Parametric and nonparametric Granger causality testing: Linkages between international stock markets
De Gooijer, Jan G.; Sivarajasingham, Selliah
2008-04-01
This study investigates long-term linear and nonlinear causal linkages among eleven stock markets, six industrialized markets and five emerging markets of South-East Asia. We cover the period 1987-2006, taking into account the on-set of the Asian financial crisis of 1997. We first apply a test for the presence of general nonlinearity in vector time series. Substantial differences exist between the pre- and post-crisis period in terms of the total number of significant nonlinear relationships. We then examine both periods, using a new nonparametric test for Granger noncausality and the conventional parametric Granger noncausality test. One major finding is that the Asian stock markets have become more internationally integrated after the Asian financial crisis. An exception is the Sri Lankan market with almost no significant long-term linear and nonlinear causal linkages with other markets. To ensure that any causality is strictly nonlinear in nature, we also examine the nonlinear causal relationships of VAR filtered residuals and VAR filtered squared residuals for the post-crisis sample. We find quite a few remaining significant bi- and uni-directional causal nonlinear relationships in these series. Finally, after filtering the VAR-residuals with GARCH-BEKK models, we show that the nonparametric test statistics are substantially smaller in both magnitude and statistical significance than those before filtering. This indicates that nonlinear causality can, to a large extent, be explained by simple volatility effects.
Nonparametric Analyses of Log-Periodic Precursors to Financial Crashes
Zhou, Wei-Xing; Sornette, Didier
We apply two nonparametric methods to further test the hypothesis that log-periodicity characterizes the detrended price trajectory of large financial indices prior to financial crashes or strong corrections. The term "parametric" refers here to the use of the log-periodic power law formula to fit the data; in contrast, "nonparametric" refers to the use of general tools such as Fourier transform, and in the present case the Hilbert transform and the so-called (H, q)-analysis. The analysis using the (H, q)-derivative is applied to seven time series ending with the October 1987 crash, the October 1997 correction and the April 2000 crash of the Dow Jones Industrial Average (DJIA), the Standard & Poor 500 and Nasdaq indices. The Hilbert transform is applied to two detrended price time series in terms of the ln(tc-t) variable, where tc is the time of the crash. Taking all results together, we find strong evidence for a universal fundamental log-frequency f=1.02±0.05 corresponding to the scaling ratio λ=2.67±0.12. These values are in very good agreement with those obtained in earlier works with different parametric techniques. This note is extracted from a long unpublished report with 58 figures available at , which extensively describes the evidence we have accumulated on these seven time series, in particular by presenting all relevant details so that the reader can judge for himself or herself the validity and robustness of the results.
DEFF Research Database (Denmark)
Tan, Qihua; Zhao, J H; Iachine, I
2004-01-01
This report investigates the power issue in applying the non-parametric linkage analysis of affected sib-pairs (ASP) [Kruglyak and Lander, 1995: Am J Hum Genet 57:439-454] to localize genes that contribute to human longevity using long-lived sib-pairs. Data were simulated by introducing a recently...... developed statistical model for measuring marker-longevity associations [Yashin et al., 1999: Am J Hum Genet 65:1178-1193], enabling direct power comparison between linkage and association approaches. The non-parametric linkage (NPL) scores estimated in the region harboring the causal allele are evaluated...... in case of a dominant effect. Although the power issue may depend heavily on the true genetic nature in maintaining survival, our study suggests that results from small-scale sib-pair investigations should be referred with caution, given the complexity of human longevity....
Riihimäki, Jaakko; Sund, Reijo; Vehtari, Aki
2010-06-01
Effective utilisation of limited resources is a challenge for health care providers. Accurate and relevant information extracted from the length of stay distributions is useful for management purposes. Patient care episodes can be reconstructed from the comprehensive health registers, and in this paper we develop a Bayesian approach to analyse the length of care episode after a fractured hip. We model the large scale data with a flexible nonparametric multilayer perceptron network and with a parametric Weibull mixture model. To assess the performances of the models, we estimate expected utilities using predictive density as a utility measure. Since the model parameters cannot be directly compared, we focus on observables, and estimate the relevances of patient explanatory variables in predicting the length of stay. To demonstrate how the use of the nonparametric flexible model is advantageous for this complex health care data, we also study joint effects of variables in predictions, and visualise nonlinearities and interactions found in the data.
Directory of Open Access Journals (Sweden)
Lars Ängquist
2008-01-01
Full Text Available In this article we try to discuss nonparametric linkage (NPL score functions within a broad and quite general framework. The main focus of the paper is the structure, derivation principles and interpretations of the score function entity itself. We define and discuss several families of one-locus score function definitions, i.e. the implicit, explicit and optimal ones. Some generalizations and comments to the two-locus, unconditional and conditional, cases are included as well. Although this article mainly aims at serving as an overview, where the concept of score functions are put into a covering context, we generalize the noncentrality parameter (NCP optimal score functions in Ängquist et al. (2007 to facilitate—through weighting—for incorporation of several plausible distinct genetic models. Since the genetic model itself most oftenly is to some extent unknown this facilitates weaker prior assumptions with respect to plausible true disease models without loosing the property of NCP-optimality. Moreover, we discuss general assumptions and properties of score functions in the above sense. For instance, the concept of identical by descent (IBD sharing structures and score function equivalence are discussed in some detail.
HLA region excluded by linkage analyses of early onset periodontitis
Energy Technology Data Exchange (ETDEWEB)
Sun, C.; Wang, S.; Lopez, N.
1994-09-01
Previous studies suggested that HLA genes may influence susceptibility to early-onset periodontitis (EOP). Segregation analyses indicate that EOP may be due to a single major gene. We conducted linkage analyses to assess possible HLA effects on EOP. Fifty families with two or more close relatives affected by EOP were ascertained in Virginia and Chile. A microsatellite polymorphism within the HLA region (at the tumor necrosis factor beta locus) was typed using PCR. Linkage analyses used a donimant model most strongly supported by previous studies. Assuming locus homogeneity, our results exclude a susceptibility gene within 10 cM on either side of our marker locus. This encompasses all of the HLA region. Analyses assuming alternative models gave qualitatively similar results. Allowing for locus heterogeneity, our data still provide no support for HLA-region involvement. However, our data do not statistically exclude (LOD <-2.0) hypotheses of disease-locus heterogeneity, including models where up to half of our families could contain an EOP disease gene located in the HLA region. This is due to the limited power of even our relatively large collection of families and the inherent difficulties of mapping genes for disorders that have complex and heterogeneous etiologies. Additional statistical analyses, recruitment of families, and typing of flanking DNA markers are planned to more conclusively address these issues with respect to the HLA region and other candidate locations in the human genome. Additional results for markers covering most of the human genome will also be presented.
Effects of dating errors on nonparametric trend analyses of speleothem time series
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M. Mudelsee
2012-10-01
Full Text Available A fundamental problem in paleoclimatology is to take fully into account the various error sources when examining proxy records with quantitative methods of statistical time series analysis. Records from dated climate archives such as speleothems add extra uncertainty from the age determination to the other sources that consist in measurement and proxy errors. This paper examines three stalagmite time series of oxygen isotopic composition (δ^{18}O from two caves in western Germany, the series AH-1 from the Atta Cave and the series Bu1 and Bu4 from the Bunker Cave. These records carry regional information about past changes in winter precipitation and temperature. U/Th and radiocarbon dating reveals that they cover the later part of the Holocene, the past 8.6 thousand years (ka. We analyse centennial- to millennial-scale climate trends by means of nonparametric Gasser–Müller kernel regression. Error bands around fitted trend curves are determined by combining (1 block bootstrap resampling to preserve noise properties (shape, autocorrelation of the δ^{18}O residuals and (2 timescale simulations (models StalAge and iscam. The timescale error influences on centennial- to millennial-scale trend estimation are not excessively large. We find a "mid-Holocene climate double-swing", from warm to cold to warm winter conditions (6.5 ka to 6.0 ka to 5.1 ka, with warm–cold amplitudes of around 0.5‰ δ^{18}O; this finding is documented by all three records with high confidence. We also quantify the Medieval Warm Period (MWP, the Little Ice Age (LIA and the current warmth. Our analyses cannot unequivocally support the conclusion that current regional winter climate is warmer than that during the MWP.
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Vychytilova Jana
2015-09-01
Full Text Available This paper aims to explore specific cross-asset market correlations over the past fifteen- yearperiod-from January 04, 1999 till April 01, 2015, and within four sub-phases covering both the crisis and the non-crisis periods. On the basis of multivariate statistical methods, we focus on investigating relations between selected well-known market indices- U.S. treasury bond yields- the 30-year treasury yield index (TYX and the 10-year treasury yield (TNX; commodity futures the TR/J CRB; and implied volatility of S&P 500 index- the VIX. We estimate relative logarithmic returns by using monthly close prices adjusted for dividends and splits and run normality and correlation analyses. This paper indicates that the TR/J CRB can be adequately modeled by a normal distribution, whereas the rest of benchmarks do not come from a normal distribution. This paper, inter alia, points out some evidence of a statistically significant negative relationship between bond yields and the VIX in the past fifteen years and a statistically significant negative linkage between the TR/J CRB and the VIX since 2009. In rather general terms, this paper thereafter supports the a priori idea- financial markets are interconnected. Such knowledge can be beneficial for building and testing accurate financial market models, and particularly for the understanding and recognizing market cycles.
Linkage and related analyses of Barrett's esophagus and its associated adenocarcinomas.
Sun, Xiangqing; Elston, Robert; Falk, Gary W; Grady, William M; Faulx, Ashley; Mittal, Sumeet K; Canto, Marcia I; Shaheen, Nicholas J; Wang, Jean S; Iyer, Prasad G; Abrams, Julian A; Willis, Joseph E; Guda, Kishore; Markowitz, Sanford; Barnholtz-Sloan, Jill S; Chandar, Apoorva; Brock, Wendy; Chak, Amitabh
2016-07-01
Familial aggregation and segregation analysis studies have provided evidence of a genetic basis for esophageal adenocarcinoma (EAC) and its premalignant precursor, Barrett's esophagus (BE). We aim to demonstrate the utility of linkage analysis to identify the genomic regions that might contain the genetic variants that predispose individuals to this complex trait (BE and EAC). We genotyped 144 individuals in 42 multiplex pedigrees chosen from 1000 singly ascertained BE/EAC pedigrees, and performed both model-based and model-free linkage analyses, using S.A.G.E. and other software. Segregation models were fitted, from the data on both the 42 pedigrees and the 1000 pedigrees, to determine parameters for performing model-based linkage analysis. Model-based and model-free linkage analyses were conducted in two sets of pedigrees: the 42 pedigrees and a subset of 18 pedigrees with female affected members that are expected to be more genetically homogeneous. Genome-wide associations were also tested in these families. Linkage analyses on the 42 pedigrees identified several regions consistently suggestive of linkage by different linkage analysis methods on chromosomes 2q31, 12q23, and 4p14. A linkage on 15q26 is the only consistent linkage region identified in the 18 female-affected pedigrees, in which the linkage signal is higher than in the 42 pedigrees. Other tentative linkage signals are also reported. Our linkage study of BE/EAC pedigrees identified linkage regions on chromosomes 2, 4, 12, and 15, with some reported associations located within our linkage peaks. Our linkage results can help prioritize association tests to delineate the genetic determinants underlying susceptibility to BE and EAC.
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Charles Onyutha
2017-10-01
Full Text Available Some of the problems in drought assessments are that: analyses tend to focus on coarse temporal scales, many of the methods yield skewed indices, a few terminologies are ambiguously used, and analyses comprise an implicit assumption that the observations come from a stationary process. To solve these problems, this paper introduces non-stationary frequency analyses of quantiles. How to use non-parametric rescaling to obtain robust indices that are not (or minimally skewed is also introduced. To avoid ambiguity, some concepts on, e.g., incidence, extremity, etc., were revisited through shift from monthly to daily time scale. Demonstrations on the introduced methods were made using daily flow and precipitation insufficiency (precipitation minus potential evapotranspiration from the Blue Nile basin in Africa. Results show that, when a significant trend exists in extreme events, stationarity-based quantiles can be far different from those when non-stationarity is considered. The introduced non-parametric indices were found to closely agree with the well-known standardized precipitation evapotranspiration indices in many aspects but skewness. Apart from revisiting some concepts, the advantages of the use of fine instead of coarse time scales in drought assessment were given. The links for obtaining freely downloadable tools on how to implement the introduced methods were provided.
Linkage and mapping analyses of the no glue egg gene Ng in the ...
African Journals Online (AJOL)
Jane
2011-08-24
Aug 24, 2011 ... The Ng gene was mapped at 28.0 of the silkworm classical genetic linkage group 12. (Xiang, 1995). In recent years, molecular biology has made consider- able progress ..... project (08080703017), China agriculture research.
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Chipperfield James O.
2015-09-01
Full Text Available Record linkage is the act of bringing together records that are believed to belong to the same unit (e.g., person or business from two or more files. Record linkage is not an error-free process and can lead to linking a pair of records that do not belong to the same unit. This occurs because linking fields on the files, which ideally would uniquely identify each unit, are often imperfect. There has been an explosion of record linkage applications, particularly involving government agencies and in the field of health, yet there has been little work on making correct inference using such linked files. Naively treating a linked file as if it were linked without errors can lead to biased inferences. This article develops a method of making inferences for cross tabulated variables when record linkage is not an error-free process. In particular, it develops a parametric bootstrap approach to estimation which can accommodate the sophisticated probabilistic record linkage techniques that are widely used in practice (e.g., 1-1 linkage. The article demonstrates the effectiveness of this method in a simulation and in a real application.
Linkage and mapping analyses of the no glue egg gene Ng in the ...
African Journals Online (AJOL)
In the silkworm, Bombyx mori, no glue egg is mainly controlled by Ng (No glue) gene, which is located on the 12th chromosome. Owning to a lack of crossing over in females, reciprocal backcrossed F1 (BC1) progenies were used for linkage analysis and mapping of the Ng gene based on the simple sequence repeats ...
Yan, Liying; Huang, Lei; Xu, Liya; Huang, Jin; Ma, Fei; Zhu, Xiaohui; Tang, Yaqiong; Liu, Mingshan; Lian, Ying; Liu, Ping; Li, Rong; Lu, Sijia; Tang, Fuchou; Qiao, Jie; Xie, X Sunney
2015-12-29
In vitro fertilization (IVF), preimplantation genetic diagnosis (PGD), and preimplantation genetic screening (PGS) help patients to select embryos free of monogenic diseases and aneuploidy (chromosome abnormality). Next-generation sequencing (NGS) methods, while experiencing a rapid cost reduction, have improved the precision of PGD/PGS. However, the precision of PGD has been limited by the false-positive and false-negative single-nucleotide variations (SNVs), which are not acceptable in IVF and can be circumvented by linkage analyses, such as short tandem repeats or karyomapping. It is noteworthy that existing methods of detecting SNV/copy number variation (CNV) and linkage analysis often require separate procedures for the same embryo. Here we report an NGS-based PGD/PGS procedure that can simultaneously detect a single-gene disorder and aneuploidy and is capable of linkage analysis in a cost-effective way. This method, called "mutated allele revealed by sequencing with aneuploidy and linkage analyses" (MARSALA), involves multiple annealing and looping-based amplification cycles (MALBAC) for single-cell whole-genome amplification. Aneuploidy is determined by CNVs, whereas SNVs associated with the monogenic diseases are detected by PCR amplification of the MALBAC product. The false-positive and -negative SNVs are avoided by an NGS-based linkage analysis. Two healthy babies, free of the monogenic diseases of their parents, were born after such embryo selection. The monogenic diseases originated from a single base mutation on the autosome and the X-chromosome of the disease-carrying father and mother, respectively.
The power to detect linkage in complex disease by means of simple LOD-score analyses.
Greenberg, D A; Abreu, P; Hodge, S E
1998-09-01
Maximum-likelihood analysis (via LOD score) provides the most powerful method for finding linkage when the mode of inheritance (MOI) is known. However, because one must assume an MOI, the application of LOD-score analysis to complex disease has been questioned. Although it is known that one can legitimately maximize the maximum LOD score with respect to genetic parameters, this approach raises three concerns: (1) multiple testing, (2) effect on power to detect linkage, and (3) adequacy of the approximate MOI for the true MOI. We evaluated the power of LOD scores to detect linkage when the true MOI was complex but a LOD score analysis assumed simple models. We simulated data from 14 different genetic models, including dominant and recessive at high (80%) and low (20%) penetrances, intermediate models, and several additive two-locus models. We calculated LOD scores by assuming two simple models, dominant and recessive, each with 50% penetrance, then took the higher of the two LOD scores as the raw test statistic and corrected for multiple tests. We call this test statistic "MMLS-C." We found that the ELODs for MMLS-C are >=80% of the ELOD under the true model when the ELOD for the true model is >=3. Similarly, the power to reach a given LOD score was usually >=80% that of the true model, when the power under the true model was >=60%. These results underscore that a critical factor in LOD-score analysis is the MOI at the linked locus, not that of the disease or trait per se. Thus, a limited set of simple genetic models in LOD-score analysis can work well in testing for linkage.
He, Wei; Li, Xin; Chen, Jiajing; Xu, Ling; Zhang, Feng; Dai, Qiushi; Cui, Hao; Wang, Duen-Mei; Yu, Jun; Hu, Songnian; Lu, Shan
2011-03-01
The aim of the study was to characterize the underlying mutation in a large multiplex Chinese family with hereditary nuclear cataract. A 6-generation Chinese family having hereditary nuclear cataract was recruited and clinically verified. Blood DNA samples were obtained from 53 available family members. Linkage analyses were performed on the known candidate regions for hereditary cataract with 36 polymorphic microsatellite markers. To identify mutations related to cataract, a direct sequencing approach was applied to a candidate gene residing in our linkage locus. A linkage locus was identified with a maximum 2-point LOD score of 4.31 (recombination fraction = 0) at marker D1S498 and a maximum multipoint LOD score of 5.7 between markers D1S2344 and D1S498 on chromosome 1q21.1, where the candidate gene Cx50 is located. Direct sequencing of Cx50 showed a 139 G to A transition occurred in all affected family members. This transitional mutation resulted in a replacement of aspartic acid by asparagine at residue 47 (D47N) and led to a loss-of-function of the protein. The D47N mutation of Cx50 causes the hereditary nuclear cataract in this family in an autosomal dominant mode of inheritance with incomplete penetrance.
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Carolina Klagges
Full Text Available Despite the agronomical importance and high synteny with other Prunus species, breeding improvements for cherry have been slow compared to other temperate fruits, such as apple or peach. However, the recent release of the peach genome v1.0 by the International Peach Genome Initiative and the sequencing of cherry accessions to identify Single Nucleotide Polymorphisms (SNPs provide an excellent basis for the advancement of cherry genetic and genomic studies. The availability of dense genetic linkage maps in phenotyped segregating progenies would be a valuable tool for breeders and geneticists. Using two sweet cherry (Prunus avium L. intra-specific progenies derived from crosses between 'Black Tartarian' × 'Kordia' (BT×K and 'Regina' × 'Lapins'(R×L, high-density genetic maps of the four parental lines and the two segregating populations were constructed. For BT×K and R×L, 89 and 121 F(1 plants were used for linkage mapping, respectively. A total of 5,696 SNP markers were tested in each progeny. As a result of these analyses, 723 and 687 markers were mapped into eight linkage groups (LGs in BT×K and R×L, respectively. The resulting maps spanned 752.9 and 639.9 cM with an average distance of 1.1 and 0.9 cM between adjacent markers in BT×K and R×L, respectively. The maps displayed high synteny and co-linearity between each other, with the Prunus bin map, and with the peach genome v1.0 for all eight LGs (LG1-LG8. These maps provide a useful tool for investigating traits of interest in sweet cherry and represent a qualitative advance in the understanding of the cherry genome and its synteny with other members of the Rosaceae family.
Linkage analysis: Inadequate for detecting susceptibility loci in complex disorders?
Energy Technology Data Exchange (ETDEWEB)
Field, L.L.; Nagatomi, J. [Univ. of Calgary, Alberta (Canada)
1994-09-01
Insulin-dependent diabetes mellitus (IDDM) may provide valuable clues about approaches to detecting susceptibility loci in other oligogenic disorders. Numerous studies have demonstrated significant association between IDDM and a VNTR in the 5{prime} flanking region of the insulin (INS) gene. Paradoxically, all attempts to demonstrate linkage of IDDM to this VNTR have failed. Lack of linkage has been attributed to insufficient marker locus information, genetic heterogeneity, or high frequency of the IDDM-predisposing allele in the general population. Tyrosine hydroxylase (TH) is located 2.7 kb from INS on the 5` side of the VNTR and shows linkage disequilibrium with INS region loci. We typed a highly polymorphic microsatellite within TH in 176 multiplex families, and performed parametric (lod score) linkage analysis using various intermediate reduced penetrance models for IDDM (including rare and common disease allele frequencies), as well as non-parametric (affected sib pair) linkage analysis. The scores significantly reject linkage for recombination values of .05 or less, excluding the entire 19 kb region containing TH, the 5{prime} VNTR, the INS gene, and IGF2 on the 3{prime} side of INS. Non-parametric linkage analysis also provided no significant evidence for linkage (mean TH allele sharing 52.5%, P=.12). These results have important implications for efforts to locate genes predisposing to complex disorders, strongly suggesting that regions which are significantly excluded by linkage methods may nevertheless contain predisposing genes readily detectable by association methods. We advocate that investigators routinely perform association analyses in addition to linkage analyses.
Candidate gene linkage approach to identify DNA variants that predispose to preterm birth
DEFF Research Database (Denmark)
Bream, Elise N A; Leppellere, Cara R; Cooper, Margaret E
2013-01-01
Background:The aim of this study was to identify genetic variants contributing to preterm birth (PTB) using a linkage candidate gene approach.Methods:We studied 99 single-nucleotide polymorphisms (SNPs) for 33 genes in 257 families with PTBs segregating. Nonparametric and parametric analyses were...... through the infant and/or the mother in the etiology of PTB....
A contingency table approach to nonparametric testing
Rayner, JCW
2000-01-01
Most texts on nonparametric techniques concentrate on location and linear-linear (correlation) tests, with less emphasis on dispersion effects and linear-quadratic tests. Tests for higher moment effects are virtually ignored. Using a fresh approach, A Contingency Table Approach to Nonparametric Testing unifies and extends the popular, standard tests by linking them to tests based on models for data that can be presented in contingency tables.This approach unifies popular nonparametric statistical inference and makes the traditional, most commonly performed nonparametric analyses much more comp
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Velculescu Victor E
2008-04-01
Full Text Available Abstract Background Colorectal cancer is one of the most common causes of cancer-related mortality. The disease is clinically and genetically heterogeneous though a strong hereditary component has been identified. However, only a small proportion of the inherited susceptibility can be ascribed to dominant syndromes, such as Hereditary Non-Polyposis Colorectal Cancer (HNPCC or Familial Adenomatous Polyposis (FAP. In an attempt to identify novel colorectal cancer predisposing genes, we have performed a genome-wide linkage analysis in 30 Swedish non-FAP/non-HNPCC families with a strong family history of colorectal cancer. Methods Statistical analysis was performed using multipoint parametric and nonparametric linkage. Results Parametric analysis under the assumption of locus homogeneity excluded any common susceptibility regions harbouring a predisposing gene for colorectal cancer. However, several loci on chromosomes 2q, 3q, 6q, and 7q with suggestive linkage were detected in the parametric analysis under the assumption of locus heterogeneity as well as in the nonparametric analysis. Among these loci, the locus on chromosome 3q21.1-q26.2 was the most consistent finding providing positive results in both parametric and nonparametric analyses Heterogeneity LOD score (HLOD = 1.90, alpha = 0.45, Non-Parametric LOD score (NPL = 2.1. Conclusion The strongest evidence of linkage was seen for the region on chromosome 3. Interestingly, the same region has recently been reported as the most significant finding in a genome-wide analysis performed with SNP arrays; thus our results independently support the finding on chromosome 3q.
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Hong Liu
Full Text Available As a genetic disorder of abnormal pigmentation, the molecular basis of dyschromatosis universalis hereditaria (DUH had remained unclear until recently when ABCB6 was reported as a causative gene of DUH.We performed genome-wide linkage scan using Illumina Human 660W-Quad BeadChip and exome sequencing analyses using Agilent SureSelect Human All Exon Kits in a multiplex Chinese DUH family to identify the pathogenic mutations and verified the candidate mutations using Sanger sequencing. Quantitative RT-PCR and Immunohistochemistry was performed to verify the expression of the pathogenic gene, Zebrafish was also used to confirm the functional role of ABCB6 in melanocytes and pigmentation.Genome-wide linkage (assuming autosomal dominant inheritance mode and exome sequencing analyses identified ABCB6 as the disease candidate gene by discovering a coding mutation (c.1358C>T; p.Ala453Val that co-segregates with the disease phenotype. Further mutation analysis of ABCB6 in four other DUH families and two sporadic cases by Sanger sequencing confirmed the mutation (c.1358C>T; p.Ala453Val and discovered a second, co-segregating coding mutation (c.964A>C; p.Ser322Lys in one of the four families. Both mutations were heterozygous in DUH patients and not present in the 1000 Genome Project and dbSNP database as well as 1,516 unrelated Chinese healthy controls. Expression analysis in human skin and mutagenesis interrogation in zebrafish confirmed the functional role of ABCB6 in melanocytes and pigmentation. Given the involvement of ABCB6 mutations in coloboma, we performed ophthalmological examination of the DUH carriers of ABCB6 mutations and found ocular abnormalities in them.Our study has advanced our understanding of DUH pathogenesis and revealed the shared pathological mechanism between pigmentary DUH and ocular coloboma.
International Nuclear Information System (INIS)
Hinton, T.G.; Brechignac, F.
