as well as subjective opinion of forest experts. Two forest management scenario (current practices as well as climate change-oriented silviculture) are investigated for different time horizons. Results highlight the increasing uncertainty on climate change impact evaluation related to longer time horizons. Potential losses connected to current cultural ecosystem services provision are quantified from spatial as well as economic viewpoint. The method is tested for an illustrative example in the Tuscany region - central Italy

    Accuracy of direct genomic breeding values for nationally evaluated traits in US Limousin and Simmental beef cattle

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    BACKGROUND: In national evaluations, direct genomic breeding values can be considered as correlated traits to those for which phenotypes are available for traditional estimation of breeding values. For this purpose, estimates of the accuracy of direct genomic breeding values expressed as genetic correlations between traits and their respective direct genomic breeding values are required. METHODS: We derived direct genomic breeding values for 2239 registered Limousin and 2703 registered Simmental beef cattle genotyped with either the Illumina BovineSNP50 BeadChip or the Illumina BovineHD BeadChip. For the 264 Simmental animals that were genotyped with the BovineHD BeadChip, genotypes for markers present on the BovineSNP50 BeadChip were extracted. Deregressed estimated breeding values were used as observations in weighted analyses that estimated marker effects to derive direct genomic breeding values for each breed. For each breed, genotyped individuals were clustered into five groups using K-means clustering, with the aim of increasing within-group and decreasing between-group pedigree relationships. Cross-validation was performed five times for each breed, using four groups for training and the fifth group for validation. For each trait, we then applied a weighted bivariate analysis of the direct genomic breeding values of genotyped animals from all five validation sets and their corresponding deregressed estimated breeding values to estimate variance and covariance components. RESULTS: After minimizing relationships between training and validation groups, estimated genetic correlations between each trait and its direct genomic breeding values ranged from 0.39 to 0.76 in Limousin and from 0.29 to 0.65 in Simmental. The efficiency of selection based on direct genomic breeding values relative to selection based on parent average information ranged from 0.68 to 1.28 in genotyped Limousin and from 0.51 to 1.44 in genotyped Simmental animals. The efficiencies were higher for 323 non-genotyped young Simmental animals, born after January 2012, and ranged from 0.60 to 2.04. CONCLUSIONS: Direct genomic breeding values show promise for routine use by Limousin and Simmental breeders to improve the accuracy of predicted genetic merit of their animals at a young age and increase response to selection. Benefits from selecting on direct genomic breeding values are greater for breeders who use natural mating sires in their herds than for those who use artificial insemination sires. Producers with unregistered commercial Limousin and Simmental cattle could also benefit from being able to identify genetically superior animals in their herds, an opportunity that has in the past been limited to seed stock animals
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