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False Financial Statements: Characteristics of China's listed companies and CART detecting approach

INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2008年第2期7卷 339-359页

作者： Bai, Belinna Yen, Jerome Yang, Xiaoguang Agr Bank China Credit Dept HK Branch Admiralty Ctr Hong Kong Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Finance Kowloon Hong Kong Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100080 Peoples R China

False Financial Statements (FFS) have long been a serious problem in China and other Asian countries, which significantly dampen the confidence of the investors. Regardless of listed companies or non-listed companies, the percentage of financial statements that contained false information is quite high, which is one of the major reasons why China stock markets moved in the opposite direction towards its wonderful economic growth over the past few years. The objective of this research is to introduce one statistical technique - classification and regression tree (CART), to identify and predict the impacts of FFS. We survey financial statements manipulation tricks, FFS indicators and FFS detection techniques from both China and international perspective, and further look into ten listed companies with known FFS history in China;combining these findings, we propose key indicators to work with CART. Our analysis includes 24 false financial reports, and 124 non-false financial reports. We use CART to develop two FFS detecting models: CART without industry benchmark and CART with industry benchmark. For supporting comparison, we also build a Logit regression which is a commonly used technique in FFS detecting. We find that CART is effective in distinguishing FFS from non-FFS. Both CART models achieve better accuracy in identifying fraud cases and making predictions than Logit regression does, and CART with industry benchmark is slightly better than CART without benchmark, but it does not always have superior performance. Our CART model also tries to capture the indicators and their combinations that could reflect firms with high possibility of FFS in China.

关键词： false financial statements China's listed companies classification and regression tree industry benchmark

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What clues are available for differential diagnosis of headaches in emergency settings?

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JOURNAL OF HEADACHE AND PAIN 2008年第2期9卷 89-97页

作者： Mert, Ertan Ozge, Aynur Tasdelen, Bahar Yilmaz, Arda Bilgin, Nursel G. Mersin Univ Sch Med Mersin Turkey

The correct diagnosis of headache disorders in an emergency room is important for developing early management strategies and determining optimal emergency room activities. This prospective clinical based study was performed in order to determine demographic and clinical clues for differential diagnosis of primary and secondary headache disorders and also to obtain a classification plot for the emergency room practitioners. This study included 174 patients older than 15 years of age presenting in the emergency room with a chief complaint of headache. Definite headache diagnoses were made according to ICHD-II criteria. classification and regression tree was used as new method for the statistical analysis of the differential diagnostic process. Our 174 patients with headache were diagnosed as basically primary (72.9%) and secondary (27.1%) headaches. Univariate analysis with cross tabs showed three important results. First, unilateral pain location caused 1.431-fold increase in the primary headache risk ( p = 0.006). Second, having any triggers caused 1.440-fold increase in the primary headache risk ( p = 0.001). Third, having associated co-morbid medical disorders caused 4.643-fold increase in the secondary headache risk (p < 0.001). It was concluded that the presence of comorbidity, the patient's age, the existence of trigger and relaxing factors, the pain in other body parts that accompanies headache and the quality of pain in terms of location and duration were all important clues for physicians in making an accurate differentiation between primary and secondary headaches.

关键词： Emergency Headache Primary headaches Secondary headaches classification and regression tree Differential diagnosis

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The pervasive influence of sampling and methodological artefacts on a macroecological pattern: the abundance-occupancy relationship

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GLOBAL ECOLOGY AND BIOGEOGRAPHY 2008年第4期17卷 457-464页

作者： Wilson, Peter D. Macquarie Univ Dept Biol Sci N Ryde NSW 2109 Australia

Aim To investigate the influence of sampling and methodological artefacts on the correlation between abundance and occupancy. Location Global scope. Methods A fixed effects weighted regression model was fitted to standardized effect size for 175 examples of correlations between abundance and occupancy. A regression tree model with standard effect size as the dependent variable was also fitted to the data. Results Standard effect size, and therefore the correlation between abundance and occupancy, was found to be strongly influenced by the type of abundance measure used to characterize the abundance-occupancy relationship. Local mean abundance (also referred to as ecological mean abundance) was primarily responsible for negative correlations. Negative correlations also resulted from a mismatch in the sampling extents of abundance and occupancy measures. Main conclusions The combination of abundance and occupancy measures selected to characterize the abundance-occupancy relationship for a given set of data has a profound impact on the sign of the correlation between the selected measures. Previous attempts to understand the processes giving rise to the pattern represented by the abundance-occupancy relationship have confounded sampling artefacts (e.g. spatial extent of abundance and occupancy information) and methodological artefacts (e.g. combining a truncated abundance measure such as local mean abundance with an untruncated occupancy measure such as proportion of occupied samples). Thus, a revision of the approach currently used to define and evaluate competing explanatory models of the abundance-occupancy relationship appears to be necessary.