2005-01-01
Bio-markers are successfully used in human risk analyses as early indicators of contaminant exposure and predictors of deleterious effects. This has boosted the search for bio-markers in determining ecological risks to non-human biota, and particularly for assessments related to radioactive contaminants. There are difficulties, however, that prevent an easy transfer of the bio-marker concept from humans to non-human biota, as there are significant differences in endpoints of concern, units of observation and dose response relationships between human and ecological risk analyses. The use of bio-markers in ecological risk analyses currently lacks a linkage between molecular-level effects and quantifiable impacts observed in individuals and populations. This is important because ecological risk analyses generally target the population level of biological organisation. We highlight various examples that demonstrate the difficulties of linking individual responses to population-level impacts, such as indirect effects and compensatory interactions. Eco-toxicologists cope with such difficulties through the use of uncertainty or extrapolation factors. Extrapolation factors (EF) typically range from 1 to 1000 when linking effects observed in individuals to those predicted to occur in populations. We question what magnitude of EF will be required when going from a molecular level effect, measured by a bio-marker, all the way up to the population level of biological organisation. Particularly, we stress that a successful application of bio-markers to radioecological risk assessment can only be achieved once the connection has been made between changes in individual resource allocation-based life histories and population dynamics. This clearly emphasises the need to quantify the propagation of molecular and cellular level effects to higher levels of biological organisation, especially in the long-term via several generations of exposure. Finally, we identify pertinent research
Nonparametric statistical inference
Gibbons, Jean Dickinson
2010-01-01
Overall, this remains a very fine book suitable for a graduate-level course in nonparametric statistics. I recommend it for all people interested in learning the basic ideas of nonparametric statistical inference.-Eugenia Stoimenova, Journal of Applied Statistics, June 2012… one of the best books available for a graduate (or advanced undergraduate) text for a theory course on nonparametric statistics. … a very well-written and organized book on nonparametric statistics, especially useful and recommended for teachers and graduate students.-Biometrics, 67, September 2011This excellently presente
Introduction to nonparametric statistics for the biological sciences using R
MacFarland, Thomas W
2016-01-01
This book contains a rich set of tools for nonparametric analyses, and the purpose of this supplemental text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences: To introduce when nonparametric approaches to data analysis are appropriate To introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test To introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively. Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses a...
On Cooper's Nonparametric Test.
Schmeidler, James
1978-01-01
The basic assumption of Cooper's nonparametric test for trend (EJ 125 069) is questioned. It is contended that the proper assumption alters the distribution of the statistic and reduces its usefulness. (JKS)
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Eveline Diopere
Full Text Available Linkage maps based on markers derived from genes are essential evolutionary tools for commercial marine fish to help identify genomic regions associated with complex traits and subject to selective forces at play during exploitation or selective breeding. Additionally, they allow the use of genomic information from other related species for which more detailed information is available. Sole (solea solea L. is a commercially important flatfish species in the North Sea, subject to overexploitation and showing evidence of fisheries-induced evolutionary changes in growth- and maturation-related traits. Sole would definitely benefit from a linkage map to better understand how evolution has shaped its genome structure. This study presents a linkage map of sole based on 423 single nucleotide polymorphisms derived from expressed sequence tags and 8 neutral microsatellite markers. The total map length is 1233.8 cM and consists of 38 linkage groups with a size varying between 0 to 92.1 cM. Being derived from expressed sequence tags allowed us to align the map with the genome of four model fish species, namely medaka (Oryzias latipes, Nile tilapia (Oreochromis niloticus, three-spined stickleback (Gasterosteus aculeatus and green spotted pufferfish (Tetraodon nigroviridis. This comparison revealed multiple conserved syntenic regions with all four species, and suggested that the linkage groups represent 21 putative sole chromosomes. The map was also compared to the linkage map of turbot (Scophthalmus maximus, another commercially important flatfish species and closely related to sole. For all putative sole chromosomes (except one a turbot homolog was detected, confirming the even higher degree of synteny between these two flatfish species.
Sung, Yun Ju; Di, Yanming; Fu, Audrey Q; Rothstein, Joseph H; Sieh, Weiva; Tong, Liping; Thompson, Elizabeth A; Wijsman, Ellen M
2007-01-01
We performed multipoint linkage analyses with multiple programs and models for several gene expression traits in the Centre d'Etude du Polymorphisme Humain families. All analyses provided consistent results for both peak location and shape. Variance-components (VC) analysis gave wider peaks and Bayes factors gave fewer peaks. Among programs from the MORGAN package, lm_multiple performed better than lm_markers, resulting in less Markov-chain Monte Carlo (MCMC) variability between runs, and the program lm_twoqtl provided higher LOD scores by also including either a polygenic component or an additional quantitative trait locus.
Nonparametric Transfer Function Models
Liu, Jun M.; Chen, Rong; Yao, Qiwei
2009-01-01
In this paper a class of nonparametric transfer function models is proposed to model nonlinear relationships between ‘input’ and ‘output’ time series. The transfer function is smooth with unknown functional forms, and the noise is assumed to be a stationary autoregressive-moving average (ARMA) process. The nonparametric transfer function is estimated jointly with the ARMA parameters. By modeling the correlation in the noise, the transfer function can be estimated more efficiently. The parsimonious ARMA structure improves the estimation efficiency in finite samples. The asymptotic properties of the estimators are investigated. The finite-sample properties are illustrated through simulations and one empirical example. PMID:20628584
Dickhaus, Thorsten
2018-01-01
This textbook provides a self-contained presentation of the main concepts and methods of nonparametric statistical testing, with a particular focus on the theoretical foundations of goodness-of-fit tests, rank tests, resampling tests, and projection tests. The substitution principle is employed as a unified approach to the nonparametric test problems discussed. In addition to mathematical theory, it also includes numerous examples and computer implementations. The book is intended for advanced undergraduate, graduate, and postdoc students as well as young researchers. Readers should be familiar with the basic concepts of mathematical statistics typically covered in introductory statistics courses.
Bayesian nonparametric data analysis
Müller, Peter; Jara, Alejandro; Hanson, Tim
2015-01-01
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages.
Bayesian nonparametric hierarchical modeling.
Dunson, David B
2009-04-01
In biomedical research, hierarchical models are very widely used to accommodate dependence in multivariate and longitudinal data and for borrowing of information across data from different sources. A primary concern in hierarchical modeling is sensitivity to parametric assumptions, such as linearity and normality of the random effects. Parametric assumptions on latent variable distributions can be challenging to check and are typically unwarranted, given available prior knowledge. This article reviews some recent developments in Bayesian nonparametric methods motivated by complex, multivariate and functional data collected in biomedical studies. The author provides a brief review of flexible parametric approaches relying on finite mixtures and latent class modeling. Dirichlet process mixture models are motivated by the need to generalize these approaches to avoid assuming a fixed finite number of classes. Focusing on an epidemiology application, the author illustrates the practical utility and potential of nonparametric Bayes methods.
Quantal Response: Nonparametric Modeling
2017-01-01
capture the behavior of observed phenomena. Higher-order polynomial and finite-dimensional spline basis models allow for more complicated responses as the...flexibility as these are nonparametric (not constrained to any particular functional form). These should be useful in identifying nonstandard behavior via... deviance ∆ = −2 log(Lreduced/Lfull) is defined in terms of the likelihood function L. For normal error, Lfull = 1, and based on Eq. A-2, we have log
Panel data specifications in nonparametric kernel regression
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...
Ellis, Justine A; Scurrah, Katrina J; Duncan, Anna E; Lamantia, Angela; Byrnes, Graham B; Harrap, Stephen B
2007-04-01
There have been a number of genome-wide linkage studies for adult height in recent years. These studies have yielded few well-replicated loci, and none have been further confirmed by the identification of associated gene variants. The inconsistent results may be attributable to the fact that few studies have combined accurate phenotype measures with informative statistical modelling in healthy populations. We have performed a multi-stage genome-wide linkage analysis for height in 275 adult sibling pairs drawn randomly from the Victorian Family Heart Study (VFHS), a healthy population-based Caucasian cohort. Height was carefully measured in a standardised fashion on regularly calibrated equipment. Following genome-wide identification of a peak Z-score of 3.14 on chromosome 3 at 69 cM, we performed a fine-mapping analysis of this region in an extended sample of 392 two-generation families. We used a number of variance components models that incorporated assortative mating and shared environment effects, and we observed a peak LOD score of approximately 3.5 at 78 cM in four of the five models tested. We also demonstrated that the most prevalent model in the literature gave the worst fit, and the lowest LOD score (2.9) demonstrating the importance of appropriate modelling. The region identified in this study replicates the results of other genome-wide scans of height and bone-related phenotypes, strongly suggesting the presence of a gene important in bone growth on chromosome 3p. Association analyses of relevant candidate genes should identify the genetic variants responsible for the chromosome 3p linkage signal in our population.
Nonparametric statistical inference
Gibbons, Jean Dickinson
2014-01-01
Thoroughly revised and reorganized, the fourth edition presents in-depth coverage of the theory and methods of the most widely used nonparametric procedures in statistical analysis and offers example applications appropriate for all areas of the social, behavioral, and life sciences. The book presents new material on the quantiles, the calculation of exact and simulated power, multiple comparisons, additional goodness-of-fit tests, methods of analysis of count data, and modern computer applications using MINITAB, SAS, and STATXACT. It includes tabular guides for simplified applications of tests and finding P values and confidence interval estimates.
Ray, Jessica L; Althammer, Julia; Skaar, Katrine S; Simonelli, Paolo; Larsen, Aud; Stoecker, Diane; Sazhin, Andrey; Ijaz, Umer Z; Quince, Christopher; Nejstgaard, Jens C; Frischer, Marc; Pohnert, Georg; Troedsson, Christofer
2016-11-01
In order to characterize copepod feeding in relation to microbial plankton community dynamics, we combined metabarcoding and metabolome analyses during a 22-day seawater mesocosm experiment. Nutrient amendment of mesocosms promoted the development of haptophyte (Phaeocystis pouchetii)- and diatom (Skeletonema marinoi)-dominated plankton communities in mesocosms, in which Calanus sp. copepods were incubated for 24 h in flow-through chambers to allow access to prey particles (<500 μm). Copepods and mesocosm water sampled six times spanning the experiment were analysed using metabarcoding, while intracellular metabolite profiles of mesocosm plankton communities were generated for all experimental days. Taxon-specific metabarcoding ratios (ratio of consumed prey to available prey in the surrounding seawater) revealed diverse and dynamic copepod feeding selection, with positive selection on large diatoms, heterotrophic nanoflagellates and fungi, while smaller phytoplankton, including P. pouchetii, were passively consumed or even negatively selected according to our indicator. Our analysis of the relationship between Calanus grazing ratios and intracellular metabolite profiles indicates the importance of carbohydrates and lipids in plankton succession and copepod-prey interactions. This molecular characterization of Calanus sp. grazing therefore provides new evidence for selective feeding in mixed plankton assemblages and corroborates previous findings that copepod grazing may be coupled to the developmental and metabolic stage of the entire prey community rather than to individual prey abundances. © 2016 John Wiley & Sons Ltd.
Nonparametric combinatorial sequence models.
Wauthier, Fabian L; Jordan, Michael I; Jojic, Nebojsa
2011-11-01
This work considers biological sequences that exhibit combinatorial structures in their composition: groups of positions of the aligned sequences are "linked" and covary as one unit across sequences. If multiple such groups exist, complex interactions can emerge between them. Sequences of this kind arise frequently in biology but methodologies for analyzing them are still being developed. This article presents a nonparametric prior on sequences which allows combinatorial structures to emerge and which induces a posterior distribution over factorized sequence representations. We carry out experiments on three biological sequence families which indicate that combinatorial structures are indeed present and that combinatorial sequence models can more succinctly describe them than simpler mixture models. We conclude with an application to MHC binding prediction which highlights the utility of the posterior distribution over sequence representations induced by the prior. By integrating out the posterior, our method compares favorably to leading binding predictors.
Nonparametric tests for censored data
Bagdonavicus, Vilijandas; Nikulin, Mikhail
2013-01-01
This book concerns testing hypotheses in non-parametric models. Generalizations of many non-parametric tests to the case of censored and truncated data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. The incorrect use of many tests applying most statistical software is highlighted and discussed.
Joanna Endter-Wada; Dale J. Blahna
2011-01-01
This article presents the " Linkages to Public Land" (LPL) Framework, a general but comprehensive data-gathering and analysis approach aimed at informing citizen and agency decision making about the social environment of public land. This social assessment and planning approach identifies and categorizes various types of linkages that people have to public...
Nonparametric identification of copula structures
Li, Bo; Genton, Marc G.
2013-01-01
We propose a unified framework for testing a variety of assumptions commonly made about the structure of copulas, including symmetry, radial symmetry, joint symmetry, associativity and Archimedeanity, and max-stability. Our test is nonparametric
Evidence for an asthma risk locus on chromosome Xp: a replication linkage study
DEFF Research Database (Denmark)
Brasch-Andersen, C; Møller, M U; Haagerup, A
2008-01-01
replication sample as used in the present study. The aim of the study was to replicate linkage to candidate regions for asthma in an independent Danish sample. METHODS: We performed a replication study investigating linkage to candidate regions for asthma on chromosomes 1p36.31-p36.21, 5q15-q23.2, 6p24.3-p22...... studies have been carried out the results are still conflicting and call for replication experiments. A Danish genome-wide scan has prior reported evidence for candidate regions for asthma susceptibility genes on chromosomes 1p, 5q, 6p, 12q and Xp. Linkage to chromosome 12q was later confirmed in the same.......3, and Xp22.31-p11.4 using additional markers in an independent set of 136 Danish asthmatic sib pair families. RESULTS: Nonparametric multipoint linkage analyses yielded suggestive evidence for linkage to asthma to chromosome Xp21.2 (MLS 2.92) but failed to replicate linkage to chromosomes 1p36.31-p36.21, 5...
A nonparametric mixture model for cure rate estimation.
Peng, Y; Dear, K B
2000-03-01
Nonparametric methods have attracted less attention than their parametric counterparts for cure rate analysis. In this paper, we study a general nonparametric mixture model. The proportional hazards assumption is employed in modeling the effect of covariates on the failure time of patients who are not cured. The EM algorithm, the marginal likelihood approach, and multiple imputations are employed to estimate parameters of interest in the model. This model extends models and improves estimation methods proposed by other researchers. It also extends Cox's proportional hazards regression model by allowing a proportion of event-free patients and investigating covariate effects on that proportion. The model and its estimation method are investigated by simulations. An application to breast cancer data, including comparisons with previous analyses using a parametric model and an existing nonparametric model by other researchers, confirms the conclusions from the parametric model but not those from the existing nonparametric model.
Decision support using nonparametric statistics
Beatty, Warren
2018-01-01
This concise volume covers nonparametric statistics topics that most are most likely to be seen and used from a practical decision support perspective. While many degree programs require a course in parametric statistics, these methods are often inadequate for real-world decision making in business environments. Much of the data collected today by business executives (for example, customer satisfaction opinions) requires nonparametric statistics for valid analysis, and this book provides the reader with a set of tools that can be used to validly analyze all data, regardless of type. Through numerous examples and exercises, this book explains why nonparametric statistics will lead to better decisions and how they are used to reach a decision, with a wide array of business applications. Online resources include exercise data, spreadsheets, and solutions.
Comparing parametric and nonparametric regression methods for panel data
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
We investigate and compare the suitability of parametric and non-parametric stochastic regression methods for analysing production technologies and the optimal firm size. Our theoretical analysis shows that the most commonly used functional forms in empirical production analysis, Cobb......-Douglas and Translog, are unsuitable for analysing the optimal firm size. We show that the Translog functional form implies an implausible linear relationship between the (logarithmic) firm size and the elasticity of scale, where the slope is artificially related to the substitutability between the inputs....... The practical applicability of the parametric and non-parametric regression methods is scrutinised and compared by an empirical example: we analyse the production technology and investigate the optimal size of Polish crop farms based on a firm-level balanced panel data set. A nonparametric specification test...
Testing discontinuities in nonparametric regression
Dai, Wenlin
2017-01-19
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Testing discontinuities in nonparametric regression
Dai, Wenlin; Zhou, Yuejin; Tong, Tiejun
2017-01-01
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Nonparametric factor analysis of time series
Rodríguez-Poo, Juan M.; Linton, Oliver Bruce
1998-01-01
We introduce a nonparametric smoothing procedure for nonparametric factor analaysis of multivariate time series. The asymptotic properties of the proposed procedures are derived. We present an application based on the residuals from the Fair macromodel.
Genome scan for linkage to asthma using a linkage disequilibrium-lod score test.
Jiang, Y; Slager, S L; Huang, J
2001-01-01
We report a genome-wide linkage study of asthma on the German and Collaborative Study on the Genetics of Asthma (CSGA) data. Using a combined linkage and linkage disequilibrium test and the nonparametric linkage score, we identified 13 markers from the German data, 1 marker from the African American (CSGA) data, and 7 markers from the Caucasian (CSGA) data in which the p-values ranged between 0.0001 and 0.0100. From our analysis and taking into account previous published linkage studies of asthma, we suggest that three regions in chromosome 5 (around D5S418, D5S644, and D5S422), one region in chromosome 6 (around three neighboring markers D6S1281, D6S291, and D6S1019), one region in chromosome 11 (around D11S2362), and two regions in chromosome 12 (around D12S351 and D12S324) especially merit further investigation.
Nonparametric Inference for Periodic Sequences
Sun, Ying
2012-02-01
This article proposes a nonparametric method for estimating the period and values of a periodic sequence when the data are evenly spaced in time. The period is estimated by a "leave-out-one-cycle" version of cross-validation (CV) and complements the periodogram, a widely used tool for period estimation. The CV method is computationally simple and implicitly penalizes multiples of the smallest period, leading to a "virtually" consistent estimator of integer periods. This estimator is investigated both theoretically and by simulation.We also propose a nonparametric test of the null hypothesis that the data have constantmean against the alternative that the sequence of means is periodic. Finally, our methodology is demonstrated on three well-known time series: the sunspots and lynx trapping data, and the El Niño series of sea surface temperatures. © 2012 American Statistical Association and the American Society for Quality.
Celedón, Juan C; Soto-Quiros, Manuel E; Avila, Lydiana; Lake, Stephen L; Liang, Catherine; Fournier, Eduardo; Spesny, Mitzi; Hersh, Craig P; Sylvia, Jody S; Hudson, Thomas J; Verner, Andrei; Klanderman, Barbara J; Freimer, Nelson B; Silverman, Edwin K; Weiss, Scott T
2007-01-01
Although asthma is a major public health problem in certain Hispanic subgroups in the United States and Latin America, only one genome scan for asthma has included Hispanic individuals. Because of small sample size, that study had limited statistical power to detect linkage to asthma and its intermediate phenotypes in Hispanic participants. To identify genomic regions that contain susceptibility genes for asthma and airway responsiveness in an isolated Hispanic population living in the Central Valley of Costa Rica, we conducted a genome-wide linkage analysis of asthma (n = 638) and airway responsiveness (n = 488) in members of eight large pedigrees of Costa Rican children with asthma. Nonparametric multipoint linkage analysis of asthma was conducted by the NPL-PAIR allele-sharing statistic, and variance component models were used for the multipoint linkage analysis of airway responsiveness as a quantitative phenotype. All linkage analyses were repeated after exclusion of the phenotypic data of former and current smokers. Chromosome 12q showed some evidence of linkage to asthma, particularly in nonsmokers (P asthma (airway responsiveness) in Costa Ricans.
Nonparametric predictive inference in reliability
International Nuclear Information System (INIS)
Coolen, F.P.A.; Coolen-Schrijner, P.; Yan, K.J.
2002-01-01
We introduce a recently developed statistical approach, called nonparametric predictive inference (NPI), to reliability. Bounds for the survival function for a future observation are presented. We illustrate how NPI can deal with right-censored data, and discuss aspects of competing risks. We present possible applications of NPI for Bernoulli data, and we briefly outline applications of NPI for replacement decisions. The emphasis is on introduction and illustration of NPI in reliability contexts, detailed mathematical justifications are presented elsewhere
Nonparametric regression using the concept of minimum energy
International Nuclear Information System (INIS)
Williams, Mike
2011-01-01
It has recently been shown that an unbinned distance-based statistic, the energy, can be used to construct an extremely powerful nonparametric multivariate two sample goodness-of-fit test. An extension to this method that makes it possible to perform nonparametric regression using multiple multivariate data sets is presented in this paper. The technique, which is based on the concept of minimizing the energy of the system, permits determination of parameters of interest without the need for parametric expressions of the parent distributions of the data sets. The application and performance of this new method is discussed in the context of some simple example analyses.
Genome scan for linkage to Gilles de la Tourette syndrome
Energy Technology Data Exchange (ETDEWEB)
Barr, C.L.; Livingston, J.; Williamson, R. [and others
1994-09-01
Gilles de la Tourette Syndrome (TS) is a familial, neuropsychiatric disorder characterized by chronic, intermittent motor and vocal tics. In addition to tics, affected individuals frequently display symptoms such as attention-deficit hyperactivity disorder and/or obsessive compulsive disorder. Genetic analyses of family data have suggested that susceptibility to the disorder is most likely due to a single genetic locus with a dominant mode of transmission and reduced penetrance. In the search for genetic linkage for TS, we have collected well-characterized pedigrees with multiple affected individuals on whom extensive diagnostic evaluations have been done. The first stage of our study is to scan the genome systematically using a panel of uniformly spaced (10 to 20 cM), highly polymorphic, microsatellite markers on 5 families segregating TS. To date, 290 markers have been typed and 3,660 non-overlapping cM of the genome have been excluded for possible linkage under the assumption of genetic homogeneity. Because of the possibility of locus heterogeneity overall summed exclusion is not considered tantamount to absolute exclusion of a disease locus in that region. The results from each family are carefully evaluated and a positive lod score in a single family is followed up by typing closely linked markers. Linkage to TS was examined by two-point analysis using the following genetic model: single autosomal dominant gene with gene frequency .003 and maximum penetrance of .99. An age-of-onset correction is included using a linear function increasing from age 2 years to 21 years. A small rate of phenocopies is also incorporated into the model. Only individuals with TS or CMT according to DSM III-R criteria were regarded as affected for the purposes of this summary. Additional markers are being tested to provide coverage at 5 cM intervals. Moreover, we are currently analyzing the data non-parametrically using the Affected-Pedigree-Member Method of linkage analysis.
Nonparametric identification of copula structures
Li, Bo
2013-06-01
We propose a unified framework for testing a variety of assumptions commonly made about the structure of copulas, including symmetry, radial symmetry, joint symmetry, associativity and Archimedeanity, and max-stability. Our test is nonparametric and based on the asymptotic distribution of the empirical copula process.We perform simulation experiments to evaluate our test and conclude that our method is reliable and powerful for assessing common assumptions on the structure of copulas, particularly when the sample size is moderately large. We illustrate our testing approach on two datasets. © 2013 American Statistical Association.
Nonparametric Mixture of Regression Models.
Huang, Mian; Li, Runze; Wang, Shaoli
2013-07-01
Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.
The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard
and nonparametric estimations of production functions in order to evaluate the optimal firm size. The second paper discusses the use of parametric and nonparametric regression methods to estimate panel data regression models. The third paper analyses production risk, price uncertainty, and farmers' risk preferences...... within a nonparametric panel data regression framework. The fourth paper analyses the technical efficiency of dairy farms with environmental output using nonparametric kernel regression in a semiparametric stochastic frontier analysis. The results provided in this PhD thesis show that nonparametric......This PhD thesis addresses one of the fundamental problems in applied econometric analysis, namely the econometric estimation of regression functions. The conventional approach to regression analysis is the parametric approach, which requires the researcher to specify the form of the regression...
Nonparametric correlation models for portfolio allocation
DEFF Research Database (Denmark)
Aslanidis, Nektarios; Casas, Isabel
2013-01-01
This article proposes time-varying nonparametric and semiparametric estimators of the conditional cross-correlation matrix in the context of portfolio allocation. Simulations results show that the nonparametric and semiparametric models are best in DGPs with substantial variability or structural ...... currencies. Results show the nonparametric model generally dominates the others when evaluating in-sample. However, the semiparametric model is best for out-of-sample analysis....
Nonparametric statistics for social and behavioral sciences
Kraska-MIller, M
2013-01-01
Introduction to Research in Social and Behavioral SciencesBasic Principles of ResearchPlanning for ResearchTypes of Research Designs Sampling ProceduresValidity and Reliability of Measurement InstrumentsSteps of the Research Process Introduction to Nonparametric StatisticsData AnalysisOverview of Nonparametric Statistics and Parametric Statistics Overview of Parametric Statistics Overview of Nonparametric StatisticsImportance of Nonparametric MethodsMeasurement InstrumentsAnalysis of Data to Determine Association and Agreement Pearson Chi-Square Test of Association and IndependenceContingency
Directory of Open Access Journals (Sweden)
Kristy R Crooks
Full Text Available Primary open-angle glaucoma (POAG is the most common form of glaucoma and one of the leading causes of vision loss worldwide. The genetic etiology of POAG is complex and poorly understood. The purpose of this work is to identify genomic regions of interest linked to POAG. This study is the largest genetic linkage study of POAG performed to date: genomic DNA samples from 786 subjects (538 Caucasian ancestry, 248 African ancestry were genotyped using either the Illumina GoldenGate Linkage 4 Panel or the Illumina Infinium Human Linkage-12 Panel. A total of 5233 SNPs was analyzed in 134 multiplex POAG families (89 Caucasian ancestry, 45 African ancestry. Parametric and non-parametric linkage analyses were performed on the overall dataset and within race-specific datasets (Caucasian ancestry and African ancestry. Ordered subset analysis was used to stratify the data on the basis of age of glaucoma diagnosis. Novel linkage regions were identified on chromosomes 1 and 20, and two previously described loci-GLC1D on chromosome 8 and GLC1I on chromosome 15--were replicated. These data will prove valuable in the context of interpreting results from genome-wide association studies for POAG.
Nonparametric Bayesian inference in biostatistics
Müller, Peter
2015-01-01
As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters c...
Nonparametric e-Mixture Estimation.
Takano, Ken; Hino, Hideitsu; Akaho, Shotaro; Murata, Noboru
2016-12-01
This study considers the common situation in data analysis when there are few observations of the distribution of interest or the target distribution, while abundant observations are available from auxiliary distributions. In this situation, it is natural to compensate for the lack of data from the target distribution by using data sets from these auxiliary distributions-in other words, approximating the target distribution in a subspace spanned by a set of auxiliary distributions. Mixture modeling is one of the simplest ways to integrate information from the target and auxiliary distributions in order to express the target distribution as accurately as possible. There are two typical mixtures in the context of information geometry: the [Formula: see text]- and [Formula: see text]-mixtures. The [Formula: see text]-mixture is applied in a variety of research fields because of the presence of the well-known expectation-maximazation algorithm for parameter estimation, whereas the [Formula: see text]-mixture is rarely used because of its difficulty of estimation, particularly for nonparametric models. The [Formula: see text]-mixture, however, is a well-tempered distribution that satisfies the principle of maximum entropy. To model a target distribution with scarce observations accurately, this letter proposes a novel framework for a nonparametric modeling of the [Formula: see text]-mixture and a geometrically inspired estimation algorithm. As numerical examples of the proposed framework, a transfer learning setup is considered. The experimental results show that this framework works well for three types of synthetic data sets, as well as an EEG real-world data set.
