The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Zhuang Miao - One of the best experts on this subject based on the ideXlab platform.
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Nonparametric roc summary Statistics for correlated diagnostic marker data
Statistics in Medicine, 2013Co-Authors: Liansheng Larry Tang, Aiyi Liu, Zhen Chen, Enrique F Schisterman, Bo Zhang, Zhuang MiaoAbstract:We propose efficient Nonparametric Statistics to compare medical imaging modalities in multi-reader multi-test data and to compare markers in longitudinal ROC data. The proposed methods are based on the weighted area under the ROC curve, which includes the area under the curve and the partial area under the curve as special cases. The methods maximize the local power for detecting the difference between imaging modalities. We develop the asymptotic results of the proposed methods under a complex correlation structure. Our simulation studies show that the proposed Statistics result in much better powers than existing Statistics. We apply the proposed Statistics to an endometriosis diagnosis study.
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Nonparametric roc summary Statistics for correlated diagnostic marker data
arXiv: Applications, 2012Co-Authors: Liansheng Larry Tang, Aiyi Liu, Zhen Chen, Enrique F Schisterman, Bo Zhang, Zhuang MiaoAbstract:We propose efficient Nonparametric Statistics to compare medical imaging modalities in multi-reader multi-test data and to compare markers in longitudinal ROC data. The proposed methods are based on the weighted area under the ROC curve which includes the area under the curve and the partial area under the curve as special cases. The methods maximize the local power for detecting the difference between imaging modalities. The asymptotic results of the proposed methods are developed under a complex correlation structure. Our simulation studies show that the proposed Statistics result in much better powers than existing Statistics. We applied the proposed Statistics to an endometriosis diagnosis study.
Daniel E Weeks - One of the best experts on this subject based on the ideXlab platform.
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comparison of Nonparametric Statistics for detection of linkage in nuclear families single marker evaluation
American Journal of Human Genetics, 1997Co-Authors: Sean Davis, Daniel E WeeksAbstract:Summary We have evaluated 23 different Statistics, from a total of 10 popular software packages for model-free linkage analysis of nuclear-family data, by applying them to single-marker data simulated under several two-locus disease models. The Statistics that we examined fall into two broad categories: (1) those that test directly for increased identity-by-state or identity-by-descent sharing (by use of the programs APM, Genetic Analysis System [GAS] SIBSTATE and SIBDES, SAGE SIBPAL, ERPA, SimIBD, and Genehunter NPL) and (2) those that are based on likelihood-ratio tests and that report LOD scores (by use of the programs Splink, SIBPAIR, Mapmaker/Sibs, ASPEX, and GAS SIBMLS). For each of eight two-locus disease models, we analyzed six data sets; the first three data sets consisted of two-child families with both sibs affected and zero, one, or both parents typed, whereas the other three data sets consisted of four-child families with at least two affected sibs and zero, one, or both parents typed. We report false-positive rates, overall rank by power, and the power for each statistic. We give rough recommendations regarding which programs provide the most powerful tests for linkage, as well as the programs to be avoided under certain conditions. For the likelihood-ratio-based Statistics, we examined the effects of various treatments of sibships with multiple affected individuals. Finally, we explored the use of some simple two-of-three composite Statistics and found that such tests are of only marginal benefit over the most powerful single statistic.
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Nonparametric simulation based Statistics for detecting linkage in general pedigrees
American Journal of Human Genetics, 1996Co-Authors: Sean Davis, Mark Schroeder, Lynn R Goldin, Daniel E WeeksAbstract:We present here four Nonparametric Statistics for linkage analysis that test whether pairs of affected relatives share marker alleles more often than expected. These Statistics are based on simulating the null distribution of a given statistic conditional on the unaffecteds` marker genotypes. Each statistic uses a different measure of marker sharing: the SimAPM statistic uses the simulation-based affected-pedigree-member measure based on identity-by-state (IBS) sharing. The SimKIN (kinship) measure is 1.0 for identity-by-descent (IBD) sharing, 0.0 for no IBD sharing, and the kinship coefficient when the IBD status is ambiguous. The simulation-based IBD (SimIBD) statistic uses a recursive algorithm to determine the probability of two affecteds sharing a specific allele IBD. The SimISO statistic is identical to SimIBD, except that it also measures marker similarity between unaffected pairs. We evaluated our Statistics on data simulated under different two-locus disease models, comparing our results to those obtained with several other Nonparametric Statistics. Use of IBD information produces dramatic increases in power over the SimAPM method, which uses only IBS information. The power of our best statistic in most cases meets or exceeds the power of the other Nonparametric Statistics. Furthermore, our Statistics perform comparisons between all affected relative pairs within general pedigreesmore » and are not restricted to sib pairs or nuclear families. 32 refs., 5 figs., 6 tabs.« less
Robert A Simons - One of the best experts on this subject based on the ideXlab platform.