关键词： abundance-occupancy relationship classification and regression tree macroecology meta-analysis

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Clinical signs and early prognosis in vegetative state: A decisional tree, data-mining study

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BRAIN INJURY 2008年第7-8期22卷 617-623页

作者： Dolce, G. Quintieri, M. Serra, S. Lagani, V. Pignolo, L. S Anna Inst Intens Care Unit Vegetat State I-88900 Crotone Italy Univ Calabria Dept Elect Informat & Syst I-87036 Cosenza Italy

Background and purpose: Residual brain function has been documented in vegetative state patients, yet early prognosis remains difficult. The purpose of this study was to identify by artificial intelligence procedures (classification and regression trees, data-mining) the significant neurological signs correlated to and predictive of outcome. Methods: Three hundred and thirty-three patients in vegetative state of traumatic or non-traumatic aetiology referred to the *** Institute were retrospectively studied. Twenty-two neurological signs were assessed according to criteria included in the UNI ENI ISO 9001 : 2000 quality standards at admission (Time(0)) and after 50, 100 or 180 days and entered into a CART (classification and regression tree) data-mining procedure with a decisional tree j48 (Weka software and 10-fold cross-validation). Outcome was conventionally rated by the Glasgow outcome scale. Results and conclusions: Re-appearance with proper timing of spontaneous motility, eye tracking and oculo-cephalic reflex and disappearance of oral automatisms proved highly correlated to outcome and allowed early and reliable prognosis. These findings are consistent with the brain functional organization thought to sustain consciousness and warrant systematic investigation. classification and regression trees and data-mining procedures proved applicable in neurology to sort out significant clinical signs also in clinical conditions characterized by paucity of signs such as the vegetative state. Extended application in clinical medicine is conceivable based on the approach peculiarities.

关键词： vegetative state early prognosis data mining classification and regression tree

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Modeling major lung resection outcomes using classification trees and multiple imputation techniques

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EUROPEAN JOURNAL OF CARDIO-THORACIC SURGERY 2008年第5期34卷 1085-1089页

作者： Ferguson, Mark K. Siddique, Juned Karrison, Theodore Univ Chicago Dept Surg Chicago IL 60637 USA Univ Chicago Dept Hlth Studies Chicago IL 60637 USA

Objective: Modeling of operative risks associated with major lung resection is potentially inaccurate and inefficient because of incomplete observations for predictor variables (covariates). Missing values do not usually occur randomly, potentially introducing an important source of bias in modeling. Deletion of cases with missing data also results in loss of precision. The current study analyzes incomplete variables as potential predictors of outcomes after major lung resection using imputation techniques. Methods: We analyzed major lung resection patients treated from 1980 to 2006 for predictors of pulmonary, cardiovascular, and overall complications, as well as mortality. Predictive variables were initially determined using classification and regression tree (CART) methods. Imputation models were developed and variables with missing values were multiply imputed. We fit a logistic regression model for each outcome using CART variables and any covariates that were of interest clinically. Results: Of 1046 resected patients, serum albumin and diffusing capacity (DLCO%) had a large number of missing values (32% and 13% missing, respectively). Models included 10 covariates for pulmonary complications (p < 0.05 for DLCO% and forced expiratory volume in the first second [FEV1%]), 12 covariates for cardiovascular complications (p < 0.05 for FEV1%, extent of resection, year of operation, and age), 15 covariates for overall complications (p < 0.05 for DLCO%, performance status, serum albumin, and FEV1/FVC ratio), and 12 covariates for death (p < 0.05 for DLCO%, extent of resection, and operation year). Conclusions: We identified serum albumin as a previously under-reported and strong predictor of overall complications. Serum albumin was marginally significantly related to pulmonary and cardiovascular outcomes after major lung surgery. Use of imputation techniques for modeling surgical risks has potential value in identifying important predictive variables that may ordinaril

关键词： Neoplasm Lung Surgical risk Serum albumin Imputation Diffusing capacity classification and regression tree

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Approximations by Smooth Transitions in Binary Space Partitions

Approximations by Smooth Transitions in Binary Space Partiti...