Heritability and whole genome linkage of pulse pressure in Chinese twin pairs
DEFF Research Database (Denmark)
Jiang, Wengjie; Zhang, Dongfeng; Pang, Zengchang
2012-01-01
with a heritability estimate of 0.45. Genome-wide non-parametric linkage analysis identified three significant linkage peaks on chromosome 11 (lod score 4.06 at 30.5 cM), chromosome 12 (lod score 3.97 at 100.7 cM), and chromosome 18 (lod score 4.01 at 70.7 cM) with the last two peaks closely overlapping with linkage...
International Conference on Robust Rank-Based and Nonparametric Methods
McKean, Joseph
2016-01-01
The contributors to this volume include many of the distinguished researchers in this area. Many of these scholars have collaborated with Joseph McKean to develop underlying theory for these methods, obtain small sample corrections, and develop efficient algorithms for their computation. The papers cover the scope of the area, including robust nonparametric rank-based procedures through Bayesian and big data rank-based analyses. Areas of application include biostatistics and spatial areas. Over the last 30 years, robust rank-based and nonparametric methods have developed considerably. These procedures generalize traditional Wilcoxon-type methods for one- and two-sample location problems. Research into these procedures has culminated in complete analyses for many of the models used in practice including linear, generalized linear, mixed, and nonlinear models. Settings are both multivariate and univariate. With the development of R packages in these areas, computation of these procedures is easily shared with r...
Bayesian Nonparametric Longitudinal Data Analysis.
Quintana, Fernando A; Johnson, Wesley O; Waetjen, Elaine; Gold, Ellen
2016-01-01
Practical Bayesian nonparametric methods have been developed across a wide variety of contexts. Here, we develop a novel statistical model that generalizes standard mixed models for longitudinal data that include flexible mean functions as well as combined compound symmetry (CS) and autoregressive (AR) covariance structures. AR structure is often specified through the use of a Gaussian process (GP) with covariance functions that allow longitudinal data to be more correlated if they are observed closer in time than if they are observed farther apart. We allow for AR structure by considering a broader class of models that incorporates a Dirichlet Process Mixture (DPM) over the covariance parameters of the GP. We are able to take advantage of modern Bayesian statistical methods in making full predictive inferences and about characteristics of longitudinal profiles and their differences across covariate combinations. We also take advantage of the generality of our model, which provides for estimation of a variety of covariance structures. We observe that models that fail to incorporate CS or AR structure can result in very poor estimation of a covariance or correlation matrix. In our illustration using hormone data observed on women through the menopausal transition, biology dictates the use of a generalized family of sigmoid functions as a model for time trends across subpopulation categories.
Nonparametric Bayesian Modeling of Complex Networks
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Mørup, Morten
2013-01-01
an infinite mixture model as running example, we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model?s fit and predictive performance. We explain how advanced nonparametric models......Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...
Metzger, Julia; Ohnesorge, Bernhard; Distl, Ottmar
2012-01-01
Equine guttural pouch tympany (GPT) is a hereditary condition affecting foals in their first months of life. Complex segregation analyses in Arabian and German warmblood horses showed the involvement of a major gene as very likely. Genome-wide linkage and association analyses including a high density marker set of single nucleotide polymorphisms (SNPs) were performed to map the genomic region harbouring the potential major gene for GPT. A total of 85 Arabian and 373 German warmblood horses were genotyped on the Illumina equine SNP50 beadchip. Non-parametric multipoint linkage analyses showed genome-wide significance on horse chromosomes (ECA) 3 for German warmblood at 16–26 Mb and 34–55 Mb and for Arabian on ECA15 at 64–65 Mb. Genome-wide association analyses confirmed the linked regions for both breeds. In Arabian, genome-wide association was detected at 64 Mb within the region with the highest linkage peak on ECA15. For German warmblood, signals for genome-wide association were close to the peak region of linkage at 52 Mb on ECA3. The odds ratio for the SNP with the highest genome-wide association was 0.12 for the Arabian. In conclusion, the refinement of the regions with the Illumina equine SNP50 beadchip is an important step to unravel the responsible mutations for GPT. PMID:22848553
Zeng, Li-ping; Hu, Zheng-mao; Mu, Li-li; Mei, Gui-sen; Lu, Xiu-ling; Zheng, Yong-jun; Li, Pei-jian; Zhang, Ying-xue; Pan, Qian; Long, Zhi-gao; Dai, He-ping; Zhang, Zhuo-hua; Xia, Jia-hui; Zhao, Jing-ping; Xia, Kun
2011-06-01
To investigate the relationship of susceptibility loci in chromosomes 1q21-25 and 6p21-25 and schizophrenia subtypes in Chinese population. A genomic scan and parametric and non-parametric analyses were performed on 242 individuals from 36 schizophrenia pedigrees, including 19 paranoid schizophrenia and 17 undifferentiated schizophrenia pedigrees, from Henan province of China using 5 microsatellite markers in the chromosome region 1q21-25 and 8 microsatellite markers in the chromosome region 6p21-25, which were the candidates of previous studies. All affected subjects were diagnosed and typed according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revised (DSM-IV-TR; American Psychiatric Association, 2000). All subjects signed informed consent. In chromosome 1, parametric analysis under the dominant inheritance mode of all 36 pedigrees showed that the maximum multi-point heterogeneity Log of odds score method (HLOD) score was 1.33 (α = 0.38). The non-parametric analysis and the single point and multi-point nonparametric linkage (NPL) scores suggested linkage at D1S484, D1S2878, and D1S196. In the 19 paranoid schizophrenias pedigrees, linkage was not observed for any of the 5 markers. In the 17 undifferentiated schizophrenia pedigrees, the multi-point NPL score was 1.60 (P= 0.0367) at D1S484. The single point NPL score was 1.95(P= 0.0145) and the multi-point NPL score was 2.39 (P= 0.0041) at D1S2878. Additionally, the multi-point NPL score was 1.74 (P= 0.0255) at D1S196. These same three loci showed suggestive linkage during the integrative analysis of all 36 pedigrees. In chromosome 6, parametric linkage analysis under the dominant and recessive inheritance and the non-parametric linkage analysis of all 36 pedigrees and the 17 undifferentiated schizophrenia pedigrees, linkage was not observed for any of the 8 markers. In the 19 paranoid schizophrenias pedigrees, parametric analysis showed that under recessive
Nonparametric tests for equality of psychometric functions.
García-Pérez, Miguel A; Núñez-Antón, Vicente
2017-12-07
Many empirical studies measure psychometric functions (curves describing how observers' performance varies with stimulus magnitude) because these functions capture the effects of experimental conditions. To assess these effects, parametric curves are often fitted to the data and comparisons are carried out by testing for equality of mean parameter estimates across conditions. This approach is parametric and, thus, vulnerable to violations of the implied assumptions. Furthermore, testing for equality of means of parameters may be misleading: Psychometric functions may vary meaningfully across conditions on an observer-by-observer basis with no effect on the mean values of the estimated parameters. Alternative approaches to assess equality of psychometric functions per se are thus needed. This paper compares three nonparametric tests that are applicable in all situations of interest: The existing generalized Mantel-Haenszel test, a generalization of the Berry-Mielke test that was developed here, and a split variant of the generalized Mantel-Haenszel test also developed here. Their statistical properties (accuracy and power) are studied via simulation and the results show that all tests are indistinguishable as to accuracy but they differ non-uniformly as to power. Empirical use of the tests is illustrated via analyses of published data sets and practical recommendations are given. The computer code in MATLAB and R to conduct these tests is available as Electronic Supplemental Material.
Single versus mixture Weibull distributions for nonparametric satellite reliability
International Nuclear Information System (INIS)
Castet, Jean-Francois; Saleh, Joseph H.
2010-01-01
Long recognized as a critical design attribute for space systems, satellite reliability has not yet received the proper attention as limited on-orbit failure data and statistical analyses can be found in the technical literature. To fill this gap, we recently conducted a nonparametric analysis of satellite reliability for 1584 Earth-orbiting satellites launched between January 1990 and October 2008. In this paper, we provide an advanced parametric fit, based on mixture of Weibull distributions, and compare it with the single Weibull distribution model obtained with the Maximum Likelihood Estimation (MLE) method. We demonstrate that both parametric fits are good approximations of the nonparametric satellite reliability, but that the mixture Weibull distribution provides significant accuracy in capturing all the failure trends in the failure data, as evidenced by the analysis of the residuals and their quasi-normal dispersion.
Nonparametric functional mapping of quantitative trait loci.
Yang, Jie; Wu, Rongling; Casella, George
2009-03-01
Functional mapping is a useful tool for mapping quantitative trait loci (QTL) that control dynamic traits. It incorporates mathematical aspects of biological processes into the mixture model-based likelihood setting for QTL mapping, thus increasing the power of QTL detection and the precision of parameter estimation. However, in many situations there is no obvious functional form and, in such cases, this strategy will not be optimal. Here we propose to use nonparametric function estimation, typically implemented with B-splines, to estimate the underlying functional form of phenotypic trajectories, and then construct a nonparametric test to find evidence of existing QTL. Using the representation of a nonparametric regression as a mixed model, the final test statistic is a likelihood ratio test. We consider two types of genetic maps: dense maps and general maps, and the power of nonparametric functional mapping is investigated through simulation studies and demonstrated by examples.
Essays on nonparametric econometrics of stochastic volatility
Zu, Y.
2012-01-01
Volatility is a concept that describes the variation of financial returns. Measuring and modelling volatility dynamics is an important aspect of financial econometrics. This thesis is concerned with nonparametric approaches to volatility measurement and volatility model validation.
Nonparametric methods for volatility density estimation
Es, van Bert; Spreij, P.J.C.; Zanten, van J.H.
2009-01-01
Stochastic volatility modelling of financial processes has become increasingly popular. The proposed models usually contain a stationary volatility process. We will motivate and review several nonparametric methods for estimation of the density of the volatility process. Both models based on
Leu, Costin; de Kovel, Carolien G F; Zara, Federico; Striano, Pasquale; Pezzella, Marianna; Robbiano, Angela; Bianchi, Amedeo; Bisulli, Francesca; Coppola, Antonietta; Giallonardo, Anna Teresa; Beccaria, Francesca; Trenité, Dorothée Kasteleijn-Nolst; Lindhout, Dick; Gaus, Verena; Schmitz, Bettina; Janz, Dieter; Weber, Yvonne G; Becker, Felicitas; Lerche, Holger; Kleefuss-Lie, Ailing A; Hallman, Kerstin; Kunz, Wolfram S; Elger, Christian E; Muhle, Hiltrud; Stephani, Ulrich; Møller, Rikke S; Hjalgrim, Helle; Mullen, Saul; Scheffer, Ingrid E; Berkovic, Samuel F; Everett, Kate V; Gardiner, Mark R; Marini, Carla; Guerrini, Renzo; Lehesjoki, Anna-Elina; Siren, Auli; Nabbout, Rima; Baulac, Stephanie; Leguern, Eric; Serratosa, Jose M; Rosenow, Felix; Feucht, Martha; Unterberger, Iris; Covanis, Athanasios; Suls, Arvid; Weckhuysen, Sarah; Kaneva, Radka; Caglayan, Hande; Turkdogan, Dilsad; Baykan, Betul; Bebek, Nerses; Ozbek, Ugur; Hempelmann, Anne; Schulz, Herbert; Rüschendorf, Franz; Trucks, Holger; Nürnberg, Peter; Avanzini, Giuliano; Koeleman, Bobby P C; Sander, Thomas
2012-02-01
Genetic generalized epilepsies (GGEs) have a lifetime prevalence of 0.3% with heritability estimates of 80%. A considerable proportion of families with siblings affected by GGEs presumably display an oligogenic inheritance. The present genome-wide linkage meta-analysis aimed to map: (1) susceptibility loci shared by a broad spectrum of GGEs, and (2) seizure type-related genetic factors preferentially predisposing to either typical absence or myoclonic seizures, respectively. Meta-analysis of three genome-wide linkage datasets was carried out in 379 GGE-multiplex families of European ancestry including 982 relatives with GGEs. To dissect out seizure type-related susceptibility genes, two family subgroups were stratified comprising 235 families with predominantly genetic absence epilepsies (GAEs) and 118 families with an aggregation of juvenile myoclonic epilepsy (JME). To map shared and seizure type-related susceptibility loci, both nonparametric loci (NPL) and parametric linkage analyses were performed for a broad trait model (GGEs) in the entire set of GGE-multiplex families and a narrow trait model (typical absence or myoclonic seizures) in the subgroups of JME and GAE families. For the entire set of 379 GGE-multiplex families, linkage analysis revealed six loci achieving suggestive evidence for linkage at 1p36.22, 3p14.2, 5q34, 13q12.12, 13q31.3, and 19q13.42. The linkage finding at 5q34 was consistently supported by both NPL and parametric linkage results across all three family groups. A genome-wide significant nonparametric logarithm of odds score of 3.43 was obtained at 2q34 in 118 JME families. Significant parametric linkage to 13q31.3 was found in 235 GAE families assuming recessive inheritance (heterogeneity logarithm of odds = 5.02). Our linkage results support an oligogenic predisposition of familial GGE syndromes. The genetic risk factor at 5q34 confers risk to a broad spectrum of familial GGE syndromes, whereas susceptibility loci at 2q34 and 13q31
Vermont Center for Geographic Information — (Link to Metadata) The Wildlife Linkage Habitat Analysis uses landscape scale data to identify or predict the location of potentially significant wildlife linkage...
Challenges in administrative data linkage for research
Directory of Open Access Journals (Sweden)
Katie Harron
2017-12-01
Full Text Available Linkage of population-based administrative data is a valuable tool for combining detailed individual-level information from different sources for research. While not a substitute for classical studies based on primary data collection, analyses of linked administrative data can answer questions that require large sample sizes or detailed data on hard-to-reach populations, and generate evidence with a high level of external validity and applicability for policy making. There are unique challenges in the appropriate research use of linked administrative data, for example with respect to bias from linkage errors where records cannot be linked or are linked together incorrectly. For confidentiality and other reasons, the separation of data linkage processes and analysis of linked data is generally regarded as best practice. However, the ‘black box’ of data linkage can make it difficult for researchers to judge the reliability of the resulting linked data for their required purposes. This article aims to provide an overview of challenges in linking administrative data for research. We aim to increase understanding of the implications of (i the data linkage environment and privacy preservation; (ii the linkage process itself (including data preparation, and deterministic and probabilistic linkage methods and (iii linkage quality and potential bias in linked data. We draw on examples from a number of countries to illustrate a range of approaches for data linkage in different contexts.
DEFF Research Database (Denmark)
Andersson, Ulf; Perri, Alessandra; Nell, Phillip C.
2012-01-01
channels for spillovers to competitors. We find a curvilinear relationship between the extent of competitive pressure and the quality of a subsidiary's set of local linkages. Furthermore, the extent to which a subsidiary possesses capabilities moderates this relationship: Very capable subsidiaries...... in strongly competitive environments tend to shy away from high quality linkages. We discuss our findings in light of the literature on spillovers and inter-organizational linkages.......This paper investigates the pattern of subsidiaries' local vertical linkages under varying levels of competition and subsidiary capabilities. Contrary to most previous literature, we explicitly account for the double role of such linkages as conduits of learning prospects as well as potential...
McMaster, Mary L.; Goldin, Lynn R.; Bai, Yan; Ter-Minassian, Monica; Boehringer, Stefan; Giambarresi, Therese R.; Vasquez, Linda G.; Tucker, Margaret A.
2006-01-01
Waldenström macroglobulinemia (WM), a distinctive subtype of non-Hodgkin lymphoma that features overproduction of immunoglobulin M (IgM), clearly has a familial component; however, no susceptibility genes have yet been identified. We performed a genomewide linkage analysis in 11 high-risk families with WM that were informative for linkage, for a total of 122 individuals with DNA samples, including 34 patients with WM and 10 patients with IgM monoclonal gammopathy of undetermined significance (IgM MGUS). We genotyped 1,058 microsatellite markers (average spacing 3.5 cM), performed both nonparametric and parametric linkage analysis, and computed both two-point and multipoint linkage statistics. The strongest evidence of linkage was found on chromosomes 1q and 4q when patients with WM and with IgM MGUS were both considered affected; nonparametric linkage scores were 2.5 (P=.0089) and 3.1 (P=.004), respectively. Other locations suggestive of linkage were found on chromosomes 3 and 6. Results of two-locus linkage analysis were consistent with independent effects. The findings from this first linkage analysis of families at high risk for WM represent important progress toward identifying gene(s) that modulate susceptibility to WM and toward understanding its complex etiology. PMID:16960805
Recent Advances and Trends in Nonparametric Statistics
Akritas, MG
2003-01-01
The advent of high-speed, affordable computers in the last two decades has given a new boost to the nonparametric way of thinking. Classical nonparametric procedures, such as function smoothing, suddenly lost their abstract flavour as they became practically implementable. In addition, many previously unthinkable possibilities became mainstream; prime examples include the bootstrap and resampling methods, wavelets and nonlinear smoothers, graphical methods, data mining, bioinformatics, as well as the more recent algorithmic approaches such as bagging and boosting. This volume is a collection o
Teaching Nonparametric Statistics Using Student Instrumental Values.
Anderson, Jonathan W.; Diddams, Margaret
Nonparametric statistics are often difficult to teach in introduction to statistics courses because of the lack of real-world examples. This study demonstrated how teachers can use differences in the rankings and ratings of undergraduate and graduate values to discuss: (1) ipsative and normative scaling; (2) uses of the Mann-Whitney U-test; and…
Nonparametric conditional predictive regions for time series
de Gooijer, J.G.; Zerom Godefay, D.
2000-01-01
Several nonparametric predictors based on the Nadaraya-Watson kernel regression estimator have been proposed in the literature. They include the conditional mean, the conditional median, and the conditional mode. In this paper, we consider three types of predictive regions for these predictors — the
Nonparametric predictive inference in statistical process control
Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.
2000-01-01
New methods for statistical process control are presented, where the inferences have a nonparametric predictive nature. We consider several problems in process control in terms of uncertainties about future observable random quantities, and we develop inferences for these random quantities hased on
Nonparametric predictive inference in statistical process control
Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.
2004-01-01
Statistical process control (SPC) is used to decide when to stop a process as confidence in the quality of the next item(s) is low. Information to specify a parametric model is not always available, and as SPC is of a predictive nature, we present a control chart developed using nonparametric
Non-Parametric Estimation of Correlation Functions
DEFF Research Database (Denmark)
Brincker, Rune; Rytter, Anders; Krenk, Steen
In this paper three methods of non-parametric correlation function estimation are reviewed and evaluated: the direct method, estimation by the Fast Fourier Transform and finally estimation by the Random Decrement technique. The basic ideas of the techniques are reviewed, sources of bias are point...
Nonparametric estimation in models for unobservable heterogeneity
Hohmann, Daniel
2014-01-01
Nonparametric models which allow for data with unobservable heterogeneity are studied. The first publication introduces new estimators and their asymptotic properties for conditional mixture models. The second publication considers estimation of a function from noisy observations of its Radon transform in a Gaussian white noise model.
Nonparametric estimation of location and scale parameters
Potgieter, C.J.; Lombard, F.
2012-01-01
Two random variables X and Y belong to the same location-scale family if there are constants μ and σ such that Y and μ+σX have the same distribution. In this paper we consider non-parametric estimation of the parameters μ and σ under minimal
A Bayesian Nonparametric Approach to Factor Analysis
DEFF Research Database (Denmark)
Piatek, Rémi; Papaspiliopoulos, Omiros
2018-01-01
This paper introduces a new approach for the inference of non-Gaussian factor models based on Bayesian nonparametric methods. It relaxes the usual normality assumption on the latent factors, widely used in practice, which is too restrictive in many settings. Our approach, on the contrary, does no...
Sayers, Adrian; Ben-Shlomo, Yoav; Blom, Ashley W; Steele, Fiona
2016-06-01
Studies involving the use of probabilistic record linkage are becoming increasingly common. However, the methods underpinning probabilistic record linkage are not widely taught or understood, and therefore these studies can appear to be a 'black box' research tool. In this article, we aim to describe the process of probabilistic record linkage through a simple exemplar. We first introduce the concept of deterministic linkage and contrast this with probabilistic linkage. We illustrate each step of the process using a simple exemplar and describe the data structure required to perform a probabilistic linkage. We describe the process of calculating and interpreting matched weights and how to convert matched weights into posterior probabilities of a match using Bayes theorem. We conclude this article with a brief discussion of some of the computational demands of record linkage, how you might assess the quality of your linkage algorithm, and how epidemiologists can maximize the value of their record-linked research using robust record linkage methods. © The Author 2015; Published by Oxford University Press on behalf of the International Epidemiological Association.
Moser, Kathy L.; Neas, Barbara R.; Salmon, Jane E.; Yu, Hua; Gray-McGuire, Courtney; Asundi, Neeraj; Bruner, Gail R.; Fox, Jerome; Kelly, Jennifer; Henshall, Stephanie; Bacino, Debra; Dietz, Myron; Hogue, Robert; Koelsch, Gerald; Nightingale, Lydia; Shaver, Tim; Abdou, Nabih I.; Albert, Daniel A.; Carson, Craig; Petri, Michelle; Treadwell, Edward L.; James, Judith A.; Harley, John B.
1998-01-01
Systemic lupus erythematosus (SLE) is an autoimmune disorder characterized by production of autoantibodies against intracellular antigens including DNA, ribosomal P, Ro (SS-A), La (SS-B), and the spliceosome. Etiology is suspected to involve genetic and environmental factors. Evidence of genetic involvement includes: associations with HLA-DR3, HLA-DR2, Fcγ receptors (FcγR) IIA and IIIA, and hereditary complement component deficiencies, as well as familial aggregation, monozygotic twin concordance >20%, λs > 10, purported linkage at 1q41–42, and inbred mouse strains that consistently develop lupus. We have completed a genome scan in 94 extended multiplex pedigrees by using model-based linkage analysis. Potential [log10 of the odds for linkage (lod) > 2.0] SLE loci have been identified at chromosomes 1q41, 1q23, and 11q14–23 in African-Americans; 14q11, 4p15, 11q25, 2q32, 19q13, 6q26–27, and 12p12–11 in European-Americans; and 1q23, 13q32, 20q13, and 1q31 in all pedigrees combined. An effect for the FcγRIIA candidate polymorphism) at 1q23 (lod = 3.37 in African-Americans) is syntenic with linkage in a murine model of lupus. Sib-pair and multipoint nonparametric analyses also support linkage (P 2.0). Our results are consistent with the presumed complexity of genetic susceptibility to SLE and illustrate racial origin is likely to influence the specific nature of these genetic effects. PMID:9843982
Meta-analysis of genome-wide linkage studies in BMI and obesity
Saunders, Catherine L.; Chiodini, Benedetta D.; Sham, Pak; Lewis, Cathryn M.; Abkevich, Victor; Adeyemo, Adebowale A.; de Andrade, Mariza; Arya, Rector; Berenson, Gerald S.; Blangero, John; Boehnke, Michael; Borecki, Ingrid B.; Chagnon, Yvon C.; Chen, Wei; Comuzzie, Anthony G.; Deng, Hong-Wen; Duggirala, Ravindranath; Feitosa, Mary F.; Froguel, Philippe; Hanson, Robert L.; Hebebrand, Johannes; Huezo-Dias, Patricia; Kissebah, Ahmed H.; Li, Weidong; Luke, Amy; Martin, Lisa J.; Nash, Matthew; Ohman, Muena; Palmer, Lyle J.; Peltonen, Leena; Perola, Markus; Price, R. Arlen; Redline, Susan; Srinivasan, Sathanur R.; Stern, Michael P.; Stone, Steven; Stringham, Heather; Turner, Stephen; Wijmenga, Cisca; Collier, David A.
Objective: The objective was to provide an overall assessment of genetic linkage data of BMI and BMI-defined obesity using a nonparametric genome scan meta-analysis. Research Methods and Procedures: We identified 37 published studies containing data on over 31,000 individuals from more than >10,000
Curtis, David; Knight, Jo; Sham, Pak C
2005-09-01
Although LOD score methods have been applied to diseases with complex modes of inheritance, linkage analysis of quantitative traits has tended to rely on non-parametric methods based on regression or variance components analysis. Here, we describe a new method for LOD score analysis of quantitative traits which does not require specification of a mode of inheritance. The technique is derived from the MFLINK method for dichotomous traits. A range of plausible transmission models is constructed, constrained to yield the correct population mean and variance for the trait but differing with respect to the contribution to the variance due to the locus under consideration. Maximized LOD scores under homogeneity and admixture are calculated, as is a model-free LOD score which compares the maximized likelihoods under admixture assuming linkage and no linkage. These LOD scores have known asymptotic distributions and hence can be used to provide a statistical test for linkage. The method has been implemented in a program called QMFLINK. It was applied to data sets simulated using a variety of transmission models and to a measure of monoamine oxidase activity in 105 pedigrees from the Collaborative Study on the Genetics of Alcoholism. With the simulated data, the results showed that the new method could detect linkage well if the true allele frequency for the trait was close to that specified. However, it performed poorly on models in which the true allele frequency was much rarer. For the Collaborative Study on the Genetics of Alcoholism data set only a modest overlap was observed between the results obtained from the new method and those obtained when the same data were analysed previously using regression and variance components analysis. Of interest is that D17S250 produced a maximized LOD score under homogeneity and admixture of 2.6 but did not indicate linkage using the previous methods. However, this region did produce evidence for linkage in a separate data set
Parametric and Non-Parametric System Modelling
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg
1999-01-01
the focus is on combinations of parametric and non-parametric methods of regression. This combination can be in terms of additive models where e.g. one or more non-parametric term is added to a linear regression model. It can also be in terms of conditional parametric models where the coefficients...... considered. It is shown that adaptive estimation in conditional parametric models can be performed by combining the well known methods of local polynomial regression and recursive least squares with exponential forgetting. The approach used for estimation in conditional parametric models also highlights how...... networks is included. In this paper, neural networks are used for predicting the electricity production of a wind farm. The results are compared with results obtained using an adaptively estimated ARX-model. Finally, two papers on stochastic differential equations are included. In the first paper, among...