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industrial real estate mortgage default experience of the new york state job development authority second loan program
1999Co-Authors: Robert A SimonsAbstract:This paper explores the loan loss experience of a public industrial lending authority, employing contemporaneous borrower net equity as a link to mortgage loan default. The relationship between default, net equity and bankruptcy is tested on a small longitudinal data set of loans using Nonparametric Statistics and a proportional hazard model. Results show that negative net equity and firm bankruptcy are strongly associated with default among the study population. Further, the borrowers studied did not exercise the put option promptly, suggesting potential benefits from monitoring net equity one year or more prior to default.
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industrial real estate mortgage default experience of the new york state job development authority second loan program a preliminary investigation
Real Estate Economics, 1994Co-Authors: Robert A SimonsAbstract:This paper explores the loan loss experience of a public industrial lending authority, employing contemporaneous borrower net equity as a link to mortgage loan default. The relationship between default, net equity and bankruptcy is tested on a small longitudinal data set of loans using Nonparametric Statistics and a proportional hazard model. Results show that negative net equity and firm bankruptcy are strongly associated with default among the study population. Further, the borrowers studied did not exercise the put option promptly, suggesting potential benefits from monitoring net equity one year or more prior to default. Copyright American Real Estate and Urban Economics Association.
Liansheng Larry Tang - One of the best experts on this subject based on the ideXlab platform.
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Nonparametric roc summary Statistics for correlated diagnostic marker data
Statistics in Medicine, 2013Co-Authors: Liansheng Larry Tang, Aiyi Liu, Zhen Chen, Enrique F Schisterman, Bo Zhang, Zhuang MiaoAbstract:We propose efficient Nonparametric Statistics to compare medical imaging modalities in multi-reader multi-test data and to compare markers in longitudinal ROC data. The proposed methods are based on the weighted area under the ROC curve, which includes the area under the curve and the partial area under the curve as special cases. The methods maximize the local power for detecting the difference between imaging modalities. We develop the asymptotic results of the proposed methods under a complex correlation structure. Our simulation studies show that the proposed Statistics result in much better powers than existing Statistics. We apply the proposed Statistics to an endometriosis diagnosis study.
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Nonparametric roc summary Statistics for correlated diagnostic marker data
arXiv: Applications, 2012Co-Authors: Liansheng Larry Tang, Aiyi Liu, Zhen Chen, Enrique F Schisterman, Bo Zhang, Zhuang MiaoAbstract:We propose efficient Nonparametric Statistics to compare medical imaging modalities in multi-reader multi-test data and to compare markers in longitudinal ROC data. The proposed methods are based on the weighted area under the ROC curve which includes the area under the curve and the partial area under the curve as special cases. The methods maximize the local power for detecting the difference between imaging modalities. The asymptotic results of the proposed methods are developed under a complex correlation structure. Our simulation studies show that the proposed Statistics result in much better powers than existing Statistics. We applied the proposed Statistics to an endometriosis diagnosis study.
Roger Stone - One of the best experts on this subject based on the ideXlab platform.
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inferential Nonparametric Statistics to assess the quality of probabilistic forecast systems
Monthly Weather Review, 2007Co-Authors: Aline De Holanda Nunes Maia, Holger Meinke, Sarah M Lennox, Roger StoneAbstract:Many statistical forecast systems are available to interested users. In order to be useful for decision-making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and their statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of `quality’. However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what ‘quality’ entails and how to measure it, leading to confusion and misinformation. Here we present a generic framework to quantify aspects of forecast quality using an inferential approach to calculate nominal significance levels (p-values) that can be obtained either by directly applying non-parametric statistical tests such as Kruskal-Wallis (KW) or Kolmogorov-Smirnov (KS) or by using Monte-Carlo methods (in the case of forecast skill scores). Once converted to p-values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. Our analysis demonstrates the importance of providing p-values rather than adopting some arbitrarily chosen significance levels such as p < 0.05 or p < 0.01, which is still common practice. This is illustrated by applying non-parametric tests (such as KW and KS) and skill scoring methods (LEPS and RPSS) to the 5-phase Southern Oscillation Index classification system using historical rainfall data from Australia, The Republic of South Africa and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. We found that non-parametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system or quality measure. Eventually such inferential evidence should be complimented by descriptive statistical methods in order to fully assist in operational risk management.