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21st Brazilian Symposium on Computer Graphics and Image Processing

作者： Lage, Marcos Bordignon, Alex Petronetto, Fabiano Veiga, Alvaro Tavares, Geovan Lewiner, Thomas Lopes, Helio Pontificia Univ Catolica Rio de Janeiro Lab Matmidia Dept Matemat BR-22453 Rio De Janeiro Brazil Pontificia Univ Catolica Rio de Janeiro Dept Elect Engn BR-22453 Rio De Janeiro Brazil

ISBN: (纸本)9780769533582

This work proposes a simple approximation scheme for discrete data that leads to an infinitely smooth result without global optimization. It combines the flexibility of Binary Space Partitions trees with the statistical robustness of Smooth Transition regression trees. The construction of the tree is straightforward and easily controllable, using error-driven metrics or external constraints. Moreover, it leads to a concise representation. Applications on synthetic and real data, both scalar and vector-valued demonstrated the effectiveness of this approach.

关键词： Binary Space Partitions Smooth Transition regression trees classification and regression tree

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Incorporating taxonomic distance into spatial prediction and digital mapping of soil classes

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GEODERMA 2007年第3-4期142卷 285-293页

作者： Minasny, Budiman McBratney, Alex B. Univ Sydney Fac Agr Food & Nat Resources Sydney NSW 2006 Australia

Mapping soil classes digitally generally starts with soil profile description with observed soil classes at a taxonomic level in a particular classification system. At each soil observation location there is a set of co-located environmental variables, and the challenge is to correlate the soil class with the environmental variables. The current methodology treats soil classes as 'labels' and their prediction only considers the minimisation of the misclassification error. Soil classes at any taxonomic level have taxonomic relationships between each other, and in some instances the errors in prediction of certain classes are more serious than the others. No statistical procedure so far has been utilised to account for these relationships. This paper shows that in digital mapping of soil classes, we can incorporate the taxonomic distance between soil classes in a supervised classification routine. Using classification trees, we can specify an algorithm that minimises the taxonomic distance rather than misclassification error. Two examples are given in this paper for mapping soil orders in the Australian soil classification system. A site in the Edgeroi area showed the advantage of using the method that minimises the taxonomic distance. Meanwhile a site in the Hunter Valley showed minimising the misclassification error performed similarly to minimising taxonomic distance. The advantages and challenges of using soil taxonomic distance are discussed. (C) 2007 Elsevier B.V. All rights reserved.

关键词： soil classification spatial prediction decision tree supervised classification classification and regression tree

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Monitoring and verifying agricultural practices related to soil carbon sequestration with satellite imagery

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AGRICULTURE ECOSYSTEMS & ENVIRONMENT 2007年第1-4期118卷 201-210页

作者： Bricklemyer, Ross S. Lawrence, Rick L. Miller, Perry R. Battogtokh, Norov Montana State Univ Dept Land Resources & Environm Sci Bozeman MT 59717 USA Mongolian Soc Sustainable Dev Ulaanbaatar 13 Mongolia

The Kyoto Protocol entering into force on 16 February 2005 continues to spur interest in development of carbon trading mechanisms internationally and domestically. Critical to the development of a carbon trading effort is verification that carbon has been sequestered, and field level measurement of C change is likely cost prohibitive. Estimating C change based on agricultural management practices related to carbon sequestration seems more realistic, and analysis of satellite imagery could be used to monitor and verify these practices over large areas. We examined using Landsat imagery to verify crop rotations and quantify crop residue biomass in north central Montana. Field data were collected using a survey of farms. Standard classification tree analysis (CTA) and boosted classification and regression tree analysis (BCTA) were used to classify crop types. Linear regression (LM), regression tree analysis (RTA), and stochastic gradient boosting (SGB) were used to estimate crop residue. Six crop types were classified with 97% accuracy (BCTA) with class accuracies of 88-99%. Paired t-tests were used to compare the difference between known and predicted mean crop residue biomass. The difference between known and predicted mean residues using SGB was not different than 0 (p-value = 0.99);however root mean square error (RMSE) was large (1981 kg ha(-1)), implying that SGB accurately predicted regional crop residue biomass but not local predictions (i.e., field or farm level). The results of this study, and previous research classifying tillage practices and estimating soil disturbance, supports using satellite imagery as an effective tool for monitoring and verifying agricultural management practices related to carbon sequestration over large areas. Published by Elsevier B.V.