Nonparametric Bayes Modeling of Multivariate Categorical Data.
Dunson, David B; Xing, Chuanhua
2012-01-01
Modeling of multivariate unordered categorical (nominal) data is a challenging problem, particularly in high dimensions and cases in which one wishes to avoid strong assumptions about the dependence structure. Commonly used approaches rely on the incorporation of latent Gaussian random variables or parametric latent class models. The goal of this article is to develop a nonparametric Bayes approach, which defines a prior with full support on the space of distributions for multiple unordered categorical variables. This support condition ensures that we are not restricting the dependence structure a priori. We show this can be accomplished through a Dirichlet process mixture of product multinomial distributions, which is also a convenient form for posterior computation. Methods for nonparametric testing of violations of independence are proposed, and the methods are applied to model positional dependence within transcription factor binding motifs.
Network structure exploration via Bayesian nonparametric models
International Nuclear Information System (INIS)
Chen, Y; Wang, X L; Xiang, X; Tang, B Z; Bu, J Z
2015-01-01
Complex networks provide a powerful mathematical representation of complex systems in nature and society. To understand complex networks, it is crucial to explore their internal structures, also called structural regularities. The task of network structure exploration is to determine how many groups there are in a complex network and how to group the nodes of the network. Most existing structure exploration methods need to specify either a group number or a certain type of structure when they are applied to a network. In the real world, however, the group number and also the certain type of structure that a network has are usually unknown in advance. To explore structural regularities in complex networks automatically, without any prior knowledge of the group number or the certain type of structure, we extend a probabilistic mixture model that can handle networks with any type of structure but needs to specify a group number using Bayesian nonparametric theory. We also propose a novel Bayesian nonparametric model, called the Bayesian nonparametric mixture (BNPM) model. Experiments conducted on a large number of networks with different structures show that the BNPM model is able to explore structural regularities in networks automatically with a stable, state-of-the-art performance. (paper)
portfolio optimization based on nonparametric estimation methods
Directory of Open Access Journals (Sweden)
mahsa ghandehari
2017-03-01
Full Text Available One of the major issues investors are facing with in capital markets is decision making about select an appropriate stock exchange for investing and selecting an optimal portfolio. This process is done through the risk and expected return assessment. On the other hand in portfolio selection problem if the assets expected returns are normally distributed, variance and standard deviation are used as a risk measure. But, the expected returns on assets are not necessarily normal and sometimes have dramatic differences from normal distribution. This paper with the introduction of conditional value at risk ( CVaR, as a measure of risk in a nonparametric framework, for a given expected return, offers the optimal portfolio and this method is compared with the linear programming method. The data used in this study consists of monthly returns of 15 companies selected from the top 50 companies in Tehran Stock Exchange during the winter of 1392 which is considered from April of 1388 to June of 1393. The results of this study show the superiority of nonparametric method over the linear programming method and the nonparametric method is much faster than the linear programming method.
Nonparametric Mixture Models for Supervised Image Parcellation.
Sabuncu, Mert R; Yeo, B T Thomas; Van Leemput, Koen; Fischl, Bruce; Golland, Polina
2009-09-01
We present a nonparametric, probabilistic mixture model for the supervised parcellation of images. The proposed model yields segmentation algorithms conceptually similar to the recently developed label fusion methods, which register a new image with each training image separately. Segmentation is achieved via the fusion of transferred manual labels. We show that in our framework various settings of a model parameter yield algorithms that use image intensity information differently in determining the weight of a training subject during fusion. One particular setting computes a single, global weight per training subject, whereas another setting uses locally varying weights when fusing the training data. The proposed nonparametric parcellation approach capitalizes on recently developed fast and robust pairwise image alignment tools. The use of multiple registrations allows the algorithm to be robust to occasional registration failures. We report experiments on 39 volumetric brain MRI scans with expert manual labels for the white matter, cerebral cortex, ventricles and subcortical structures. The results demonstrate that the proposed nonparametric segmentation framework yields significantly better segmentation than state-of-the-art algorithms.
Robustifying Bayesian nonparametric mixtures for count data.
Canale, Antonio; Prünster, Igor
2017-03-01
Our motivating application stems from surveys of natural populations and is characterized by large spatial heterogeneity in the counts, which makes parametric approaches to modeling local animal abundance too restrictive. We adopt a Bayesian nonparametric approach based on mixture models and innovate with respect to popular Dirichlet process mixture of Poisson kernels by increasing the model flexibility at the level both of the kernel and the nonparametric mixing measure. This allows to derive accurate and robust estimates of the distribution of local animal abundance and of the corresponding clusters. The application and a simulation study for different scenarios yield also some general methodological implications. Adding flexibility solely at the level of the mixing measure does not improve inferences, since its impact is severely limited by the rigidity of the Poisson kernel with considerable consequences in terms of bias. However, once a kernel more flexible than the Poisson is chosen, inferences can be robustified by choosing a prior more general than the Dirichlet process. Therefore, to improve the performance of Bayesian nonparametric mixtures for count data one has to enrich the model simultaneously at both levels, the kernel and the mixing measure. © 2016, The International Biometric Society.
Data analysis with small samples and non-normal data nonparametrics and other strategies
Siebert, Carl F
2017-01-01
Written in everyday language for non-statisticians, this book provides all the information needed to successfully conduct nonparametric analyses. This ideal reference book provides step-by-step instructions to lead the reader through each analysis, screenshots of the software and output, and case scenarios to illustrate of all the analytic techniques.
International Nuclear Information System (INIS)
Cole, G.V.
1982-01-01
A pantograph linkage is actuated by two linear actuators, pivotally connected together at the linkage. The displacement of the actuators is monitored by rectilinear potentiometers to provide feedback signals to a microprocessor which also receives input signals related to a required movement of a slave end of the linkage. In response to these signals, the microprocessor provides signals to control the displacement of the linear actuators to effect the required movement of the slave end. The movement of the slave end might be straightline in a substantially horizontal or vertical direction. (author)
A non-parametric framework for estimating threshold limit values
Directory of Open Access Journals (Sweden)
Ulm Kurt
2005-11-01
Full Text Available Abstract Background To estimate a threshold limit value for a compound known to have harmful health effects, an 'elbow' threshold model is usually applied. We are interested on non-parametric flexible alternatives. Methods We describe how a step function model fitted by isotonic regression can be used to estimate threshold limit values. This method returns a set of candidate locations, and we discuss two algorithms to select the threshold among them: the reduced isotonic regression and an algorithm considering the closed family of hypotheses. We assess the performance of these two alternative approaches under different scenarios in a simulation study. We illustrate the framework by analysing the data from a study conducted by the German Research Foundation aiming to set a threshold limit value in the exposure to total dust at workplace, as a causal agent for developing chronic bronchitis. Results In the paper we demonstrate the use and the properties of the proposed methodology along with the results from an application. The method appears to detect the threshold with satisfactory success. However, its performance can be compromised by the low power to reject the constant risk assumption when the true dose-response relationship is weak. Conclusion The estimation of thresholds based on isotonic framework is conceptually simple and sufficiently powerful. Given that in threshold value estimation context there is not a gold standard method, the proposed model provides a useful non-parametric alternative to the standard approaches and can corroborate or challenge their findings.
Non-parametric smoothing of experimental data
International Nuclear Information System (INIS)
Kuketayev, A.T.; Pen'kov, F.M.
2007-01-01
Full text: Rapid processing of experimental data samples in nuclear physics often requires differentiation in order to find extrema. Therefore, even at the preliminary stage of data analysis, a range of noise reduction methods are used to smooth experimental data. There are many non-parametric smoothing techniques: interval averages, moving averages, exponential smoothing, etc. Nevertheless, it is more common to use a priori information about the behavior of the experimental curve in order to construct smoothing schemes based on the least squares techniques. The latter methodology's advantage is that the area under the curve can be preserved, which is equivalent to conservation of total speed of counting. The disadvantages of this approach include the lack of a priori information. For example, very often the sums of undifferentiated (by a detector) peaks are replaced with one peak during the processing of data, introducing uncontrolled errors in the determination of the physical quantities. The problem is solvable only by having experienced personnel, whose skills are much greater than the challenge. We propose a set of non-parametric techniques, which allows the use of any additional information on the nature of experimental dependence. The method is based on a construction of a functional, which includes both experimental data and a priori information. Minimum of this functional is reached on a non-parametric smoothed curve. Euler (Lagrange) differential equations are constructed for these curves; then their solutions are obtained analytically or numerically. The proposed approach allows for automated processing of nuclear physics data, eliminating the need for highly skilled laboratory personnel. Pursuant to the proposed approach is the possibility to obtain smoothing curves in a given confidence interval, e.g. according to the χ 2 distribution. This approach is applicable when constructing smooth solutions of ill-posed problems, in particular when solving
Linkage Analysis in Autoimmune Addison's Disease: NFATC1 as a Potential Novel Susceptibility Locus.
Directory of Open Access Journals (Sweden)
Anna L Mitchell
Full Text Available Autoimmune Addison's disease (AAD is a rare, highly heritable autoimmune endocrinopathy. It is possible that there may be some highly penetrant variants which confer disease susceptibility that have yet to be discovered.DNA samples from 23 multiplex AAD pedigrees from the UK and Norway (50 cases, 67 controls were genotyped on the Affymetrix SNP 6.0 array. Linkage analysis was performed using Merlin. EMMAX was used to carry out a genome-wide association analysis comparing the familial AAD cases to 2706 UK WTCCC controls. To explore some of the linkage findings further, a replication study was performed by genotyping 64 SNPs in two of the four linked regions (chromosomes 7 and 18, on the Sequenom iPlex platform in three European AAD case-control cohorts (1097 cases, 1117 controls. The data were analysed using a meta-analysis approach.In a parametric analysis, applying a rare dominant model, loci on chromosomes 7, 9 and 18 had LOD scores >2.8. In a non-parametric analysis, a locus corresponding to the HLA region on chromosome 6, known to be associated with AAD, had a LOD score >3.0. In the genome-wide association analysis, a SNP cluster on chromosome 2 and a pair of SNPs on chromosome 6 were associated with AAD (P <5x10-7. A meta-analysis of the replication study data demonstrated that three chromosome 18 SNPs were associated with AAD, including a non-synonymous variant in the NFATC1 gene.This linkage study has implicated a number of novel chromosomal regions in the pathogenesis of AAD in multiplex AAD families and adds further support to the role of HLA in AAD. The genome-wide association analysis has also identified a region of interest on chromosome 2. A replication study has demonstrated that the NFATC1 gene is worthy of future investigation, however each of the regions identified require further, systematic analysis.
Linkage Analysis in Autoimmune Addison's Disease: NFATC1 as a Potential Novel Susceptibility Locus.
Mitchell, Anna L; Bøe Wolff, Anette; MacArthur, Katie; Weaver, Jolanta U; Vaidya, Bijay; Erichsen, Martina M; Darlay, Rebecca; Husebye, Eystein S; Cordell, Heather J; Pearce, Simon H S
2015-01-01
Autoimmune Addison's disease (AAD) is a rare, highly heritable autoimmune endocrinopathy. It is possible that there may be some highly penetrant variants which confer disease susceptibility that have yet to be discovered. DNA samples from 23 multiplex AAD pedigrees from the UK and Norway (50 cases, 67 controls) were genotyped on the Affymetrix SNP 6.0 array. Linkage analysis was performed using Merlin. EMMAX was used to carry out a genome-wide association analysis comparing the familial AAD cases to 2706 UK WTCCC controls. To explore some of the linkage findings further, a replication study was performed by genotyping 64 SNPs in two of the four linked regions (chromosomes 7 and 18), on the Sequenom iPlex platform in three European AAD case-control cohorts (1097 cases, 1117 controls). The data were analysed using a meta-analysis approach. In a parametric analysis, applying a rare dominant model, loci on chromosomes 7, 9 and 18 had LOD scores >2.8. In a non-parametric analysis, a locus corresponding to the HLA region on chromosome 6, known to be associated with AAD, had a LOD score >3.0. In the genome-wide association analysis, a SNP cluster on chromosome 2 and a pair of SNPs on chromosome 6 were associated with AAD (P <5x10-7). A meta-analysis of the replication study data demonstrated that three chromosome 18 SNPs were associated with AAD, including a non-synonymous variant in the NFATC1 gene. This linkage study has implicated a number of novel chromosomal regions in the pathogenesis of AAD in multiplex AAD families and adds further support to the role of HLA in AAD. The genome-wide association analysis has also identified a region of interest on chromosome 2. A replication study has demonstrated that the NFATC1 gene is worthy of future investigation, however each of the regions identified require further, systematic analysis.
Decompounding random sums: A nonparametric approach
DEFF Research Database (Denmark)
Hansen, Martin Bøgsted; Pitts, Susan M.
Observations from sums of random variables with a random number of summands, known as random, compound or stopped sums arise within many areas of engineering and science. Quite often it is desirable to infer properties of the distribution of the terms in the random sum. In the present paper we...... review a number of applications and consider the nonlinear inverse problem of inferring the cumulative distribution function of the components in the random sum. We review the existing literature on non-parametric approaches to the problem. The models amenable to the analysis are generalized considerably...
A Nonparametric Test for Seasonal Unit Roots
Kunst, Robert M.
2009-01-01
Abstract: We consider a nonparametric test for the null of seasonal unit roots in quarterly time series that builds on the RUR (records unit root) test by Aparicio, Escribano, and Sipols. We find that the test concept is more promising than a formalization of visual aids such as plots by quarter. In order to cope with the sensitivity of the original RUR test to autocorrelation under its null of a unit root, we suggest an augmentation step by autoregression. We present some evidence on the siz...
Multi-Directional Non-Parametric Analysis of Agricultural Efficiency
DEFF Research Database (Denmark)
Balezentis, Tomas
This thesis seeks to develop methodologies for assessment of agricultural efficiency and employ them to Lithuanian family farms. In particular, we focus on three particular objectives throughout the research: (i) to perform a fully non-parametric analysis of efficiency effects, (ii) to extend...... to the Multi-Directional Efficiency Analysis approach when the proposed models were employed to analyse empirical data of Lithuanian family farm performance, we saw substantial differences in efficiencies associated with different inputs. In particular, assets appeared to be the least efficiently used input...... relative to labour, intermediate consumption and land (in some cases land was not treated as a discretionary input). These findings call for further research on relationships among financial structure, investment decisions, and efficiency in Lithuanian family farms. Application of different techniques...
Bayesian Nonparametric Clustering for Positive Definite Matrices.
Cherian, Anoop; Morellas, Vassilios; Papanikolopoulos, Nikolaos
2016-05-01
Symmetric Positive Definite (SPD) matrices emerge as data descriptors in several applications of computer vision such as object tracking, texture recognition, and diffusion tensor imaging. Clustering these data matrices forms an integral part of these applications, for which soft-clustering algorithms (K-Means, expectation maximization, etc.) are generally used. As is well-known, these algorithms need the number of clusters to be specified, which is difficult when the dataset scales. To address this issue, we resort to the classical nonparametric Bayesian framework by modeling the data as a mixture model using the Dirichlet process (DP) prior. Since these matrices do not conform to the Euclidean geometry, rather belongs to a curved Riemannian manifold,existing DP models cannot be directly applied. Thus, in this paper, we propose a novel DP mixture model framework for SPD matrices. Using the log-determinant divergence as the underlying dissimilarity measure to compare these matrices, and further using the connection between this measure and the Wishart distribution, we derive a novel DPM model based on the Wishart-Inverse-Wishart conjugate pair. We apply this model to several applications in computer vision. Our experiments demonstrate that our model is scalable to the dataset size and at the same time achieves superior accuracy compared to several state-of-the-art parametric and nonparametric clustering algorithms.
Thermally actuated linkage arrangement
International Nuclear Information System (INIS)
Anderson, P.M.
1981-01-01
A reusable thermally actuated linkage arrangement includes a first link member having a longitudinal bore therein adapted to receive at least a portion of a second link member therein, the first and second members being sized to effect an interference fit preventing relative movement there-between at a temperature below a predetermined temperature. The link members have different coefficients of thermal expansion so that when the linkage is selectively heated by heating element to a temperature above the predetermined temperature, relative longitudinal and/or rotational movement between the first and second link members is enabled. Two embodiments of a thermally activated linkage are disclosed which find particular application in actuators for a grapple head positioning arm in a nuclear reactor fuel handling mechanism to facilitate back-up safety retraction of the grapple head independently from the primary fuel handling mechanism drive system. (author)
DEFF Research Database (Denmark)
Zhang, Dong Feng; Pang, Zengchang; Li, Shuxia
2012-01-01
The genetic loci affecting the commonly used BMI have been intensively investigated using linkage approaches in multiple populations. This study aims at performing the first genome-wide linkage scan on BMI in the Chinese population in mainland China with hypothesis that heterogeneity in genetic...... linkage could exist in different ethnic populations. BMI was measured from 126 dizygotic twins in Qingdao municipality who were genotyped using high-resolution Affymetrix Genome-Wide Human SNP arrays containing about 1 million single-nucleotide polymorphisms (SNPs). Nonparametric linkage analysis...... in western countries. Multiple loci showing suggestive linkage were found on chromosome 1 (lod score 2.38 at 242 cM), chromosome 8 (2.48 at 95 cM), and chromosome 14 (2.2 at 89.4 cM). The strong linkage identified in the Chinese subjects that is consistent with that found in populations of European origin...
Genome-wide linkage analysis for human longevity
DEFF Research Database (Denmark)
Beekman, Marian; Blanché, Hélène; Perola, Markus
2013-01-01
Clear evidence exists for heritability of human longevity, and much interest is focused on identifying genes associated with longer lives. To identify such longevity alleles, we performed the largest genome-wide linkage scan thus far reported. Linkage analyses included 2118 nonagenarian Caucasian...
On Parametric (and Non-Parametric Variation
Directory of Open Access Journals (Sweden)
Neil Smith
2009-11-01
Full Text Available This article raises the issue of the correct characterization of ‘Parametric Variation’ in syntax and phonology. After specifying their theoretical commitments, the authors outline the relevant parts of the Principles–and–Parameters framework, and draw a three-way distinction among Universal Principles, Parameters, and Accidents. The core of the contribution then consists of an attempt to provide identity criteria for parametric, as opposed to non-parametric, variation. Parametric choices must be antecedently known, and it is suggested that they must also satisfy seven individually necessary and jointly sufficient criteria. These are that they be cognitively represented, systematic, dependent on the input, deterministic, discrete, mutually exclusive, and irreversible.
Nonparametric predictive pairwise comparison with competing risks
International Nuclear Information System (INIS)
Coolen-Maturi, Tahani
2014-01-01
In reliability, failure data often correspond to competing risks, where several failure modes can cause a unit to fail. This paper presents nonparametric predictive inference (NPI) for pairwise comparison with competing risks data, assuming that the failure modes are independent. These failure modes could be the same or different among the two groups, and these can be both observed and unobserved failure modes. NPI is a statistical approach based on few assumptions, with inferences strongly based on data and with uncertainty quantified via lower and upper probabilities. The focus is on the lower and upper probabilities for the event that the lifetime of a future unit from one group, say Y, is greater than the lifetime of a future unit from the second group, say X. The paper also shows how the two groups can be compared based on particular failure mode(s), and the comparison of the two groups when some of the competing risks are combined is discussed
Nonparametric estimation of location and scale parameters
Potgieter, C.J.
2012-12-01
Two random variables X and Y belong to the same location-scale family if there are constants μ and σ such that Y and μ+σX have the same distribution. In this paper we consider non-parametric estimation of the parameters μ and σ under minimal assumptions regarding the form of the distribution functions of X and Y. We discuss an approach to the estimation problem that is based on asymptotic likelihood considerations. Our results enable us to provide a methodology that can be implemented easily and which yields estimators that are often near optimal when compared to fully parametric methods. We evaluate the performance of the estimators in a series of Monte Carlo simulations. © 2012 Elsevier B.V. All rights reserved.
Nonparametric inference of network structure and dynamics
Peixoto, Tiago P.
The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among
Nefedov, Andrey
2015-01-01
This work provides a typologically oriented description of clause linkage strategies in Ket, a highly endangered language spoken in Central Siberia. It is now the only surviving member of the Yeniseian language family with the last remaining speakers residing in the north of Russia’s Krasnoyarsk
A NONPARAMETRIC HYPOTHESIS TEST VIA THE BOOTSTRAP RESAMPLING
Temel, Tugrul T.
2001-01-01
This paper adapts an already existing nonparametric hypothesis test to the bootstrap framework. The test utilizes the nonparametric kernel regression method to estimate a measure of distance between the models stated under the null hypothesis. The bootstraped version of the test allows to approximate errors involved in the asymptotic hypothesis test. The paper also develops a Mathematica Code for the test algorithm.
Simple nonparametric checks for model data fit in CAT
Meijer, R.R.
2005-01-01
In this paper, the usefulness of several nonparametric checks is discussed in a computerized adaptive testing (CAT) context. Although there is no tradition of nonparametric scalability in CAT, it can be argued that scalability checks can be useful to investigate, for example, the quality of item
Nonparametric Bayesian inference for multidimensional compound Poisson processes
Gugushvili, S.; van der Meulen, F.; Spreij, P.
2015-01-01
Given a sample from a discretely observed multidimensional compound Poisson process, we study the problem of nonparametric estimation of its jump size density r0 and intensity λ0. We take a nonparametric Bayesian approach to the problem and determine posterior contraction rates in this context,
Nonparametric analysis of blocked ordered categories data: some examples revisited
Directory of Open Access Journals (Sweden)
O. Thas
2006-08-01
Full Text Available Nonparametric analysis for general block designs can be given by using the Cochran-Mantel-Haenszel (CMH statistics. We demonstrate this with four examples and note that several well-known nonparametric statistics are special cases of CMH statistics.
A Structural Labor Supply Model with Nonparametric Preferences
van Soest, A.H.O.; Das, J.W.M.; Gong, X.
2000-01-01
Nonparametric techniques are usually seen as a statistic device for data description and exploration, and not as a tool for estimating models with a richer economic structure, which are often required for policy analysis.This paper presents an example where nonparametric flexibility can be attained
Directory of Open Access Journals (Sweden)
Farook Thameem
Full Text Available Estimated glomerular filtration rate (eGFR, a measure of kidney function, is heritable, suggesting that genes influence renal function. Genes that influence eGFR have been identified through genome-wide association studies. However, family-based linkage approaches may identify loci that explain a larger proportion of the heritability. This study used genome-wide linkage and association scans to identify quantitative trait loci (QTL that influence eGFR.Genome-wide linkage and sparse association scans of eGFR were performed in families ascertained by probands with advanced diabetic nephropathy (DN from the multi-ethnic Family Investigation of Nephropathy and Diabetes (FIND study. This study included 954 African Americans (AA, 781 American Indians (AI, 614 European Americans (EA and 1,611 Mexican Americans (MA. A total of 3,960 FIND participants were genotyped for 6,000 single nucleotide polymorphisms (SNPs using the Illumina Linkage IVb panel. GFR was estimated by the Modification of Diet in Renal Disease (MDRD formula.The non-parametric linkage analysis, accounting for the effects of diabetes duration and BMI, identified the strongest evidence for linkage of eGFR on chromosome 20q11 (log of the odds [LOD] = 3.34; P = 4.4 × 10(-5 in MA and chromosome 15q12 (LOD = 2.84; P = 1.5 × 10(-4 in EA. In all subjects, the strongest linkage signal for eGFR was detected on chromosome 10p12 (P = 5.5 × 10(-4 at 44 cM near marker rs1339048. A subsequent association scan in both ancestry-specific groups and the entire population identified several SNPs significantly associated with eGFR across the genome.The present study describes the localization of QTL influencing eGFR on 20q11 in MA, 15q21 in EA and 10p12 in the combined ethnic groups participating in the FIND study. Identification of causal genes/variants influencing eGFR, within these linkage and association loci, will open new avenues for functional analyses and development of novel diagnostic markers
Thameem, Farook; Igo, Robert P; Freedman, Barry I; Langefeld, Carl; Hanson, Robert L; Schelling, Jeffrey R; Elston, Robert C; Duggirala, Ravindranath; Nicholas, Susanne B; Goddard, Katrina A B; Divers, Jasmin; Guo, Xiuqing; Ipp, Eli; Kimmel, Paul L; Meoni, Lucy A; Shah, Vallabh O; Smith, Michael W; Winkler, Cheryl A; Zager, Philip G; Knowler, William C; Nelson, Robert G; Pahl, Madeline V; Parekh, Rulan S; Kao, W H Linda; Rasooly, Rebekah S; Adler, Sharon G; Abboud, Hanna E; Iyengar, Sudha K; Sedor, John R
2013-01-01
Estimated glomerular filtration rate (eGFR), a measure of kidney function, is heritable, suggesting that genes influence renal function. Genes that influence eGFR have been identified through genome-wide association studies. However, family-based linkage approaches may identify loci that explain a larger proportion of the heritability. This study used genome-wide linkage and association scans to identify quantitative trait loci (QTL) that influence eGFR. Genome-wide linkage and sparse association scans of eGFR were performed in families ascertained by probands with advanced diabetic nephropathy (DN) from the multi-ethnic Family Investigation of Nephropathy and Diabetes (FIND) study. This study included 954 African Americans (AA), 781 American Indians (AI), 614 European Americans (EA) and 1,611 Mexican Americans (MA). A total of 3,960 FIND participants were genotyped for 6,000 single nucleotide polymorphisms (SNPs) using the Illumina Linkage IVb panel. GFR was estimated by the Modification of Diet in Renal Disease (MDRD) formula. The non-parametric linkage analysis, accounting for the effects of diabetes duration and BMI, identified the strongest evidence for linkage of eGFR on chromosome 20q11 (log of the odds [LOD] = 3.34; P = 4.4 × 10(-5)) in MA and chromosome 15q12 (LOD = 2.84; P = 1.5 × 10(-4)) in EA. In all subjects, the strongest linkage signal for eGFR was detected on chromosome 10p12 (P = 5.5 × 10(-4)) at 44 cM near marker rs1339048. A subsequent association scan in both ancestry-specific groups and the entire population identified several SNPs significantly associated with eGFR across the genome. The present study describes the localization of QTL influencing eGFR on 20q11 in MA, 15q21 in EA and 10p12 in the combined ethnic groups participating in the FIND study. Identification of causal genes/variants influencing eGFR, within these linkage and association loci, will open new avenues for functional analyses and development of novel diagnostic markers
Nonparametric statistics with applications to science and engineering
Kvam, Paul H
2007-01-01
A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics This book presents a practical approach to nonparametric statistical analysis and provides comprehensive coverage of both established and newly developed methods. With the use of MATLAB, the authors present information on theorems and rank tests in an applied fashion, with an emphasis on modern methods in regression and curve fitting, bootstrap confidence intervals, splines, wavelets, empirical likelihood, and goodness-of-fit testing. Nonparametric Statistics with Applications to Science and Engineering begins with succinct coverage of basic results for order statistics, methods of categorical data analysis, nonparametric regression, and curve fitting methods. The authors then focus on nonparametric procedures that are becoming more relevant to engineering researchers and practitioners. The important fundamental materials needed to effectively learn and apply the discussed methods are also provide...
2nd Conference of the International Society for Nonparametric Statistics
Manteiga, Wenceslao; Romo, Juan
2016-01-01
This volume collects selected, peer-reviewed contributions from the 2nd Conference of the International Society for Nonparametric Statistics (ISNPS), held in Cádiz (Spain) between June 11–16 2014, and sponsored by the American Statistical Association, the Institute of Mathematical Statistics, the Bernoulli Society for Mathematical Statistics and Probability, the Journal of Nonparametric Statistics and Universidad Carlos III de Madrid. The 15 articles are a representative sample of the 336 contributed papers presented at the conference. They cover topics such as high-dimensional data modelling, inference for stochastic processes and for dependent data, nonparametric and goodness-of-fit testing, nonparametric curve estimation, object-oriented data analysis, and semiparametric inference. The aim of the ISNPS 2014 conference was to bring together recent advances and trends in several areas of nonparametric statistics in order to facilitate the exchange of research ideas, promote collaboration among researchers...
Nonparametric methods in actigraphy: An update
Directory of Open Access Journals (Sweden)
Bruno S.B. Gonçalves
2014-09-01
Full Text Available Circadian rhythmicity in humans has been well studied using actigraphy, a method of measuring gross motor movement. As actigraphic technology continues to evolve, it is important for data analysis to keep pace with new variables and features. Our objective is to study the behavior of two variables, interdaily stability and intradaily variability, to describe rest activity rhythm. Simulated data and actigraphy data of humans, rats, and marmosets were used in this study. We modified the method of calculation for IV and IS by modifying the time intervals of analysis. For each variable, we calculated the average value (IVm and ISm results for each time interval. Simulated data showed that (1 synchronization analysis depends on sample size, and (2 fragmentation is independent of the amplitude of the generated noise. We were able to obtain a significant difference in the fragmentation patterns of stroke patients using an IVm variable, while the variable IV60 was not identified. Rhythmic synchronization of activity and rest was significantly higher in young than adults with Parkinson׳s when using the ISM variable; however, this difference was not seen using IS60. We propose an updated format to calculate rhythmic fragmentation, including two additional optional variables. These alternative methods of nonparametric analysis aim to more precisely detect sleep–wake cycle fragmentation and synchronization.
Bayesian nonparametric adaptive control using Gaussian processes.
Chowdhary, Girish; Kingravi, Hassan A; How, Jonathan P; Vela, Patricio A
2015-03-01
Most current model reference adaptive control (MRAC) methods rely on parametric adaptive elements, in which the number of parameters of the adaptive element are fixed a priori, often through expert judgment. An example of such an adaptive element is radial basis function networks (RBFNs), with RBF centers preallocated based on the expected operating domain. If the system operates outside of the expected operating domain, this adaptive element can become noneffective in capturing and canceling the uncertainty, thus rendering the adaptive controller only semiglobal in nature. This paper investigates a Gaussian process-based Bayesian MRAC architecture (GP-MRAC), which leverages the power and flexibility of GP Bayesian nonparametric models of uncertainty. The GP-MRAC does not require the centers to be preallocated, can inherently handle measurement noise, and enables MRAC to handle a broader set of uncertainties, including those that are defined as distributions over functions. We use stochastic stability arguments to show that GP-MRAC guarantees good closed-loop performance with no prior domain knowledge of the uncertainty. Online implementable GP inference methods are compared in numerical simulations against RBFN-MRAC with preallocated centers and are shown to provide better tracking and improved long-term learning.
DEFF Research Database (Denmark)
Ansari-Mahyari, S; Berg, P; Lund, M S
2009-01-01
disequilibrium-based sampling criteria (LDC) for selecting individuals to phenotype are compared to random phenotyping in a quantitative trait loci (QTL) verification experiment using stochastic simulation. Several strategies based on LAC and LDC for selecting the most informative 30%, 40% or 50% of individuals...... for phenotyping to extract maximum power and precision in a QTL fine mapping experiment were developed and assessed. Linkage analyses for the mapping was performed for individuals sampled on LAC within families and combined linkage disequilibrium and linkage analyses was performed for individuals sampled across...... the whole population based on LDC. The results showed that selecting individuals with similar haplotypes to the paternal haplotypes (minimum recombination criterion) using LAC compared to random phenotyping gave at least the same power to detect a QTL but decreased the accuracy of the QTL position. However...
Directory of Open Access Journals (Sweden)
Yi-Ju Li
Full Text Available Fuchs endothelial corneal dystrophy (FECD is a common, late-onset disorder of the corneal endothelium. Although progress has been made in understanding the genetic basis of FECD by studying large families in which the phenotype is transmitted in an autosomal dominant fashion, a recently reported genome-wide association study identified common alleles at a locus on chromosome 18 near TCF4 which confer susceptibility to FECD. Here, we report the findings of our independent validation study for TCF4 using the largest FECD dataset to date (450 FECD cases and 340 normal controls. Logistic regression with sex as a covariate was performed for three genetic models: dominant (DOM, additive (ADD, and recessive (REC. We found significant association with rs613872, the target marker reported by Baratz et al.(2010, for all three genetic models (DOM: P = 9.33×10(-35; ADD: P = 7.48×10(-30; REC: P = 5.27×10(-6. To strengthen the association study, we also conducted a genome-wide linkage scan on 64 multiplex families, composed primarily of affected sibling pairs (ASPs, using both parametric and non-parametric two-point and multipoint analyses. The most significant linkage region localizes to chromosome 18 from 69.94cM to 85.29cM, with a peak multipoint HLOD = 2.5 at rs1145315 (75.58cM under the DOM model, mapping 1.5 Mb proximal to rs613872. In summary, our study presents evidence to support the role of the intronic TCF4 single nucleotide polymorphism rs613872 in late-onset FECD through both association and linkage studies.
Nonparametric NAR-ARCH Modelling of Stock Prices by the Kernel Methodology
Directory of Open Access Journals (Sweden)
Mohamed Chikhi
2018-02-01
Full Text Available This paper analyses cyclical behaviour of Orange stock price listed in French stock exchange over 01/03/2000 to 02/02/2017 by testing the nonlinearities through a class of conditional heteroscedastic nonparametric models. The linearity and Gaussianity assumptions are rejected for Orange Stock returns and informational shocks have transitory effects on returns and volatility. The forecasting results show that Orange stock prices are short-term predictable and nonparametric NAR-ARCH model has better performance over parametric MA-APARCH model for short horizons. Plus, the estimates of this model are also better comparing to the predictions of the random walk model. This finding provides evidence for weak form of inefficiency in Paris stock market with limited rationality, thus it emerges arbitrage opportunities.
Weak Disposability in Nonparametric Production Analysis with Undesirable Outputs
Kuosmanen, T.K.
2005-01-01
Environmental Economics and Natural Resources Group at Wageningen University in The Netherlands Weak disposability of outputs means that firms can abate harmful emissions by decreasing the activity level. Modeling weak disposability in nonparametric production analysis has caused some confusion.
Multi-sample nonparametric treatments comparison in medical ...
African Journals Online (AJOL)
Multi-sample nonparametric treatments comparison in medical follow-up study with unequal observation processes through simulation and bladder tumour case study. P. L. Tan, N.A. Ibrahim, M.B. Adam, J. Arasan ...
Speaker Linking and Applications using Non-Parametric Hashing Methods
2016-09-08
nonparametric estimate of a multivariate density function,” The Annals of Math- ematical Statistics , vol. 36, no. 3, pp. 1049–1051, 1965. [9] E. A. Patrick...Speaker Linking and Applications using Non-Parametric Hashing Methods† Douglas Sturim and William M. Campbell MIT Lincoln Laboratory, Lexington, MA...with many approaches [1, 2]. For this paper, we focus on using i-vectors [2], but the methods apply to any embedding. For the task of speaker QBE and
Design of special planar linkages
Zhao, Jing-Shan; Ma, Ning; Chu, Fulei
2013-01-01
Planar linkages play a very important role in mechanical engineering. As the simplest closed chain mechanisms, planar four-bar linkages are widely used in mechanical engineering, civil engineering and aerospace engineering.Design of Special Planar Linkages proposes a uniform design theory for planar four-bar linkages. The merit of the method proposed in this book is that it allows engineers to directly obtain accurate results when there are such solutions for the specified n precise positions; otherwise, the best approximate solutions will be found. This book discusses the kinematics and reach
A Formalization of Linkage Analysis
DEFF Research Database (Denmark)
Ingolfsdottir, Anna; Christensen, A.I.; Hansen, Jens A.
In this report a formalization of genetic linkage analysis is introduced. Linkage analysis is a computationally hard biomathematical method, which purpose is to locate genes on the human genome. It is rooted in the new area of bioinformatics and no formalization of the method has previously been ...
Predicting Market Impact Costs Using Nonparametric Machine Learning Models.
Directory of Open Access Journals (Sweden)
Saerom Park
Full Text Available Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.
Predicting Market Impact Costs Using Nonparametric Machine Learning Models.
Park, Saerom; Lee, Jaewook; Son, Youngdoo
2016-01-01
Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.
Application of nonparametric statistic method for DNBR limit calculation
International Nuclear Information System (INIS)
Dong Bo; Kuang Bo; Zhu Xuenong
2013-01-01
Background: Nonparametric statistical method is a kind of statistical inference method not depending on a certain distribution; it calculates the tolerance limits under certain probability level and confidence through sampling methods. The DNBR margin is one important parameter of NPP design, which presents the safety level of NPP. Purpose and Methods: This paper uses nonparametric statistical method basing on Wilks formula and VIPER-01 subchannel analysis code to calculate the DNBR design limits (DL) of 300 MW NPP (Nuclear Power Plant) during the complete loss of flow accident, simultaneously compared with the DL of DNBR through means of ITDP to get certain DNBR margin. Results: The results indicate that this method can gain 2.96% DNBR margin more than that obtained by ITDP methodology. Conclusions: Because of the reduction of the conservation during analysis process, the nonparametric statistical method can provide greater DNBR margin and the increase of DNBR margin is benefited for the upgrading of core refuel scheme. (authors)
A nonparametric spatial scan statistic for continuous data.
Jung, Inkyung; Cho, Ho Jin
2015-10-20
Spatial scan statistics are widely used for spatial cluster detection, and several parametric models exist. For continuous data, a normal-based scan statistic can be used. However, the performance of the model has not been fully evaluated for non-normal data. We propose a nonparametric spatial scan statistic based on the Wilcoxon rank-sum test statistic and compared the performance of the method with parametric models via a simulation study under various scenarios. The nonparametric method outperforms the normal-based scan statistic in terms of power and accuracy in almost all cases under consideration in the simulation study. The proposed nonparametric spatial scan statistic is therefore an excellent alternative to the normal model for continuous data and is especially useful for data following skewed or heavy-tailed distributions.
Asherson, P; Zhou, K; Anney, R J L; Franke, B; Buitelaar, J; Ebstein, R; Gill, M; Altink, M; Arnold, R; Boer, F; Brookes, K; Buschgens, C; Butler, L; Cambell, D; Chen, W; Christiansen, H; Feldman, L; Fleischman, K; Fliers, E; Howe-Forbes, R; Goldfarb, A; Heise, A; Gabriëls, I; Johansson, L; Lubetzki, I; Marco, R; Medad, S; Minderaa, R; Mulas, F; Müller, U; Mulligan, A; Neale, B; Rijsdijk, F; Rabin, K; Rommelse, N; Sethna, V; Sorohan, J; Uebel, H; Psychogiou, L; Weeks, A; Barrett, R; Xu, X; Banaschewski, T; Sonuga-Barke, E; Eisenberg, J; Manor, I; Miranda, A; Oades, R D; Roeyers, H; Rothenberger, A; Sergeant, J; Steinhausen, H-C; Taylor, E; Thompson, M; Faraone, S V
2008-05-01
As part of the International Multi-centre ADHD Genetics project we completed an affected sibling pair study of 142 narrowly defined Diagnostic and Statistical Manual of Mental Disorders, fourth edition combined type attention deficit hyperactivity disorder (ADHD) proband-sibling pairs. No linkage was observed on the most established ADHD-linked genomic regions of 5p and 17p. We found suggestive linkage signals on chromosomes 9 and 16, respectively, with the highest multipoint nonparametric linkage signal on chromosome 16q23 at 99 cM (log of the odds, LOD=3.1) overlapping data published from the previous UCLA (University of California, Los Angeles) (LOD>1, approximately 95 cM) and Dutch (LOD>1, approximately 100 cM) studies. The second highest peak in this study was on chromosome 9q22 at 90 cM (LOD=2.13); both the previous UCLA and German studies also found some evidence of linkage at almost the same location (UCLA LOD=1.45 at 93 cM; German LOD=0.68 at 100 cM). The overlap of these two main peaks with previous findings suggests that loci linked to ADHD may lie within these regions. Meta-analysis or reanalysis of the raw data of all the available ADHD linkage scan data may help to clarify whether these represent true linked loci.
DEFF Research Database (Denmark)
Sørensen, Olav Jull; Kuada, John
2006-01-01
Based on empirical studies of linkages between TNCs and local firms in India, Malaysia, Vietnam, Ghana and South Africa, five themes are discussed and related to present theoretical perspectives. The themes are (1) Linakge Governance; (2) Globalisation and the dynamics in developing countries (the...... TNC-driven markets in developing countries); (3) The upgrading impact of FDI; (4) Non-equity linkages as a platform for business development, and (5) The learning perspective on international business linakges. The chapter offers at the end a three-dimanional model for impacts of business linkages....
From Enclave to Linkage Economies?
DEFF Research Database (Denmark)
Hansen, Michael W.
as the enclave economy par excellence, moving in with fully integrated value chains, extracting resources and exporting them as commodities having virtually no linkages to the local economy. However, new opportunities for promoting linkages are offered by changing business strategies of local African enterprises...... as well as foreign multinational corporations (MNCs). MNCs in extractives are increasingly seeking local linkages as part of their efficiency, risk, and asset-seeking strategies, and linkage programmes are becoming integral elements in many MNCs’ corporate social responsibility (CSR) activities....... At the same time, local African enterprises are eager to, and increasingly capable of, linking up to the foreign investors in order to expand their activities and acquire technology, skills and market access. The changing strategies of MNCs and the improving capabilities of African enterprises offer new...
Adaptive nonparametric Bayesian inference using location-scale mixture priors
Jonge, de R.; Zanten, van J.H.
2010-01-01
We study location-scale mixture priors for nonparametric statistical problems, including multivariate regression, density estimation and classification. We show that a rate-adaptive procedure can be obtained if the prior is properly constructed. In particular, we show that adaptation is achieved if
The nonparametric bootstrap for the current status model
Groeneboom, P.; Hendrickx, K.
2017-01-01
It has been proved that direct bootstrapping of the nonparametric maximum likelihood estimator (MLE) of the distribution function in the current status model leads to inconsistent confidence intervals. We show that bootstrapping of functionals of the MLE can however be used to produce valid
Non-Parametric Analysis of Rating Transition and Default Data
DEFF Research Database (Denmark)
Fledelius, Peter; Lando, David; Perch Nielsen, Jens
2004-01-01
We demonstrate the use of non-parametric intensity estimation - including construction of pointwise confidence sets - for analyzing rating transition data. We find that transition intensities away from the class studied here for illustration strongly depend on the direction of the previous move b...
Bayesian nonparametric system reliability using sets of priors
Walter, G.M.; Aslett, L.J.M.; Coolen, F.P.A.
2016-01-01
An imprecise Bayesian nonparametric approach to system reliability with multiple types of components is developed. This allows modelling partial or imperfect prior knowledge on component failure distributions in a flexible way through bounds on the functioning probability. Given component level test
Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.
Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F
2013-04-01
In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.
Nonparametric modeling of dynamic functional connectivity in fmri data
DEFF Research Database (Denmark)
Nielsen, Søren Føns Vind; Madsen, Kristoffer H.; Røge, Rasmus
2015-01-01
dynamic changes. The existing approaches modeling dynamic connectivity have primarily been based on time-windowing the data and k-means clustering. We propose a nonparametric generative model for dynamic FC in fMRI that does not rely on specifying window lengths and number of dynamic states. Rooted...
Surface Estimation, Variable Selection, and the Nonparametric Oracle Property.
Storlie, Curtis B; Bondell, Howard D; Reich, Brian J; Zhang, Hao Helen
2011-04-01
Variable selection for multivariate nonparametric regression is an important, yet challenging, problem due, in part, to the infinite dimensionality of the function space. An ideal selection procedure should be automatic, stable, easy to use, and have desirable asymptotic properties. In particular, we define a selection procedure to be nonparametric oracle (np-oracle) if it consistently selects the correct subset of predictors and at the same time estimates the smooth surface at the optimal nonparametric rate, as the sample size goes to infinity. In this paper, we propose a model selection procedure for nonparametric models, and explore the conditions under which the new method enjoys the aforementioned properties. Developed in the framework of smoothing spline ANOVA, our estimator is obtained via solving a regularization problem with a novel adaptive penalty on the sum of functional component norms. Theoretical properties of the new estimator are established. Additionally, numerous simulated and real examples further demonstrate that the new approach substantially outperforms other existing methods in the finite sample setting.
Parametric vs. Nonparametric Regression Modelling within Clinical Decision Support
Czech Academy of Sciences Publication Activity Database
Kalina, Jan; Zvárová, Jana
2017-01-01
Roč. 5, č. 1 (2017), s. 21-27 ISSN 1805-8698 R&D Projects: GA ČR GA17-01251S Institutional support: RVO:67985807 Keywords : decision support systems * decision rules * statistical analysis * nonparametric regression Subject RIV: IN - Informatics, Computer Science OBOR OECD: Statistics and probability
On the robust nonparametric regression estimation for a functional regressor
Azzedine , Nadjia; Laksaci , Ali; Ould-Saïd , Elias
2009-01-01
On the robust nonparametric regression estimation for a functional regressor correspondance: Corresponding author. (Ould-Said, Elias) (Azzedine, Nadjia) (Laksaci, Ali) (Ould-Said, Elias) Departement de Mathematiques--> , Univ. Djillali Liabes--> , BP 89--> , 22000 Sidi Bel Abbes--> - ALGERIA (Azzedine, Nadjia) Departement de Mathema...
A general approach to posterior contraction in nonparametric inverse problems
Knapik, Bartek; Salomond, Jean Bernard
In this paper, we propose a general method to derive an upper bound for the contraction rate of the posterior distribution for nonparametric inverse problems. We present a general theorem that allows us to derive contraction rates for the parameter of interest from contraction rates of the related
Non-parametric analysis of production efficiency of poultry egg ...
African Journals Online (AJOL)
Non-parametric analysis of production efficiency of poultry egg farmers in Delta ... analysis of factors affecting the output of poultry farmers showed that stock ... should be put in place for farmers to learn the best farm practices carried out on the ...
Meta-analysis of genome-wide linkage studies in BMI and obesity.
Saunders, Catherine L; Chiodini, Benedetta D; Sham, Pak; Lewis, Cathryn M; Abkevich, Victor; Adeyemo, Adebowale A; de Andrade, Mariza; Arya, Rector; Berenson, Gerald S; Blangero, John; Boehnke, Michael; Borecki, Ingrid B; Chagnon, Yvon C; Chen, Wei; Comuzzie, Anthony G; Deng, Hong-Wen; Duggirala, Ravindranath; Feitosa, Mary F; Froguel, Philippe; Hanson, Robert L; Hebebrand, Johannes; Huezo-Dias, Patricia; Kissebah, Ahmed H; Li, Weidong; Luke, Amy; Martin, Lisa J; Nash, Matthew; Ohman, Miina; Palmer, Lyle J; Peltonen, Leena; Perola, Markus; Price, R Arlen; Redline, Susan; Srinivasan, Sathanur R; Stern, Michael P; Stone, Steven; Stringham, Heather; Turner, Stephen; Wijmenga, Cisca; Collier, David A
2007-09-01
The objective was to provide an overall assessment of genetic linkage data of BMI and BMI-defined obesity using a nonparametric genome scan meta-analysis. We identified 37 published studies containing data on over 31,000 individuals from more than >10,000 families and obtained genome-wide logarithm of the odds (LOD) scores, non-parametric linkage (NPL) scores, or maximum likelihood scores (MLS). BMI was analyzed in a pooled set of all studies, as a subgroup of 10 studies that used BMI-defined obesity, and for subgroups ascertained through type 2 diabetes, hypertension, or subjects of European ancestry. Bins at chromosome 13q13.2- q33.1, 12q23-q24.3 achieved suggestive evidence of linkage to BMI in the pooled analysis and samples ascertained for hypertension. Nominal evidence of linkage to these regions and suggestive evidence for 11q13.3-22.3 were also observed for BMI-defined obesity. The FTO obesity gene locus at 16q12.2 also showed nominal evidence for linkage. However, overall distribution of summed rank p values <0.05 is not different from that expected by chance. The strongest evidence was obtained in the families ascertained for hypertension at 9q31.1-qter and 12p11.21-q23 (p < 0.01). Despite having substantial statistical power, we did not unequivocally implicate specific loci for BMI or obesity. This may be because genes influencing adiposity are of very small effect, with substantial genetic heterogeneity and variable dependence on environmental factors. However, the observation that the FTO gene maps to one of the highest ranking bins for obesity is interesting and, while not a validation of this approach, indicates that other potential loci identified in this study should be investigated further.
Wei, Jiawei; Carroll, Raymond J.; Maity, Arnab
2011-01-01
We consider the problem of testing for a constant nonparametric effect in a general semi-parametric regression model when there is the potential for interaction between the parametrically and nonparametrically modeled variables. The work
Genetic recombinational and physical linkage analyses on slash pine
Rob Doudrick
1996-01-01
Slash pine is native to the southeastern USA, but is commercially valuable world-wide as a timber-,fiber- and resin-producing species. Breeding objectives emphasize selection for fusiform rust disease resistance. Identification of markers linked to genetic factors conditioning specificity should expand our knowledge of disease development. Towards this end, random...
GLOBEC Northeast Pacific Analyses: Linkages Between Cetaceans and Oceanographic Features
National Research Council Canada - National Science Library
Tynan, Cynthia
2004-01-01
...., sea surface salinity, SST, thermocline depth, halocline depth, chlorophyll maximum, distance to the center of the equatorward jet, distance to the upwelling front, and acoustic backscatter at 38, 120, 200 and 420 kHz...
Curceac, S.; Ternynck, C.; Ouarda, T.
2015-12-01
Over the past decades, a substantial amount of research has been conducted to model and forecast climatic variables. In this study, Nonparametric Functional Data Analysis (NPFDA) methods are applied to forecast air temperature and wind speed time series in Abu Dhabi, UAE. The dataset consists of hourly measurements recorded for a period of 29 years, 1982-2010. The novelty of the Functional Data Analysis approach is in expressing the data as curves. In the present work, the focus is on daily forecasting and the functional observations (curves) express the daily measurements of the above mentioned variables. We apply a non-linear regression model with a functional non-parametric kernel estimator. The computation of the estimator is performed using an asymmetrical quadratic kernel function for local weighting based on the bandwidth obtained by a cross validation procedure. The proximities between functional objects are calculated by families of semi-metrics based on derivatives and Functional Principal Component Analysis (FPCA). Additionally, functional conditional mode and functional conditional median estimators are applied and the advantages of combining their results are analysed. A different approach employs a SARIMA model selected according to the minimum Akaike (AIC) and Bayessian (BIC) Information Criteria and based on the residuals of the model. The performance of the models is assessed by calculating error indices such as the root mean square error (RMSE), relative RMSE, BIAS and relative BIAS. The results indicate that the NPFDA models provide more accurate forecasts than the SARIMA models. Key words: Nonparametric functional data analysis, SARIMA, time series forecast, air temperature, wind speed
Wang, Yuanjia; Garcia, Tanya P; Ma, Yanyuan
2012-01-01
This work presents methods for estimating genotype-specific distributions from genetic epidemiology studies where the event times are subject to right censoring, the genotypes are not directly observed, and the data arise from a mixture of scientifically meaningful subpopulations. Examples of such studies include kin-cohort studies and quantitative trait locus (QTL) studies. Current methods for analyzing censored mixture data include two types of nonparametric maximum likelihood estimators (NPMLEs) which do not make parametric assumptions on the genotype-specific density functions. Although both NPMLEs are commonly used, we show that one is inefficient and the other inconsistent. To overcome these deficiencies, we propose three classes of consistent nonparametric estimators which do not assume parametric density models and are easy to implement. They are based on the inverse probability weighting (IPW), augmented IPW (AIPW), and nonparametric imputation (IMP). The AIPW achieves the efficiency bound without additional modeling assumptions. Extensive simulation experiments demonstrate satisfactory performance of these estimators even when the data are heavily censored. We apply these estimators to the Cooperative Huntington's Observational Research Trial (COHORT), and provide age-specific estimates of the effect of mutation in the Huntington gene on mortality using a sample of family members. The close approximation of the estimated non-carrier survival rates to that of the U.S. population indicates small ascertainment bias in the COHORT family sample. Our analyses underscore an elevated risk of death in Huntington gene mutation carriers compared to non-carriers for a wide age range, and suggest that the mutation equally affects survival rates in both genders. The estimated survival rates are useful in genetic counseling for providing guidelines on interpreting the risk of death associated with a positive genetic testing, and in facilitating future subjects at risk
International Nuclear Information System (INIS)
McIntee, Erin; Viglino, Emilie; Rinke, Caitlin; Kumor, Stephanie; Ni Liqiang; Sigman, Michael E.