关键词： carbon sequestration crop types crop residue biomass classification and regression tree boosting Landsat imagery

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Physicians' judgement and comprehensive geriatric assessment (CGA) select different patients as fit for chemotherapy

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CRITICAL REVIEWS IN ONCOLOGY HEMATOLOGY 2007年第1期64卷 1-9页

作者： Wedding, Ulrich Koedding, Daphne Pientka, Ludger Steinmetz, Hans T. Schmitz, Stefan Univ Jena Dept Internal Med 2 Div Haematol & Med Oncol D-07747 Jena Germany Ruhr Univ Bochum Dept Geriatr D-4630 Bochum Germany Onkol Praxis Koln D-50677 Cologne Germany

Introduction: Elderly cancer patients are a very heterogeneous population. A comprehensive geriatric assessment (CGA) shall help to identify more precisely those patients who are fit, compared to those who are vulnerable or frail, when deciding on chemotherapeutical treatment. Methods: In a prospective trial, 200 cancer patients treated in an out-patient setting were judged by their physician for their fitness for chemotherapy as fit, vulnerable or frail. A CGA was performed thereafter. We determined the feasibility of a CGA in an out-patient setting and the frequency of changes within the different assessment tools and compared physicians' judgement with the CGA results. Results: Physicians judged 64.3% of their patients as fit, 32.4% as vulnerable, and 3.2% as frail. A CGA was completed by 97.5% of patients and lasted 20 min per patients (range: 9-47 min). 26.5% of all patients had no deficits in the CGA. The CGA identified a mean of 1.7 problems per patient, 1.3 in patients judged as fit, 2.3 in those judged as vulnerable, and 4.2 in those judged as frail. A CGA is more sensitive in classifying patients as fit. compared to vulnerable or frail than physicians' judgement. Conclusion: A CGA is feasible and detects more elderly cancer patients as being unfit for chemotherapy than physicians' judgement. Further trials including disease and treatment related end-points are needed. (C) 2007 Elsevier Ireland Ltd. All rights reserved.

关键词： cancer geriatric oncology comorbidity geriatric assessment elderly decision-making classification and regression tree

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Search is a time-critical event: When search and rescue missions may become futile

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WILDERNESS & ENVIRONMENTAL MEDICINE 2007年第2期18卷 95-101页

作者： Adams, Annette L. Schmidt, Terri A. Newgard, Craig D. Federiuk, Carol S. Christie, Michael Scorvo, Sean DeFreest, Melissa Oregon Hlth & Sci Univ Ctr Policy & Res Emergency Med Portland OR 97239 USA Oregon Hlth & Sci Univ Ctr Policy & Res Emergency Med Dept Emergency Med Portland OR USA Oregon Hlth & Sci Univ Ctr Policy & Res Emergency Med Dept Publ Hlth & Prevent Med Portland OR USA Corvallis Clin Corvallis OR USA Stanford Univ Dept Surg Div Emergency Med Palo Alto CA 94304 USA

Objectives.-The purpose of this study was to derive and validate a rule for duration of search (ie, search time) that maximizes survivors and after which a search and rescue (SAR) mission may be considered for termination. Methods.-This was a retrospective cohort study of all SAR missions initiated in Oregon over a 7-year period, which were documented in a population-based administrative database. The following types of search missions were excluded from analysis: redundant reports of a single search;lost helicopters and airplanes: support of organized events;law-enforcement searches;searches for persons actively avoiding rescued body recovery missions;and cases without outcome information. The cohort was divided into a derivation cohort (searches from 1997-2000) and a validation cohort (2001-2003). The primary Outcome was survival. Variables considered in the model included age, gender, minimum and maximum daily temperatures, precipitation, search time, and whether the search involved an air or water incident. Missing data were handled using Multiple imputation. classification and regression tree (CART) methods were used to derive the model. Results.-The derivation cohort included 1040 searches involving 1509 victims, 70 (4.6%) of whom died. The validation cohort included 1262 searches involving 1778 victims;115 (6.5%) died. Search time was the only variable retained in the final model, with a cut-point of 51 hours. The derivation model was 98.9% sensitive;the same model run using the validation cohort was 99.3% sensitive. Conclusions.-This time-based model may aid search managers in the decision about starting a search or changing search tactics for missing persons.

关键词： search and rescue EMS wilderness medicine classification and regression tree

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