2010-01-01
Laser-induced breakdown spectroscopy (LIBS) has been investigated for the discrimination of automobile paint samples. Paint samples from automobiles of different makes, models, and years were collected and separated into sets based on the color, presence or absence of effect pigments and the number of paint layers. Twelve LIBS spectra were obtained for each paint sample, each an average of a five single shot 'drill down' spectra from consecutive laser ablations in the same spot on the sample. Analyses by a nonparametric permutation test and a parametric Wald test were performed to determine the extent of discrimination within each set of paint samples. The discrimination power and Type I error were assessed for each data analysis method. Conversion of the spectral intensity to a log-scale (base 10) resulted in a higher overall discrimination power while observing the same significance level. Working on the log-scale, the nonparametric permutation tests gave an overall 89.83% discrimination power with a size of Type I error being 4.44% at the nominal significance level of 5%. White paint samples, as a group, were the most difficult to differentiate with the power being only 86.56% followed by 95.83% for black paint samples. Parametric analysis of the data set produced lower discrimination (85.17%) with 3.33% Type I errors, which is not recommended for both theoretical and practical considerations. The nonparametric testing method is applicable across many analytical comparisons, with the specific application described here being the pairwise comparison of automotive paint samples.
Nonparametric Regression Estimation for Multivariate Null Recurrent Processes
Directory of Open Access Journals (Sweden)
Biqing Cai
2015-04-01
Full Text Available This paper discusses nonparametric kernel regression with the regressor being a \\(d\\-dimensional \\(\\beta\\-null recurrent process in presence of conditional heteroscedasticity. We show that the mean function estimator is consistent with convergence rate \\(\\sqrt{n(Th^{d}}\\, where \\(n(T\\ is the number of regenerations for a \\(\\beta\\-null recurrent process and the limiting distribution (with proper normalization is normal. Furthermore, we show that the two-step estimator for the volatility function is consistent. The finite sample performance of the estimate is quite reasonable when the leave-one-out cross validation method is used for bandwidth selection. We apply the proposed method to study the relationship of Federal funds rate with 3-month and 5-year T-bill rates and discover the existence of nonlinearity of the relationship. Furthermore, the in-sample and out-of-sample performance of the nonparametric model is far better than the linear model.
Nonparametric instrumental regression with non-convex constraints
International Nuclear Information System (INIS)
Grasmair, M; Scherzer, O; Vanhems, A
2013-01-01
This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. A classical example in microeconomics considers the consumer demand function as a function of the price of goods and the income, both variables often considered as endogenous. In this framework, the economic theory also imposes shape restrictions on the demand function, such as integrability conditions. Motivated by this illustration in microeconomics, we study an estimator of a nonparametric constrained regression function using instrumental variables by means of Tikhonov regularization. We derive rates of convergence for the regularized model both in a deterministic and stochastic setting under the assumption that the true regression function satisfies a projected source condition including, because of the non-convexity of the imposed constraints, an additional smallness condition. (paper)
Nonparametric instrumental regression with non-convex constraints
Grasmair, M.; Scherzer, O.; Vanhems, A.
2013-03-01
This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. A classical example in microeconomics considers the consumer demand function as a function of the price of goods and the income, both variables often considered as endogenous. In this framework, the economic theory also imposes shape restrictions on the demand function, such as integrability conditions. Motivated by this illustration in microeconomics, we study an estimator of a nonparametric constrained regression function using instrumental variables by means of Tikhonov regularization. We derive rates of convergence for the regularized model both in a deterministic and stochastic setting under the assumption that the true regression function satisfies a projected source condition including, because of the non-convexity of the imposed constraints, an additional smallness condition.
Comparing nonparametric Bayesian tree priors for clonal reconstruction of tumors.
Deshwar, Amit G; Vembu, Shankar; Morris, Quaid
2015-01-01
Statistical machine learning methods, especially nonparametric Bayesian methods, have become increasingly popular to infer clonal population structure of tumors. Here we describe the treeCRP, an extension of the Chinese restaurant process (CRP), a popular construction used in nonparametric mixture models, to infer the phylogeny and genotype of major subclonal lineages represented in the population of cancer cells. We also propose new split-merge updates tailored to the subclonal reconstruction problem that improve the mixing time of Markov chains. In comparisons with the tree-structured stick breaking prior used in PhyloSub, we demonstrate superior mixing and running time using the treeCRP with our new split-merge procedures. We also show that given the same number of samples, TSSB and treeCRP have similar ability to recover the subclonal structure of a tumor…
Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering
Directory of Open Access Journals (Sweden)
Xin Tian
2017-06-01
Full Text Available We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online. Clustering is introduced for dictionary learning. A set of dictionaries could be generated, and each dictionary is used for one cluster’s sparse coding. In this way, the signals in one cluster could be well represented by their corresponding dictionaries. A nonparametric Bayesian dictionary learning method is used to learn the dictionaries, which naturally infers an appropriate dictionary size for each cluster. A uniform quantizer and an adaptive arithmetic coding algorithm are adopted to code the sparse coefficients. With comparisons to other state-of-the art approaches, the effectiveness of the proposed method could be validated in the experiments.
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
2012-01-01
by investigating the relationship between the elasticity of scale and the farm size. We use a balanced panel data set of 371~specialised crop farms for the years 2004-2007. A non-parametric specification test shows that neither the Cobb-Douglas function nor the Translog function are consistent with the "true......Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify a functional form of the production function of which the Cobb...... parameter estimates, but also in biased measures which are derived from the parameters, such as elasticities. Therefore, we propose to use non-parametric econometric methods. First, these can be applied to verify the functional form used in parametric production analysis. Second, they can be directly used...
Nonparametric Bayesian models through probit stick-breaking processes.
Rodríguez, Abel; Dunson, David B
2011-03-01
We describe a novel class of Bayesian nonparametric priors based on stick-breaking constructions where the weights of the process are constructed as probit transformations of normal random variables. We show that these priors are extremely flexible, allowing us to generate a great variety of models while preserving computational simplicity. Particular emphasis is placed on the construction of rich temporal and spatial processes, which are applied to two problems in finance and ecology.
Exact nonparametric inference for detection of nonlinear determinism
Luo, Xiaodong; Zhang, Jie; Small, Michael; Moroz, Irene
2005-01-01
We propose an exact nonparametric inference scheme for the detection of nonlinear determinism. The essential fact utilized in our scheme is that, for a linear stochastic process with jointly symmetric innovations, its ordinary least square (OLS) linear prediction error is symmetric about zero. Based on this viewpoint, a class of linear signed rank statistics, e.g. the Wilcoxon signed rank statistic, can be derived with the known null distributions from the prediction error. Thus one of the ad...
Non-parametric estimation of the individual's utility map
Noguchi, Takao; Sanborn, Adam N.; Stewart, Neil
2013-01-01
Models of risky choice have attracted much attention in behavioural economics. Previous research has repeatedly demonstrated that individuals' choices are not well explained by expected utility theory, and a number of alternative models have been examined using carefully selected sets of choice alternatives. The model performance however, can depend on which choice alternatives are being tested. Here we develop a non-parametric method for estimating the utility map over the wide range of choi...
Nonparametric Efficiency Testing of Asian Stock Markets Using Weekly Data
CORNELIS A. LOS
2004-01-01
The efficiency of speculative markets, as represented by Fama's 1970 fair game model, is tested on weekly price index data of six Asian stock markets - Hong Kong, Indonesia, Malaysia, Singapore, Taiwan and Thailand - using Sherry's (1992) non-parametric methods. These scientific testing methods were originally developed to analyze the information processing efficiency of nervous systems. In particular, the stationarity and independence of the price innovations are tested over ten years, from ...
Investigation of MLE in nonparametric estimation methods of reliability function
International Nuclear Information System (INIS)
Ahn, Kwang Won; Kim, Yoon Ik; Chung, Chang Hyun; Kim, Kil Yoo
2001-01-01
There have been lots of trials to estimate a reliability function. In the ESReDA 20 th seminar, a new method in nonparametric way was proposed. The major point of that paper is how to use censored data efficiently. Generally there are three kinds of approach to estimate a reliability function in nonparametric way, i.e., Reduced Sample Method, Actuarial Method and Product-Limit (PL) Method. The above three methods have some limits. So we suggest an advanced method that reflects censored information more efficiently. In many instances there will be a unique maximum likelihood estimator (MLE) of an unknown parameter, and often it may be obtained by the process of differentiation. It is well known that the three methods generally used to estimate a reliability function in nonparametric way have maximum likelihood estimators that are uniquely exist. So, MLE of the new method is derived in this study. The procedure to calculate a MLE is similar just like that of PL-estimator. The difference of the two is that in the new method, the mass (or weight) of each has an influence of the others but the mass in PL-estimator not
Linkages between biodiversity attributes and ecosystem services: A systematic review
Harrison, P.A.; Berry, P.M.; Simpson, G.; Haslett, J.R.; Blicharska, M.; Bucur, M.; Dunford, R.; Egoh, B.; Garcia-llorente, M.; Geamănă, N.; Geertsema, W.; Lommelen, E.; Meiresonne, L.; Turkelboom, F.
2014-01-01
A systematic literature review was undertaken to analyse the linkages between different biodiversity attributes and 11 ecosystem services. The majority of relationships between attributes and ecosystem services cited in the 530 studies were positive. For example, the services of water quality
Genetic linkage map of cowpea ( Vigna unguiculata (L.) Walp) using ...
African Journals Online (AJOL)
Genetic linkage maps provide a genomic framework for quantitative trait loci identification applied in marker assisted selection breeding in crops with limited resources. It serves as a powerful tool to breeders for analysing the mode of inheritance of genes of interest and monitoring of the transmission of target genes from ...
The western arctic linkage experiment (WALE): overview and synthesis
A.D. McGuire; J. Walsh; J.S. Kimball; J.S. Clein; S.E. Euskirdhen; S. Drobot; U.C. Herzfeld; J. Maslanik; R.B. Lammers; M.A. Rawlins; C.J. Vorosmarty; T.S. Rupp; W. Wu; M. Calef
2008-01-01
The primary goal of the Western Arctic Linkage Experiment (WALE) was to better understand uncertainties of simulated hydrologic and ecosystem dynamics of the western Arctic in the context of 1) uncertainties in the data available to drive the models and 2) different approaches to simulating regional hydrology and ecosystem dynamics. Analyses of datasets on climate...
Linkages over Time between Adolescents' Relationships with Parents and Friends
De Goede, Irene H. A.; Branje, Susan J. T.; Delsing, Marc J. M. H.; Meeus, Wim H. J.
2009-01-01
This 5-wave longitudinal study examines linkages over time between adolescents' perceptions of relationships with parents and friends with respect to support, negative interaction, and power. A total of 575 early adolescents (54.1% boys) and 337 middle adolescents (43.3% boys) participated. Path analyses mainly showed bidirectional associations…
Linkage disequilibrium and demographic history of the isolated population of the Faroe Islands
DEFF Research Database (Denmark)
Jorgensen, Tove H; Degn, Birte; Wang, August G
2002-01-01
The isolated population of the Faroe Islands has a history of recent expansion after being limited to a small size for centuries. Such an isolated population may be ideal for linkage disequilibrium mapping of disease genes if linkage disequilibrium (LD) extends over large regions. Analyses of 18 ...
Linkage of PRA models. Phase 1, Results
Energy Technology Data Exchange (ETDEWEB)
Smith, C.L.; Knudsen, J.K.; Kelly, D.L.
1995-12-01
The goal of the Phase I work of the ``Linkage of PRA Models`` project was to postulate methods of providing guidance for US Nuclear Regulator Commission (NRC) personnel on the selection and usage of probabilistic risk assessment (PRA) models that are best suited to the analysis they are performing. In particular, methods and associated features are provided for (a) the selection of an appropriate PRA model for a particular analysis, (b) complementary evaluation tools for the analysis, and (c) a PRA model cross-referencing method. As part of this work, three areas adjoining ``linking`` analyses to PRA models were investigated: (a) the PRA models that are currently available, (b) the various types of analyses that are performed within the NRC, and (c) the difficulty in trying to provide a ``generic`` classification scheme to groups plants based upon a particular plant attribute.
Linkage of PRA models. Phase 1, Results
International Nuclear Information System (INIS)
Smith, C.L.; Knudsen, J.K.; Kelly, D.L.
1995-12-01
The goal of the Phase I work of the ''Linkage of PRA Models'' project was to postulate methods of providing guidance for US Nuclear Regulator Commission (NRC) personnel on the selection and usage of probabilistic risk assessment (PRA) models that are best suited to the analysis they are performing. In particular, methods and associated features are provided for (a) the selection of an appropriate PRA model for a particular analysis, (b) complementary evaluation tools for the analysis, and (c) a PRA model cross-referencing method. As part of this work, three areas adjoining ''linking'' analyses to PRA models were investigated: (a) the PRA models that are currently available, (b) the various types of analyses that are performed within the NRC, and (c) the difficulty in trying to provide a ''generic'' classification scheme to groups plants based upon a particular plant attribute
STAKEHOLDER LINKAGES FOR SUSTAINABLE LAND ...
African Journals Online (AJOL)
Osondu
Key words: Stakeholders; farmer-expert linkages; resource management; Ethiopia. Introduction ... decentralized democratic decision making processes and thus ..... district offices within the given time limits. They were often .... -less willing and less ready to hearing weaker performance reports (expect more success with ...
Mapping autism risk loci using genetic linkage and chromosomal rearrangements
Szatmari, Peter; Paterson, Andrew; Zwaigenbaum, Lonnie; Roberts, Wendy; Brian, Jessica; Liu, Xiao-Qing; Vincent, John; Skaug, Jennifer; Thompson, Ann; Senman, Lili; Feuk, Lars; Qian, Cheng; Bryson, Susan; Jones, Marshall; Marshall, Christian; Scherer, Stephen; Vieland, Veronica; Bartlett, Christopher; Mangin, La Vonne; Goedken, Rhinda; Segre, Alberto; Pericak-Vance, Margaret; Cuccaro, Michael; Gilbert, John; Wright, Harry; Abramson, Ruth; Betancur, Catalina; Bourgeron, Thomas; Gillberg, Christopher; Leboyer, Marion; Buxbaum, Joseph; Davis, Kenneth; Hollander, Eric; Silverman, Jeremy; Hallmayer, Joachim; Lotspeich, Linda; Sutcliffe, James; Haines, Jonathan; Folstein, Susan; Piven, Joseph; Wassink, Thomas; Sheffield, Val; Geschwind, Daniel; Bucan, Maja; Brown, Ted; Cantor, Rita; Constantino, John; Gilliam, Conrad; Herbert, Martha; Lajonchere, Clara; Ledbetter, David; Lese-Martin, Christa; Miller, Janet; Nelson, Stan; Samango-Sprouse, Carol; Spence, Sarah; State, Matthew; Tanzi, Rudolph; Coon, Hilary; Dawson, Geraldine; Devlin, Bernie; Estes, Annette; Flodman, Pamela; Klei, Lambertus; Mcmahon, William; Minshew, Nancy; Munson, Jeff; Korvatska, Elena; Rodier, Patricia; Schellenberg, Gerard; Smith, Moyra; Spence, Anne; Stodgell, Chris; Tepper, Ping Guo; Wijsman, Ellen; Yu, Chang-En; Rogé, Bernadette; Mantoulan, Carine; Wittemeyer, Kerstin; Poustka, Annemarie; Felder, Bärbel; Klauck, Sabine; Schuster, Claudia; Poustka, Fritz; Bölte, Sven; Feineis-Matthews, Sabine; Herbrecht, Evelyn; Schmötzer, Gabi; Tsiantis, John; Papanikolaou, Katerina; Maestrini, Elena; Bacchelli, Elena; Blasi, Francesca; Carone, Simona; Toma, Claudio; Van Engeland, Herman; De Jonge, Maretha; Kemner, Chantal; Koop, Frederieke; Langemeijer, Marjolein; Hijmans, Channa; Staal, Wouter; Baird, Gillian; Bolton, Patrick; Rutter, Michael; Weisblatt, Emma; Green, Jonathan; Aldred, Catherine; Wilkinson, Julie-Anne; Pickles, Andrew; Le Couteur, Ann; Berney, Tom; Mcconachie, Helen; Bailey, Anthony; Francis, Kostas; Honeyman, Gemma; Hutchinson, Aislinn; Parr, Jeremy; Wallace, Simon; Monaco, Anthony; Barnby, Gabrielle; Kobayashi, Kazuhiro; Lamb, Janine; Sousa, Ines; Sykes, Nuala; Cook, Edwin; Guter, Stephen; Leventhal, Bennett; Salt, Jeff; Lord, Catherine; Corsello, Christina; Hus, Vanessa; Weeks, Daniel; Volkmar, Fred; Tauber, Maïté; Fombonne, Eric; Shih, Andy; Meyer, Kacie
2007-01-01
Autism spectrum disorders (ASD) are common, heritable neurodevelopmental conditions. The genetic architecture of ASD is complex, requiring large samples to overcome heterogeneity. Here we broaden coverage and sample size relative to other studies of ASD by using Affymetrix 10K single nucleotide polymorphism (SNP) arrays and 1168 families with ≥ 2 affected individuals to perform the largest linkage scan to date, while also analyzing copy number variation (CNV) in these families. Linkage and CNV analyses implicate chromosome 11p12-p13 and neurexins, respectively, amongst other candidate loci. Neurexins team with previously-implicated neuroligins for glutamatergic synaptogenesis, highlighting glutamate-related genes as promising candidates for ASD. PMID:17322880
Exploitation of linkage learning in evolutionary algorithms
Chen, Ying-ping
2010-01-01
The exploitation of linkage learning is enhancing the performance of evolutionary algorithms. This monograph examines recent progress in linkage learning, with a series of focused technical chapters that cover developments and trends in the field.
Efficient Record Linkage Algorithms Using Complete Linkage Clustering.
Mamun, Abdullah-Al; Aseltine, Robert; Rajasekaran, Sanguthevar
2016-01-01
Data from different agencies share data of the same individuals. Linking these datasets to identify all the records belonging to the same individuals is a crucial and challenging problem, especially given the large volumes of data. A large number of available algorithms for record linkage are prone to either time inefficiency or low-accuracy in finding matches and non-matches among the records. In this paper we propose efficient as well as reliable sequential and parallel algorithms for the record linkage problem employing hierarchical clustering methods. We employ complete linkage hierarchical clustering algorithms to address this problem. In addition to hierarchical clustering, we also use two other techniques: elimination of duplicate records and blocking. Our algorithms use sorting as a sub-routine to identify identical copies of records. We have tested our algorithms on datasets with millions of synthetic records. Experimental results show that our algorithms achieve nearly 100% accuracy. Parallel implementations achieve almost linear speedups. Time complexities of these algorithms do not exceed those of previous best-known algorithms. Our proposed algorithms outperform previous best-known algorithms in terms of accuracy consuming reasonable run times.
Two-locus linkage analysis in multiple sclerosis (MS)
Energy Technology Data Exchange (ETDEWEB)
Tienari, P.J. (National Public Health Institute, Helsinki (Finland) Univ. of Helsinki (Finland)); Terwilliger, J.D.; Ott, J. (Columbia Univ., New York (United States)); Palo, J. (Univ. of Helsinki (Finland)); Peltonen, L. (National Public Health Institute, Helsinki (Finland))
1994-01-15
One of the major challenges in genetic linkage analyses is the study of complex diseases. The authors demonstrate here the use of two-locus linkage analysis in multiple sclerosis (MS), a multifactorial disease with a complex mode of inheritance. In a set of Finnish multiplex families, they have previously found evidence for linkage between MS susceptibility and two independent loci, the myelin basic protein gene (MBP) on chromosome 18 and the HLA complex on chromosome 6. This set of families provides a unique opportunity to perform linkage analysis conditional on two loci contributing to the disease. In the two-trait-locus/two-marker-locus analysis, the presence of another disease locus is parametrized and the analysis more appropriately treats information from the unaffected family member than single-disease-locus analysis. As exemplified here in MS, the two-locus analysis can be a powerful method for investigating susceptibility loci in complex traits, best suited for analysis of specific candidate genes, or for situations in which preliminary evidence for linkage already exists or is suggested. 41 refs., 6 tabs.
A Nonparametric Bayesian Approach For Emission Tomography Reconstruction
International Nuclear Information System (INIS)
Barat, Eric; Dautremer, Thomas
2007-01-01
We introduce a PET reconstruction algorithm following a nonparametric Bayesian (NPB) approach. In contrast with Expectation Maximization (EM), the proposed technique does not rely on any space discretization. Namely, the activity distribution--normalized emission intensity of the spatial poisson process--is considered as a spatial probability density and observations are the projections of random emissions whose distribution has to be estimated. This approach is nonparametric in the sense that the quantity of interest belongs to the set of probability measures on R k (for reconstruction in k-dimensions) and it is Bayesian in the sense that we define a prior directly on this spatial measure. In this context, we propose to model the nonparametric probability density as an infinite mixture of multivariate normal distributions. As a prior for this mixture we consider a Dirichlet Process Mixture (DPM) with a Normal-Inverse Wishart (NIW) model as base distribution of the Dirichlet Process. As in EM-family reconstruction, we use a data augmentation scheme where the set of hidden variables are the emission locations for each observed line of response in the continuous object space. Thanks to the data augmentation, we propose a Markov Chain Monte Carlo (MCMC) algorithm (Gibbs sampler) which is able to generate draws from the posterior distribution of the spatial intensity. A difference with EM is that one step of the Gibbs sampler corresponds to the generation of emission locations while only the expected number of emissions per pixel/voxel is used in EM. Another key difference is that the estimated spatial intensity is a continuous function such that there is no need to compute a projection matrix. Finally, draws from the intensity posterior distribution allow the estimation of posterior functionnals like the variance or confidence intervals. Results are presented for simulated data based on a 2D brain phantom and compared to Bayesian MAP-EM
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify the functional form of the production function. Most often, the Cobb...... results—including measures that are of interest of applied economists, such as elasticities. Therefore, we propose to use nonparametric econometric methods. First, they can be applied to verify the functional form used in parametric estimations of production functions. Second, they can be directly used...
STATCAT, Statistical Analysis of Parametric and Non-Parametric Data
International Nuclear Information System (INIS)
David, Hugh
1990-01-01
1 - Description of program or function: A suite of 26 programs designed to facilitate the appropriate statistical analysis and data handling of parametric and non-parametric data, using classical and modern univariate and multivariate methods. 2 - Method of solution: Data is read entry by entry, using a choice of input formats, and the resultant data bank is checked for out-of- range, rare, extreme or missing data. The completed STATCAT data bank can be treated by a variety of descriptive and inferential statistical methods, and modified, using other standard programs as required
Panel data nonparametric estimation of production risk and risk preferences
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
approaches for obtaining firm-specific measures of risk attitudes. We found that Polish dairy farmers are risk averse regarding production risk and price uncertainty. According to our results, Polish dairy farmers perceive the production risk as being more significant than the risk related to output price......We apply nonparametric panel data kernel regression to investigate production risk, out-put price uncertainty, and risk attitudes of Polish dairy farms based on a firm-level unbalanced panel data set that covers the period 2004–2010. We compare different model specifications and different...
Digital spectral analysis parametric, non-parametric and advanced methods
Castanié, Francis
2013-01-01
Digital Spectral Analysis provides a single source that offers complete coverage of the spectral analysis domain. This self-contained work includes details on advanced topics that are usually presented in scattered sources throughout the literature.The theoretical principles necessary for the understanding of spectral analysis are discussed in the first four chapters: fundamentals, digital signal processing, estimation in spectral analysis, and time-series models.An entire chapter is devoted to the non-parametric methods most widely used in industry.High resolution methods a
A Bayesian nonparametric approach to causal inference on quantiles.
Xu, Dandan; Daniels, Michael J; Winterstein, Almut G
2018-02-25
We propose a Bayesian nonparametric approach (BNP) for causal inference on quantiles in the presence of many confounders. In particular, we define relevant causal quantities and specify BNP models to avoid bias from restrictive parametric assumptions. We first use Bayesian additive regression trees (BART) to model the propensity score and then construct the distribution of potential outcomes given the propensity score using a Dirichlet process mixture (DPM) of normals model. We thoroughly evaluate the operating characteristics of our approach and compare it to Bayesian and frequentist competitors. We use our approach to answer an important clinical question involving acute kidney injury using electronic health records. © 2018, The International Biometric Society.
Categorical and nonparametric data analysis choosing the best statistical technique
Nussbaum, E Michael
2014-01-01
Featuring in-depth coverage of categorical and nonparametric statistics, this book provides a conceptual framework for choosing the most appropriate type of test in various research scenarios. Class tested at the University of Nevada, the book's clear explanations of the underlying assumptions, computer simulations, and Exploring the Concept boxes help reduce reader anxiety. Problems inspired by actual studies provide meaningful illustrations of the techniques. The underlying assumptions of each test and the factors that impact validity and statistical power are reviewed so readers can explain
Nonparametric statistics a step-by-step approach
Corder, Gregory W
2014-01-01
"…a very useful resource for courses in nonparametric statistics in which the emphasis is on applications rather than on theory. It also deserves a place in libraries of all institutions where introductory statistics courses are taught."" -CHOICE This Second Edition presents a practical and understandable approach that enhances and expands the statistical toolset for readers. This book includes: New coverage of the sign test and the Kolmogorov-Smirnov two-sample test in an effort to offer a logical and natural progression to statistical powerSPSS® (Version 21) software and updated screen ca
Evaluation of Nonparametric Probabilistic Forecasts of Wind Power
DEFF Research Database (Denmark)
Pinson, Pierre; Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg, orlov 31.07.2008
Predictions of wind power production for horizons up to 48-72 hour ahead comprise a highly valuable input to the methods for the daily management or trading of wind generation. Today, users of wind power predictions are not only provided with point predictions, which are estimates of the most...... likely outcome for each look-ahead time, but also with uncertainty estimates given by probabilistic forecasts. In order to avoid assumptions on the shape of predictive distributions, these probabilistic predictions are produced from nonparametric methods, and then take the form of a single or a set...
Estimation of Stochastic Volatility Models by Nonparametric Filtering
DEFF Research Database (Denmark)
Kanaya, Shin; Kristensen, Dennis
2016-01-01
/estimated volatility process replacing the latent process. Our estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps and market microstructure noise. The resulting estimators of the stochastic volatility model will carry additional biases...... and variances due to the first-step estimation, but under regularity conditions we show that these vanish asymptotically and our estimators inherit the asymptotic properties of the infeasible estimators based on observations of the volatility process. A simulation study examines the finite-sample properties...
Faraway, Julian J
2005-01-01
Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway''s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author''s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the ...
Genomic outlier profile analysis: mixture models, null hypotheses, and nonparametric estimation.
Ghosh, Debashis; Chinnaiyan, Arul M
2009-01-01
In most analyses of large-scale genomic data sets, differential expression analysis is typically assessed by testing for differences in the mean of the distributions between 2 groups. A recent finding by Tomlins and others (2005) is of a different type of pattern of differential expression in which a fraction of samples in one group have overexpression relative to samples in the other group. In this work, we describe a general mixture model framework for the assessment of this type of expression, called outlier profile analysis. We start by considering the single-gene situation and establishing results on identifiability. We propose 2 nonparametric estimation procedures that have natural links to familiar multiple testing procedures. We then develop multivariate extensions of this methodology to handle genome-wide measurements. The proposed methodologies are compared using simulation studies as well as data from a prostate cancer gene expression study.
Bayesian nonparametric dictionary learning for compressed sensing MRI.
Huang, Yue; Paisley, John; Lin, Qin; Ding, Xinghao; Fu, Xueyang; Zhang, Xiao-Ping
2014-12-01
We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled k -space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.
1st Conference of the International Society for Nonparametric Statistics
Lahiri, S; Politis, Dimitris
2014-01-01
This volume is composed of peer-reviewed papers that have developed from the First Conference of the International Society for NonParametric Statistics (ISNPS). This inaugural conference took place in Chalkidiki, Greece, June 15-19, 2012. It was organized with the co-sponsorship of the IMS, the ISI, and other organizations. M.G. Akritas, S.N. Lahiri, and D.N. Politis are the first executive committee members of ISNPS, and the editors of this volume. ISNPS has a distinguished Advisory Committee that includes Professors R.Beran, P.Bickel, R. Carroll, D. Cook, P. Hall, R. Johnson, B. Lindsay, E. Parzen, P. Robinson, M. Rosenblatt, G. Roussas, T. SubbaRao, and G. Wahba. The Charting Committee of ISNPS consists of more than 50 prominent researchers from all over the world. The chapters in this volume bring forth recent advances and trends in several areas of nonparametric statistics. In this way, the volume facilitates the exchange of research ideas, promotes collaboration among researchers from all over the wo...
A Bayesian nonparametric estimation of distributions and quantiles
International Nuclear Information System (INIS)
Poern, K.
1988-11-01
The report describes a Bayesian, nonparametric method for the estimation of a distribution function and its quantiles. The method, presupposing random sampling, is nonparametric, so the user has to specify a prior distribution on a space of distributions (and not on a parameter space). In the current application, where the method is used to estimate the uncertainty of a parametric calculational model, the Dirichlet prior distribution is to a large extent determined by the first batch of Monte Carlo-realizations. In this case the results of the estimation technique is very similar to the conventional empirical distribution function. The resulting posterior distribution is also Dirichlet, and thus facilitates the determination of probability (confidence) intervals at any given point in the space of interest. Another advantage is that also the posterior distribution of a specified quantitle can be derived and utilized to determine a probability interval for that quantile. The method was devised for use in the PROPER code package for uncertainty and sensitivity analysis. (orig.)
Genomic breeding value estimation using nonparametric additive regression models
Directory of Open Access Journals (Sweden)
Solberg Trygve
2009-01-01
Full Text Available Abstract Genomic selection refers to the use of genomewide dense markers for breeding value estimation and subsequently for selection. The main challenge of genomic breeding value estimation is the estimation of many effects from a limited number of observations. Bayesian methods have been proposed to successfully cope with these challenges. As an alternative class of models, non- and semiparametric models were recently introduced. The present study investigated the ability of nonparametric additive regression models to predict genomic breeding values. The genotypes were modelled for each marker or pair of flanking markers (i.e. the predictors separately. The nonparametric functions for the predictors were estimated simultaneously using additive model theory, applying a binomial kernel. The optimal degree of smoothing was determined by bootstrapping. A mutation-drift-balance simulation was carried out. The breeding values of the last generation (genotyped was predicted using data from the next last generation (genotyped and phenotyped. The results show moderate to high accuracies of the predicted breeding values. A determination of predictor specific degree of smoothing increased the accuracy.
Application of nonparametric statistics to material strength/reliability assessment
International Nuclear Information System (INIS)
Arai, Taketoshi
1992-01-01
An advanced material technology requires data base on a wide variety of material behavior which need to be established experimentally. It may often happen that experiments are practically limited in terms of reproducibility or a range of test parameters. Statistical methods can be applied to understanding uncertainties in such a quantitative manner as required from the reliability point of view. Statistical assessment involves determinations of a most probable value and the maximum and/or minimum value as one-sided or two-sided confidence limit. A scatter of test data can be approximated by a theoretical distribution only if the goodness of fit satisfies a test criterion. Alternatively, nonparametric statistics (NPS) or distribution-free statistics can be applied. Mathematical procedures by NPS are well established for dealing with most reliability problems. They handle only order statistics of a sample. Mathematical formulas and some applications to engineering assessments are described. They include confidence limits of median, population coverage of sample, required minimum number of a sample, and confidence limits of fracture probability. These applications demonstrate that a nonparametric statistical estimation is useful in logical decision making in the case a large uncertainty exists. (author)
Privacy preserving interactive record linkage (PPIRL).
Kum, Hye-Chung; Krishnamurthy, Ashok; Machanavajjhala, Ashwin; Reiter, Michael K; Ahalt, Stanley
2014-01-01
Record linkage to integrate uncoordinated databases is critical in biomedical research using Big Data. Balancing privacy protection against the need for high quality record linkage requires a human-machine hybrid system to safely manage uncertainty in the ever changing streams of chaotic Big Data. In the computer science literature, private record linkage is the most published area. It investigates how to apply a known linkage function safely when linking two tables. However, in practice, the linkage function is rarely known. Thus, there are many data linkage centers whose main role is to be the trusted third party to determine the linkage function manually and link data for research via a master population list for a designated region. Recently, a more flexible computerized third-party linkage platform, Secure Decoupled Linkage (SDLink), has been proposed based on: (1) decoupling data via encryption, (2) obfuscation via chaffing (adding fake data) and universe manipulation; and (3) minimum information disclosure via recoding. We synthesize this literature to formalize a new framework for privacy preserving interactive record linkage (PPIRL) with tractable privacy and utility properties and then analyze the literature using this framework. Human-based third-party linkage centers for privacy preserving record linkage are the accepted norm internationally. We find that a computer-based third-party platform that can precisely control the information disclosed at the micro level and allow frequent human interaction during the linkage process, is an effective human-machine hybrid system that significantly improves on the linkage center model both in terms of privacy and utility.
When to conduct probabilistic linkage vs. deterministic linkage? A simulation study.
Zhu, Ying; Matsuyama, Yutaka; Ohashi, Yasuo; Setoguchi, Soko
2015-08-01
When unique identifiers are unavailable, successful record linkage depends greatly on data quality and types of variables available. While probabilistic linkage theoretically captures more true matches than deterministic linkage by allowing imperfection in identifiers, studies have shown inconclusive results likely due to variations in data quality, implementation of linkage methodology and validation method. The simulation study aimed to understand data characteristics that affect the performance of probabilistic vs. deterministic linkage. We created ninety-six scenarios that represent real-life situations using non-unique identifiers. We systematically introduced a range of discriminative power, rate of missing and error, and file size to increase linkage patterns and difficulties. We assessed the performance difference of linkage methods using standard validity measures and computation time. Across scenarios, deterministic linkage showed advantage in PPV while probabilistic linkage showed advantage in sensitivity. Probabilistic linkage uniformly outperformed deterministic linkage as the former generated linkages with better trade-off between sensitivity and PPV regardless of data quality. However, with low rate of missing and error in data, deterministic linkage performed not significantly worse. The implementation of deterministic linkage in SAS took less than 1min, and probabilistic linkage took 2min to 2h depending on file size. Our simulation study demonstrated that the intrinsic rate of missing and error of linkage variables was key to choosing between linkage methods. In general, probabilistic linkage was a better choice, but for exceptionally good quality data (<5% error), deterministic linkage was a more resource efficient choice. Copyright © 2015 Elsevier Inc. All rights reserved.
CADDIS Volume 4. Data Analysis: PECBO Appendix - R Scripts for Non-Parametric Regressions
Script for computing nonparametric regression analysis. Overview of using scripts to infer environmental conditions from biological observations, statistically estimating species-environment relationships, statistical scripts.
Generative Temporal Modelling of Neuroimaging - Decomposition and Nonparametric Testing
DEFF Research Database (Denmark)
Hald, Ditte Høvenhoff
The goal of this thesis is to explore two improvements for functional magnetic resonance imaging (fMRI) analysis; namely our proposed decomposition method and an extension to the non-parametric testing framework. Analysis of fMRI allows researchers to investigate the functional processes...... of the brain, and provides insight into neuronal coupling during mental processes or tasks. The decomposition method is a Gaussian process-based independent components analysis (GPICA), which incorporates a temporal dependency in the sources. A hierarchical model specification is used, featuring both...... instantaneous and convolutive mixing, and the inferred temporal patterns. Spatial maps are seen to capture smooth and localized stimuli-related components, and often identifiable noise components. The implementation is freely available as a GUI/SPM plugin, and we recommend using GPICA as an additional tool when...
Nonparametric Estimation of Distributions in Random Effects Models
Hart, Jeffrey D.
2011-01-01
We propose using minimum distance to obtain nonparametric estimates of the distributions of components in random effects models. A main setting considered is equivalent to having a large number of small datasets whose locations, and perhaps scales, vary randomly, but which otherwise have a common distribution. Interest focuses on estimating the distribution that is common to all datasets, knowledge of which is crucial in multiple testing problems where a location/scale invariant test is applied to every small dataset. A detailed algorithm for computing minimum distance estimates is proposed, and the usefulness of our methodology is illustrated by a simulation study and an analysis of microarray data. Supplemental materials for the article, including R-code and a dataset, are available online. © 2011 American Statistical Association.
Prior processes and their applications nonparametric Bayesian estimation
Phadia, Eswar G
2016-01-01
This book presents a systematic and comprehensive treatment of various prior processes that have been developed over the past four decades for dealing with Bayesian approach to solving selected nonparametric inference problems. This revised edition has been substantially expanded to reflect the current interest in this area. After an overview of different prior processes, it examines the now pre-eminent Dirichlet process and its variants including hierarchical processes, then addresses new processes such as dependent Dirichlet, local Dirichlet, time-varying and spatial processes, all of which exploit the countable mixture representation of the Dirichlet process. It subsequently discusses various neutral to right type processes, including gamma and extended gamma, beta and beta-Stacy processes, and then describes the Chinese Restaurant, Indian Buffet and infinite gamma-Poisson processes, which prove to be very useful in areas such as machine learning, information retrieval and featural modeling. Tailfree and P...
Spurious Seasonality Detection: A Non-Parametric Test Proposal
Directory of Open Access Journals (Sweden)
Aurelio F. Bariviera
2018-01-01
Full Text Available This paper offers a general and comprehensive definition of the day-of-the-week effect. Using symbolic dynamics, we develop a unique test based on ordinal patterns in order to detect it. This test uncovers the fact that the so-called “day-of-the-week” effect is partly an artifact of the hidden correlation structure of the data. We present simulations based on artificial time series as well. While time series generated with long memory are prone to exhibit daily seasonality, pure white noise signals exhibit no pattern preference. Since ours is a non-parametric test, it requires no assumptions about the distribution of returns, so that it could be a practical alternative to conventional econometric tests. We also made an exhaustive application of the here-proposed technique to 83 stock indexes around the world. Finally, the paper highlights the relevance of symbolic analysis in economic time series studies.
Nonparametric autocovariance estimation from censored time series by Gaussian imputation.
Park, Jung Wook; Genton, Marc G; Ghosh, Sujit K
2009-02-01
One of the most frequently used methods to model the autocovariance function of a second-order stationary time series is to use the parametric framework of autoregressive and moving average models developed by Box and Jenkins. However, such parametric models, though very flexible, may not always be adequate to model autocovariance functions with sharp changes. Furthermore, if the data do not follow the parametric model and are censored at a certain value, the estimation results may not be reliable. We develop a Gaussian imputation method to estimate an autocovariance structure via nonparametric estimation of the autocovariance function in order to address both censoring and incorrect model specification. We demonstrate the effectiveness of the technique in terms of bias and efficiency with simulations under various rates of censoring and underlying models. We describe its application to a time series of silicon concentrations in the Arctic.
Nonparametric estimation of stochastic differential equations with sparse Gaussian processes.
García, Constantino A; Otero, Abraham; Félix, Paulo; Presedo, Jesús; Márquez, David G
2017-08-01
The application of stochastic differential equations (SDEs) to the analysis of temporal data has attracted increasing attention, due to their ability to describe complex dynamics with physically interpretable equations. In this paper, we introduce a nonparametric method for estimating the drift and diffusion terms of SDEs from a densely observed discrete time series. The use of Gaussian processes as priors permits working directly in a function-space view and thus the inference takes place directly in this space. To cope with the computational complexity that requires the use of Gaussian processes, a sparse Gaussian process approximation is provided. This approximation permits the efficient computation of predictions for the drift and diffusion terms by using a distribution over a small subset of pseudosamples. The proposed method has been validated using both simulated data and real data from economy and paleoclimatology. The application of the method to real data demonstrates its ability to capture the behavior of complex systems.
Debt and growth: A non-parametric approach
Brida, Juan Gabriel; Gómez, David Matesanz; Seijas, Maria Nela
2017-11-01
In this study, we explore the dynamic relationship between public debt and economic growth by using a non-parametric approach based on data symbolization and clustering methods. The study uses annual data of general government consolidated gross debt-to-GDP ratio and gross domestic product for sixteen countries between 1977 and 2015. Using symbolic sequences, we introduce a notion of distance between the dynamical paths of different countries. Then, a Minimal Spanning Tree and a Hierarchical Tree are constructed from time series to help detecting the existence of groups of countries sharing similar economic performance. The main finding of the study appears for the period 2008-2016 when several countries surpassed the 90% debt-to-GDP threshold. During this period, three groups (clubs) of countries are obtained: high, mid and low indebted countries, suggesting that the employed debt-to-GDP threshold drives economic dynamics for the selected countries.
Nonparametric estimation of benchmark doses in environmental risk assessment
Piegorsch, Walter W.; Xiong, Hui; Bhattacharya, Rabi N.; Lin, Lizhen
2013-01-01
Summary An important statistical objective in environmental risk analysis is estimation of minimum exposure levels, called benchmark doses (BMDs), that induce a pre-specified benchmark response in a dose-response experiment. In such settings, representations of the risk are traditionally based on a parametric dose-response model. It is a well-known concern, however, that if the chosen parametric form is misspecified, inaccurate and possibly unsafe low-dose inferences can result. We apply a nonparametric approach for calculating benchmark doses, based on an isotonic regression method for dose-response estimation with quantal-response data (Bhattacharya and Kong, 2007). We determine the large-sample properties of the estimator, develop bootstrap-based confidence limits on the BMDs, and explore the confidence limits’ small-sample properties via a short simulation study. An example from cancer risk assessment illustrates the calculations. PMID:23914133
Indoor Positioning Using Nonparametric Belief Propagation Based on Spanning Trees
Directory of Open Access Journals (Sweden)
Savic Vladimir
2010-01-01
Full Text Available Nonparametric belief propagation (NBP is one of the best-known methods for cooperative localization in sensor networks. It is capable of providing information about location estimation with appropriate uncertainty and to accommodate non-Gaussian distance measurement errors. However, the accuracy of NBP is questionable in loopy networks. Therefore, in this paper, we propose a novel approach, NBP based on spanning trees (NBP-ST created by breadth first search (BFS method. In addition, we propose a reliable indoor model based on obtained measurements in our lab. According to our simulation results, NBP-ST performs better than NBP in terms of accuracy and communication cost in the networks with high connectivity (i.e., highly loopy networks. Furthermore, the computational and communication costs are nearly constant with respect to the transmission radius. However, the drawbacks of proposed method are a little bit higher computational cost and poor performance in low-connected networks.
Bayesian estimates of linkage disequilibrium
Directory of Open Access Journals (Sweden)
Abad-Grau María M
2007-06-01
Full Text Available Abstract Background The maximum likelihood estimator of D' – a standard measure of linkage disequilibrium – is biased toward disequilibrium, and the bias is particularly evident in small samples and rare haplotypes. Results This paper proposes a Bayesian estimation of D' to address this problem. The reduction of the bias is achieved by using a prior distribution on the pair-wise associations between single nucleotide polymorphisms (SNPs that increases the likelihood of equilibrium with increasing physical distances between pairs of SNPs. We show how to compute the Bayesian estimate using a stochastic estimation based on MCMC methods, and also propose a numerical approximation to the Bayesian estimates that can be used to estimate patterns of LD in large datasets of SNPs. Conclusion Our Bayesian estimator of D' corrects the bias toward disequilibrium that affects the maximum likelihood estimator. A consequence of this feature is a more objective view about the extent of linkage disequilibrium in the human genome, and a more realistic number of tagging SNPs to fully exploit the power of genome wide association studies.
Exact nonparametric confidence bands for the survivor function.
Matthews, David
2013-10-12
A method to produce exact simultaneous confidence bands for the empirical cumulative distribution function that was first described by Owen, and subsequently corrected by Jager and Wellner, is the starting point for deriving exact nonparametric confidence bands for the survivor function of any positive random variable. We invert a nonparametric likelihood test of uniformity, constructed from the Kaplan-Meier estimator of the survivor function, to obtain simultaneous lower and upper bands for the function of interest with specified global confidence level. The method involves calculating a null distribution and associated critical value for each observed sample configuration. However, Noe recursions and the Van Wijngaarden-Decker-Brent root-finding algorithm provide the necessary tools for efficient computation of these exact bounds. Various aspects of the effect of right censoring on these exact bands are investigated, using as illustrations two observational studies of survival experience among non-Hodgkin's lymphoma patients and a much larger group of subjects with advanced lung cancer enrolled in trials within the North Central Cancer Treatment Group. Monte Carlo simulations confirm the merits of the proposed method of deriving simultaneous interval estimates of the survivor function across the entire range of the observed sample. This research was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada. It was begun while the author was visiting the Department of Statistics, University of Auckland, and completed during a subsequent sojourn at the Medical Research Council Biostatistics Unit in Cambridge. The support of both institutions, in addition to that of NSERC and the University of Waterloo, is greatly appreciated.
Hyperspectral image segmentation using a cooperative nonparametric approach
Taher, Akar; Chehdi, Kacem; Cariou, Claude
2013-10-01
In this paper a new unsupervised nonparametric cooperative and adaptive hyperspectral image segmentation approach is presented. The hyperspectral images are partitioned band by band in parallel and intermediate classification results are evaluated and fused, to get the final segmentation result. Two unsupervised nonparametric segmentation methods are used in parallel cooperation, namely the Fuzzy C-means (FCM) method, and the Linde-Buzo-Gray (LBG) algorithm, to segment each band of the image. The originality of the approach relies firstly on its local adaptation to the type of regions in an image (textured, non-textured), and secondly on the introduction of several levels of evaluation and validation of intermediate segmentation results before obtaining the final partitioning of the image. For the management of similar or conflicting results issued from the two classification methods, we gradually introduced various assessment steps that exploit the information of each spectral band and its adjacent bands, and finally the information of all the spectral bands. In our approach, the detected textured and non-textured regions are treated separately from feature extraction step, up to the final classification results. This approach was first evaluated on a large number of monocomponent images constructed from the Brodatz album. Then it was evaluated on two real applications using a respectively multispectral image for Cedar trees detection in the region of Baabdat (Lebanon) and a hyperspectral image for identification of invasive and non invasive vegetation in the region of Cieza (Spain). A correct classification rate (CCR) for the first application is over 97% and for the second application the average correct classification rate (ACCR) is over 99%.
A study of inter linkage effects on Candu feeder piping
International Nuclear Information System (INIS)
Li, M.; Aggarwal, M.L.; Meysner, A.
2005-01-01
A CANDU (Canadian Deuterium Uranium) reactor core consists of a large number of fuel channels where heat is generated. Two feeder pipes are connected to each fuel channel to transport D 2 O coolant into and out of the reactor core. The feeder piping is designed to the requirements of Class 1 piping of Section III NB of the ASME Boiler and Pressure Vessel and CSA Codes. Feeder piping stress analysis is being performed to demonstrate the code compliance check and the fitness for service of feeders. In the past, stress analyses were conducted for each individual feeder without including interaction effects among connected feeders. Interaction effects occur as a result of linkages that exist between feeders to prevent fretting and impacting damage during normal, abnormal and accident conditions. In this paper, a 'combined' approach is adopted to include all feeders connected by inter linkages into one feeder piping model. MSC/NASTRAN finite element software was used in the stress simulation, which contains up to 127 feeder pipes. The ASME Class 1 piping analysis was conducted to investigate the effects of the linkages between feeders. Both seismic time history and broadened response spectra methods were used in the seismic stress calculation. The results show that the effect of linkages is significant in dynamic stresses for all feeder configurations, as well as in static stresses for certain feeder configurations. The single feeder analysis could either underestimate or overestimate feeder stresses depending on the pipe geometry and bend wall thickness. (authors)
Energy Technology Data Exchange (ETDEWEB)
Dube, M.P.; Kibar, Z.; Rouleau, G.A. [McGill Univ., Quebec (Canada)] [and others
1997-03-01
Hereditary spastic paraplegia (HSP) is a degenerative disorder of the motor system, defined by progressive weakness and spasticity of the lower limbs. HSP may be inherited as an autosomal dominant (AD), autosomal recessive, or an X-linked trait. AD HSP is genetically heterogeneous, and three loci have been identified so far: SPG3 maps to chromosome 14q, SPG4 to 2p, and SPG4a to 15q. We have undertaken linkage analysis with 21 uncomplicated AD families to the three AD HSP loci. We report significant linkage for three of our families to the SPG4 locus and exclude several families by multipoint linkage. We used linkage information from several different research teams to evaluate the statistical probability of linkage to the SPG4 locus for uncomplicated AD HSP families and established the critical LOD-score value necessary for confirmation of linkage to the SPG4 locus from Bayesian statistics. In addition, we calculated the empirical P-values for the LOD scores obtained with all families with computer simulation methods. Power to detect significant linkage, as well as type I error probabilities, were evaluated. This combined analytical approach permitted conclusive linkage analyses on small to medium-size families, under the restrictions of genetic heterogeneity. 19 refs., 1 fig., 1 tab.
Dubé, M P; Mlodzienski, M A; Kibar, Z; Farlow, M R; Ebers, G; Harper, P; Kolodny, E H; Rouleau, G A; Figlewicz, D A
1997-03-01
Hereditary spastic paraplegia (HSP) is a degenerative disorder of the motor system, defined by progressive weakness and spasticity of the lower limbs. HSP may be inherited as an autosomal dominant (AD), autosomal recessive, or an X-linked trait. AD HSP is genetically heterogeneous, and three loci have been identified so far: SPG3 maps to chromosome 14q, SPG4 to 2p, and SPG4a to 15q. We have undertaken linkage analysis with 21 uncomplicated AD families to the three AD HSP loci. We report significant linkage for three of our families to the SPG4 locus and exclude several families by multipoint linkage. We used linkage information from several different research teams to evaluate the statistical probability of linkage to the SPG4 locus for uncomplicated AD HSP families and established the critical LOD-score value necessary for confirmation of linkage to the SPG4 locus from Bayesian statistics. In addition, we calculated the empirical P-values for the LOD scores obtained with all families with computer simulation methods. Power to detect significant linkage, as well as type I error probabilities, were evaluated. This combined analytical approach permitted conclusive linkage analyses on small to medium-size families, under the restrictions of genetic heterogeneity.
Point-of-Care HIV Testing and Linkage in an Urban Cohort in the Southern US
Directory of Open Access Journals (Sweden)
Anne Zinski
2013-01-01
Full Text Available The Southern states experience the highest rates of HIV and AIDS in the US, and point-of-care (POC testing outside of primary care may contribute to status awareness in medically underserved populations in this region. To evaluate POC screening and linkage to care at an urban south site, analyses were performed on a dataset of 3,651 individuals from an integrated rapid-result HIV testing and linkage program to describe this test-seeking cohort and determine trends associated with screening, results, and linkage to care. Four percent of the population had positive results. We observed significant differences by test result for age, race and gender, reported risk behaviors, test location, and motivation for screening. The overall linkage rate was 86%, and we found significant differences for clients who were linked to HIV care versus persons whose linkage could not be confirmed with respect to race and gender, location, and motivation. The linkage rate for POC testing that included a comprehensive intake visit and colocated primary care services for in-state residents was 97%. Additional research on integrated POC screening and linkage methodologies that provide intake services at time of testing is essential for increasing status awareness and improving linkage to HIV care in the US.
A ¤nonparametric dynamic additive regression model for longitudinal data
DEFF Research Database (Denmark)
Martinussen, T.; Scheike, T. H.
2000-01-01
dynamic linear models, estimating equations, least squares, longitudinal data, nonparametric methods, partly conditional mean models, time-varying-coefficient models......dynamic linear models, estimating equations, least squares, longitudinal data, nonparametric methods, partly conditional mean models, time-varying-coefficient models...
DEFF Research Database (Denmark)
Effraimidis, Georgios; Dahl, Christian Møller
In this paper, we develop a fully nonparametric approach for the estimation of the cumulative incidence function with Missing At Random right-censored competing risks data. We obtain results on the pointwise asymptotic normality as well as the uniform convergence rate of the proposed nonparametric...
Non-parametric tests of productive efficiency with errors-in-variables
Kuosmanen, T.K.; Post, T.; Scholtes, S.
2007-01-01
We develop a non-parametric test of productive efficiency that accounts for errors-in-variables, following the approach of Varian. [1985. Nonparametric analysis of optimizing behavior with measurement error. Journal of Econometrics 30(1/2), 445-458]. The test is based on the general Pareto-Koopmans
Linkage disequilibrium and association mapping.
Weir, B S
2008-01-01
Linkage disequilibrium refers to the association between alleles at different loci. The standard definition applies to two alleles in the same gamete, and it can be regarded as the covariance of indicator variables for the states of those two alleles. The corresponding correlation coefficient rho is the parameter that arises naturally in discussions of tests of association between markers and genetic diseases. A general treatment of association tests makes use of the additive and nonadditive components of variance for the disease gene. In almost all expressions that describe the behavior of association tests, additive variance components are modified by the squared correlation coefficient rho2 and the nonadditive variance components by rho4, suggesting that nonadditive components have less influence than additive components on association tests.
Dwivedi, Alok Kumar; Mallawaarachchi, Indika; Alvarado, Luis A
2017-06-30
Experimental studies in biomedical research frequently pose analytical problems related to small sample size. In such studies, there are conflicting findings regarding the choice of parametric and nonparametric analysis, especially with non-normal data. In such instances, some methodologists questioned the validity of parametric tests and suggested nonparametric tests. In contrast, other methodologists found nonparametric tests to be too conservative and less powerful and thus preferred using parametric tests. Some researchers have recommended using a bootstrap test; however, this method also has small sample size limitation. We used a pooled method in nonparametric bootstrap test that may overcome the problem related with small samples in hypothesis testing. The present study compared nonparametric bootstrap test with pooled resampling method corresponding to parametric, nonparametric, and permutation tests through extensive simulations under various conditions and using real data examples. The nonparametric pooled bootstrap t-test provided equal or greater power for comparing two means as compared with unpaired t-test, Welch t-test, Wilcoxon rank sum test, and permutation test while maintaining type I error probability for any conditions except for Cauchy and extreme variable lognormal distributions. In such cases, we suggest using an exact Wilcoxon rank sum test. Nonparametric bootstrap paired t-test also provided better performance than other alternatives. Nonparametric bootstrap test provided benefit over exact Kruskal-Wallis test. We suggest using nonparametric bootstrap test with pooled resampling method for comparing paired or unpaired means and for validating the one way analysis of variance test results for non-normal data in small sample size studies. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
An estimating function approach to linkage heterogeneity
Indian Academy of Sciences (India)
Testing linkage heterogeneity between two loci is an important issue in genetics. Currently, there are ... on linkage heterogeneity can help people to better understand complex .... χ2(F − 2) + cχ2 (1), where c is a constant (see Appendix). Here, it can be ..... gin, ancestry, gender, age, etc., for purpose of dividing sub- groups to ...
Wesselink, Christiaan; Heeg, Govert P.; Jansonius, Nomdo M.
Objective: To compare prospectively 2 perimetric progression detection algorithms for glaucoma, the Early Manifest Glaucoma Trial algorithm (glaucoma progression analysis [GPA]) and a nonparametric algorithm applied to the mean deviation (MD) (nonparametric progression analysis [NPA]). Methods:
Pataky, Todd C; Vanrenterghem, Jos; Robinson, Mark A
2015-05-01
Biomechanical processes are often manifested as one-dimensional (1D) trajectories. It has been shown that 1D confidence intervals (CIs) are biased when based on 0D statistical procedures, and the non-parametric 1D bootstrap CI has emerged in the Biomechanics literature as a viable solution. The primary purpose of this paper was to clarify that, for 1D biomechanics datasets, the distinction between 0D and 1D methods is much more important than the distinction between parametric and non-parametric procedures. A secondary purpose was to demonstrate that a parametric equivalent to the 1D bootstrap exists in the form of a random field theory (RFT) correction for multiple comparisons. To emphasize these points we analyzed six datasets consisting of force and kinematic trajectories in one-sample, paired, two-sample and regression designs. Results showed, first, that the 1D bootstrap and other 1D non-parametric CIs were qualitatively identical to RFT CIs, and all were very different from 0D CIs. Second, 1D parametric and 1D non-parametric hypothesis testing results were qualitatively identical for all six datasets. Last, we highlight the limitations of 1D CIs by demonstrating that they are complex, design-dependent, and thus non-generalizable. These results suggest that (i) analyses of 1D data based on 0D models of randomness are generally biased unless one explicitly identifies 0D variables before the experiment, and (ii) parametric and non-parametric 1D hypothesis testing provide an unambiguous framework for analysis when one׳s hypothesis explicitly or implicitly pertains to whole 1D trajectories. Copyright © 2015 Elsevier Ltd. All rights reserved.
A local non-parametric model for trade sign inference
Blazejewski, Adam; Coggins, Richard
2005-03-01
We investigate a regularity in market order submission strategies for 12 stocks with large market capitalization on the Australian Stock Exchange. The regularity is evidenced by a predictable relationship between the trade sign (trade initiator), size of the trade, and the contents of the limit order book before the trade. We demonstrate this predictability by developing an empirical inference model to classify trades into buyer-initiated and seller-initiated. The model employs a local non-parametric method, k-nearest neighbor, which in the past was used successfully for chaotic time series prediction. The k-nearest neighbor with three predictor variables achieves an average out-of-sample classification accuracy of 71.40%, compared to 63.32% for the linear logistic regression with seven predictor variables. The result suggests that a non-linear approach may produce a more parsimonious trade sign inference model with a higher out-of-sample classification accuracy. Furthermore, for most of our stocks the observed regularity in market order submissions seems to have a memory of at least 30 trading days.
Efficient nonparametric n -body force fields from machine learning
Glielmo, Aldo; Zeni, Claudio; De Vita, Alessandro
2018-05-01
We provide a definition and explicit expressions for n -body Gaussian process (GP) kernels, which can learn any interatomic interaction occurring in a physical system, up to n -body contributions, for any value of n . The series is complete, as it can be shown that the "universal approximator" squared exponential kernel can be written as a sum of n -body kernels. These recipes enable the choice of optimally efficient force models for each target system, as confirmed by extensive testing on various materials. We furthermore describe how the n -body kernels can be "mapped" on equivalent representations that provide database-size-independent predictions and are thus crucially more efficient. We explicitly carry out this mapping procedure for the first nontrivial (three-body) kernel of the series, and we show that this reproduces the GP-predicted forces with meV /Å accuracy while being orders of magnitude faster. These results pave the way to using novel force models (here named "M-FFs") that are computationally as fast as their corresponding standard parametrized n -body force fields, while retaining the nonparametric character, the ease of training and validation, and the accuracy of the best recently proposed machine-learning potentials.
Non-parametric Bayesian networks: Improving theory and reviewing applications
International Nuclear Information System (INIS)
Hanea, Anca; Morales Napoles, Oswaldo; Ababei, Dan
2015-01-01
Applications in various domains often lead to high dimensional dependence modelling. A Bayesian network (BN) is a probabilistic graphical model that provides an elegant way of expressing the joint distribution of a large number of interrelated variables. BNs have been successfully used to represent uncertain knowledge in a variety of fields. The majority of applications use discrete BNs, i.e. BNs whose nodes represent discrete variables. Integrating continuous variables in BNs is an area fraught with difficulty. Several methods that handle discrete-continuous BNs have been proposed in the literature. This paper concentrates only on one method called non-parametric BNs (NPBNs). NPBNs were introduced in 2004 and they have been or are currently being used in at least twelve professional applications. This paper provides a short introduction to NPBNs, a couple of theoretical advances, and an overview of applications. The aim of the paper is twofold: one is to present the latest improvements of the theory underlying NPBNs, and the other is to complement the existing overviews of BNs applications with the NPNBs applications. The latter opens the opportunity to discuss some difficulties that applications pose to the theoretical framework and in this way offers some NPBN modelling guidance to practitioners. - Highlights: • The paper gives an overview of the current NPBNs methodology. • We extend the NPBN methodology by relaxing the conditions of one of its fundamental theorems. • We propose improvements of the data mining algorithm for the NPBNs. • We review the professional applications of the NPBNs.
Nonparametric predictive inference for combined competing risks data
International Nuclear Information System (INIS)
Coolen-Maturi, Tahani; Coolen, Frank P.A.
2014-01-01
The nonparametric predictive inference (NPI) approach for competing risks data has recently been presented, in particular addressing the question due to which of the competing risks the next unit will fail, and also considering the effects of unobserved, re-defined, unknown or removed competing risks. In this paper, we introduce how the NPI approach can be used to deal with situations where units are not all at risk from all competing risks. This may typically occur if one combines information from multiple samples, which can, e.g. be related to further aspects of units that define the samples or groups to which the units belong or to different applications where the circumstances under which the units operate can vary. We study the effect of combining the additional information from these multiple samples, so effectively borrowing information on specific competing risks from other units, on the inferences. Such combination of information can be relevant to competing risks scenarios in a variety of application areas, including engineering and medical studies
Transition redshift: new constraints from parametric and nonparametric methods
Energy Technology Data Exchange (ETDEWEB)
Rani, Nisha; Mahajan, Shobhit; Mukherjee, Amitabha [Department of Physics and Astrophysics, University of Delhi, New Delhi 110007 (India); Jain, Deepak [Deen Dayal Upadhyaya College, University of Delhi, New Delhi 110015 (India); Pires, Nilza, E-mail: nrani@physics.du.ac.in, E-mail: djain@ddu.du.ac.in, E-mail: shobhit.mahajan@gmail.com, E-mail: amimukh@gmail.com, E-mail: npires@dfte.ufrn.br [Departamento de Física Teórica e Experimental, UFRN, Campus Universitário, Natal, RN 59072-970 (Brazil)
2015-12-01
In this paper, we use the cosmokinematics approach to study the accelerated expansion of the Universe. This is a model independent approach and depends only on the assumption that the Universe is homogeneous and isotropic and is described by the FRW metric. We parametrize the deceleration parameter, q(z), to constrain the transition redshift (z{sub t}) at which the expansion of the Universe goes from a decelerating to an accelerating phase. We use three different parametrizations of q(z) namely, q{sub I}(z)=q{sub 1}+q{sub 2}z, q{sub II} (z) = q{sub 3} + q{sub 4} ln (1 + z) and q{sub III} (z)=½+q{sub 5}/(1+z){sup 2}. A joint analysis of the age of galaxies, strong lensing and supernovae Ia data indicates that the transition redshift is less than unity i.e. z{sub t} < 1. We also use a nonparametric approach (LOESS+SIMEX) to constrain z{sub t}. This too gives z{sub t} < 1 which is consistent with the value obtained by the parametric approach.
Discrete non-parametric kernel estimation for global sensitivity analysis
International Nuclear Information System (INIS)
Senga Kiessé, Tristan; Ventura, Anne
2016-01-01
This work investigates the discrete kernel approach for evaluating the contribution of the variance of discrete input variables to the variance of model output, via analysis of variance (ANOVA) decomposition. Until recently only the continuous kernel approach has been applied as a metamodeling approach within sensitivity analysis framework, for both discrete and continuous input variables. Now the discrete kernel estimation is known to be suitable for smoothing discrete functions. We present a discrete non-parametric kernel estimator of ANOVA decomposition of a given model. An estimator of sensitivity indices is also presented with its asymtotic convergence rate. Some simulations on a test function analysis and a real case study from agricultural have shown that the discrete kernel approach outperforms the continuous kernel one for evaluating the contribution of moderate or most influential discrete parameters to the model output. - Highlights: • We study a discrete kernel estimation for sensitivity analysis of a model. • A discrete kernel estimator of ANOVA decomposition of the model is presented. • Sensitivity indices are calculated for discrete input parameters. • An estimator of sensitivity indices is also presented with its convergence rate. • An application is realized for improving the reliability of environmental models.
Nonparametric predictive inference for combining diagnostic tests with parametric copula
Muhammad, Noryanti; Coolen, F. P. A.; Coolen-Maturi, T.
2017-09-01
Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine and health care. The Receiver Operating Characteristic (ROC) curve is a popular statistical tool for describing the performance of diagnostic tests. The area under the ROC curve (AUC) is often used as a measure of the overall performance of the diagnostic test. In this paper, we interest in developing strategies for combining test results in order to increase the diagnostic accuracy. We introduce nonparametric predictive inference (NPI) for combining two diagnostic test results with considering dependence structure using parametric copula. NPI is a frequentist statistical framework for inference on a future observation based on past data observations. NPI uses lower and upper probabilities to quantify uncertainty and is based on only a few modelling assumptions. While copula is a well-known statistical concept for modelling dependence of random variables. A copula is a joint distribution function whose marginals are all uniformly distributed and it can be used to model the dependence separately from the marginal distributions. In this research, we estimate the copula density using a parametric method which is maximum likelihood estimator (MLE). We investigate the performance of this proposed method via data sets from the literature and discuss results to show how our method performs for different family of copulas. Finally, we briefly outline related challenges and opportunities for future research.
Probability Machines: Consistent Probability Estimation Using Nonparametric Learning Machines
Malley, J. D.; Kruppa, J.; Dasgupta, A.; Malley, K. G.; Ziegler, A.
2011-01-01
Summary Background Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. Objectives The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Methods Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Results Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Conclusions Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications. PMID:21915433
Nonparametric Integrated Agrometeorological Drought Monitoring: Model Development and Application
Zhang, Qiang; Li, Qin; Singh, Vijay P.; Shi, Peijun; Huang, Qingzhong; Sun, Peng
2018-01-01
Drought is a major natural hazard that has massive impacts on the society. How to monitor drought is critical for its mitigation and early warning. This study proposed a modified version of the multivariate standardized drought index (MSDI) based on precipitation, evapotranspiration, and soil moisture, i.e., modified multivariate standardized drought index (MMSDI). This study also used nonparametric joint probability distribution analysis. Comparisons were done between standardized precipitation evapotranspiration index (SPEI), standardized soil moisture index (SSMI), MSDI, and MMSDI, and real-world observed drought regimes. Results indicated that MMSDI detected droughts that SPEI and/or SSMI failed to do. Also, MMSDI detected almost all droughts that were identified by SPEI and SSMI. Further, droughts detected by MMSDI were similar to real-world observed droughts in terms of drought intensity and drought-affected area. When compared to MMSDI, MSDI has the potential to overestimate drought intensity and drought-affected area across China, which should be attributed to exclusion of the evapotranspiration components from estimation of drought intensity. Therefore, MMSDI is proposed for drought monitoring that can detect agrometeorological droughts. Results of this study provide a framework for integrated drought monitoring in other regions of the world and can help to develop drought mitigation.
Bayesian nonparametric clustering in phylogenetics: modeling antigenic evolution in influenza.
Cybis, Gabriela B; Sinsheimer, Janet S; Bedford, Trevor; Rambaut, Andrew; Lemey, Philippe; Suchard, Marc A
2018-01-30
Influenza is responsible for up to 500,000 deaths every year, and antigenic variability represents much of its epidemiological burden. To visualize antigenic differences across many viral strains, antigenic cartography methods use multidimensional scaling on binding assay data to map influenza antigenicity onto a low-dimensional space. Analysis of such assay data ideally leads to natural clustering of influenza strains of similar antigenicity that correlate with sequence evolution. To understand the dynamics of these antigenic groups, we present a framework that jointly models genetic and antigenic evolution by combining multidimensional scaling of binding assay data, Bayesian phylogenetic machinery and nonparametric clustering methods. We propose a phylogenetic Chinese restaurant process that extends the current process to incorporate the phylogenetic dependency structure between strains in the modeling of antigenic clusters. With this method, we are able to use the genetic information to better understand the evolution of antigenicity throughout epidemics, as shown in applications of this model to H1N1 influenza. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model.
Xu, Zhiguang; MacEachern, Steven; Xu, Xinyi
2015-02-01
We present a class of Bayesian copula models whose major components are the marginal (limiting) distribution of a stationary time series and the internal dynamics of the series. We argue that these are the two features with which an analyst is typically most familiar, and hence that these are natural components with which to work. For the marginal distribution, we use a nonparametric Bayesian prior distribution along with a cdf-inverse cdf transformation to obtain large support. For the internal dynamics, we rely on the traditionally successful techniques of normal-theory time series. Coupling the two components gives us a family of (Gaussian) copula transformed autoregressive models. The models provide coherent adjustments of time scales and are compatible with many extensions, including changes in volatility of the series. We describe basic properties of the models, show their ability to recover non-Gaussian marginal distributions, and use a GARCH modification of the basic model to analyze stock index return series. The models are found to provide better fit and improved short-range and long-range predictions than Gaussian competitors. The models are extensible to a large variety of fields, including continuous time models, spatial models, models for multiple series, models driven by external covariate streams, and non-stationary models.
Nonparametric adaptive age replacement with a one-cycle criterion
International Nuclear Information System (INIS)
Coolen-Schrijner, P.; Coolen, F.P.A.
2007-01-01
Age replacement of technical units has received much attention in the reliability literature over the last four decades. Mostly, the failure time distribution for the units is assumed to be known, and minimal costs per unit of time is used as optimality criterion, where renewal reward theory simplifies the mathematics involved but requires the assumption that the same process and replacement strategy continues over a very large ('infinite') period of time. Recently, there has been increasing attention to adaptive strategies for age replacement, taking into account the information from the process. Although renewal reward theory can still be used to provide an intuitively and mathematically attractive optimality criterion, it is more logical to use minimal costs per unit of time over a single cycle as optimality criterion for adaptive age replacement. In this paper, we first show that in the classical age replacement setting, with known failure time distribution with increasing hazard rate, the one-cycle criterion leads to earlier replacement than the renewal reward criterion. Thereafter, we present adaptive age replacement with a one-cycle criterion within the nonparametric predictive inferential framework. We study the performance of this approach via simulations, which are also used for comparisons with the use of the renewal reward criterion within the same statistical framework
Bayesian Nonparametric Model for Estimating Multistate Travel Time Distribution
Directory of Open Access Journals (Sweden)
Emmanuel Kidando
2017-01-01
Full Text Available Multistate models, that is, models with more than two distributions, are preferred over single-state probability models in modeling the distribution of travel time. Literature review indicated that the finite multistate modeling of travel time using lognormal distribution is superior to other probability functions. In this study, we extend the finite multistate lognormal model of estimating the travel time distribution to unbounded lognormal distribution. In particular, a nonparametric Dirichlet Process Mixture Model (DPMM with stick-breaking process representation was used. The strength of the DPMM is that it can choose the number of components dynamically as part of the algorithm during parameter estimation. To reduce computational complexity, the modeling process was limited to a maximum of six components. Then, the Markov Chain Monte Carlo (MCMC sampling technique was employed to estimate the parameters’ posterior distribution. Speed data from nine links of a freeway corridor, aggregated on a 5-minute basis, were used to calculate the corridor travel time. The results demonstrated that this model offers significant flexibility in modeling to account for complex mixture distributions of the travel time without specifying the number of components. The DPMM modeling further revealed that freeway travel time is characterized by multistate or single-state models depending on the inclusion of onset and offset of congestion periods.
Nonparametric Bayes Classification and Hypothesis Testing on Manifolds
Bhattacharya, Abhishek; Dunson, David
2012-01-01
Our first focus is prediction of a categorical response variable using features that lie on a general manifold. For example, the manifold may correspond to the surface of a hypersphere. We propose a general kernel mixture model for the joint distribution of the response and predictors, with the kernel expressed in product form and dependence induced through the unknown mixing measure. We provide simple sufficient conditions for large support and weak and strong posterior consistency in estimating both the joint distribution of the response and predictors and the conditional distribution of the response. Focusing on a Dirichlet process prior for the mixing measure, these conditions hold using von Mises-Fisher kernels when the manifold is the unit hypersphere. In this case, Bayesian methods are developed for efficient posterior computation using slice sampling. Next we develop Bayesian nonparametric methods for testing whether there is a difference in distributions between groups of observations on the manifold having unknown densities. We prove consistency of the Bayes factor and develop efficient computational methods for its calculation. The proposed classification and testing methods are evaluated using simulation examples and applied to spherical data applications. PMID:22754028
Bayesian nonparametric meta-analysis using Polya tree mixture models.
Branscum, Adam J; Hanson, Timothy E
2008-09-01
Summary. A common goal in meta-analysis is estimation of a single effect measure using data from several studies that are each designed to address the same scientific inquiry. Because studies are typically conducted in geographically disperse locations, recent developments in the statistical analysis of meta-analytic data involve the use of random effects models that account for study-to-study variability attributable to differences in environments, demographics, genetics, and other sources that lead to heterogeneity in populations. Stemming from asymptotic theory, study-specific summary statistics are modeled according to normal distributions with means representing latent true effect measures. A parametric approach subsequently models these latent measures using a normal distribution, which is strictly a convenient modeling assumption absent of theoretical justification. To eliminate the influence of overly restrictive parametric models on inferences, we consider a broader class of random effects distributions. We develop a novel hierarchical Bayesian nonparametric Polya tree mixture (PTM) model. We present methodology for testing the PTM versus a normal random effects model. These methods provide researchers a straightforward approach for conducting a sensitivity analysis of the normality assumption for random effects. An application involving meta-analysis of epidemiologic studies designed to characterize the association between alcohol consumption and breast cancer is presented, which together with results from simulated data highlight the performance of PTMs in the presence of nonnormality of effect measures in the source population.
DPpackage: Bayesian Semi- and Nonparametric Modeling in R
Directory of Open Access Journals (Sweden)
Alejandro Jara
2011-04-01
Full Text Available Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on a function space, such as the space of all probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex and hence sampling methods play a key role. This paper provides an introduction to a simple, yet comprehensive, set of programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently, DPpackage includes models for marginal and conditional density estimation, receiver operating characteristic curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered data using generalized linear mixed models, and regression data using generalized additive models. The package also contains functions to compute pseudo-Bayes factors for model comparison and for eliciting the precision parameter of the Dirichlet process prior, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency, the actual sampling for each model is carried out using compiled C, C++ or Fortran code.
Wang, Yuanjia; Garcia, Tanya P.; Ma, Yanyuan
2012-01-01
This work presents methods for estimating genotype-specific distributions from genetic epidemiology studies where the event times are subject to right censoring, the genotypes are not directly observed, and the data arise from a mixture of scientifically meaningful subpopulations. Examples of such studies include kin-cohort studies and quantitative trait locus (QTL) studies. Current methods for analyzing censored mixture data include two types of nonparametric maximum likelihood estimators (NPMLEs) which do not make parametric assumptions on the genotype-specific density functions. Although both NPMLEs are commonly used, we show that one is inefficient and the other inconsistent. To overcome these deficiencies, we propose three classes of consistent nonparametric estimators which do not assume parametric density models and are easy to implement. They are based on the inverse probability weighting (IPW), augmented IPW (AIPW), and nonparametric imputation (IMP). The AIPW achieves the efficiency bound without additional modeling assumptions. Extensive simulation experiments demonstrate satisfactory performance of these estimators even when the data are heavily censored. We apply these estimators to the Cooperative Huntington’s Observational Research Trial (COHORT), and provide age-specific estimates of the effect of mutation in the Huntington gene on mortality using a sample of family members. The close approximation of the estimated non-carrier survival rates to that of the U.S. population indicates small ascertainment bias in the COHORT family sample. Our analyses underscore an elevated risk of death in Huntington gene mutation carriers compared to non-carriers for a wide age range, and suggest that the mutation equally affects survival rates in both genders. The estimated survival rates are useful in genetic counseling for providing guidelines on interpreting the risk of death associated with a positive genetic testing, and in facilitating future subjects at risk
Linkage Behavior and Practices of Agencies in the Agricultural ...
African Journals Online (AJOL)
The study examined the linkage behaviour and practices of agencies in the ... institutes; while (61.5%,65.5%and 50.0%) indicated that linkages with universities of ... Existing institutional framework for linkages between research and extension ...
Akhtar, Naveed; Mian, Ajmal
2017-10-03
We present a principled approach to learn a discriminative dictionary along a linear classifier for hyperspectral classification. Our approach places Gaussian Process priors over the dictionary to account for the relative smoothness of the natural spectra, whereas the classifier parameters are sampled from multivariate Gaussians. We employ two Beta-Bernoulli processes to jointly infer the dictionary and the classifier. These processes are coupled under the same sets of Bernoulli distributions. In our approach, these distributions signify the frequency of the dictionary atom usage in representing class-specific training spectra, which also makes the dictionary discriminative. Due to the coupling between the dictionary and the classifier, the popularity of the atoms for representing different classes gets encoded into the classifier. This helps in predicting the class labels of test spectra that are first represented over the dictionary by solving a simultaneous sparse optimization problem. The labels of the spectra are predicted by feeding the resulting representations to the classifier. Our approach exploits the nonparametric Bayesian framework to automatically infer the dictionary size--the key parameter in discriminative dictionary learning. Moreover, it also has the desirable property of adaptively learning the association between the dictionary atoms and the class labels by itself. We use Gibbs sampling to infer the posterior probability distributions over the dictionary and the classifier under the proposed model, for which, we derive analytical expressions. To establish the effectiveness of our approach, we test it on benchmark hyperspectral images. The classification performance is compared with the state-of-the-art dictionary learning-based classification methods.
A robust nonparametric method for quantifying undetected extinctions.
Chisholm, Ryan A; Giam, Xingli; Sadanandan, Keren R; Fung, Tak; Rheindt, Frank E
2016-06-01
How many species have gone extinct in modern times before being described by science? To answer this question, and thereby get a full assessment of humanity's impact on biodiversity, statistical methods that quantify undetected extinctions are required. Such methods have been developed recently, but they are limited by their reliance on parametric assumptions; specifically, they assume the pools of extant and undetected species decay exponentially, whereas real detection rates vary temporally with survey effort and real extinction rates vary with the waxing and waning of threatening processes. We devised a new, nonparametric method for estimating undetected extinctions. As inputs, the method requires only the first and last date at which each species in an ensemble was recorded. As outputs, the method provides estimates of the proportion of species that have gone extinct, detected, or undetected and, in the special case where the number of undetected extant species in the present day is assumed close to zero, of the absolute number of undetected extinct species. The main assumption of the method is that the per-species extinction rate is independent of whether a species has been detected or not. We applied the method to the resident native bird fauna of Singapore. Of 195 recorded species, 58 (29.7%) have gone extinct in the last 200 years. Our method projected that an additional 9.6 species (95% CI 3.4, 19.8) have gone extinct without first being recorded, implying a true extinction rate of 33.0% (95% CI 31.0%, 36.2%). We provide R code for implementing our method. Because our method does not depend on strong assumptions, we expect it to be broadly useful for quantifying undetected extinctions. © 2016 Society for Conservation Biology.
Economic decision making and the application of nonparametric prediction models
Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.
2008-01-01
Sustained increases in energy prices have focused attention on gas resources in low-permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are often large. Planning and development decisions for extraction of such resources must be areawide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm, the decision to enter such plays depends on reconnaissance-level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional-scale cost functions. The context of the worked example is the Devonian Antrim-shale gas play in the Michigan basin. One finding relates to selection of the resource prediction model to be used with economic models. Models chosen because they can best predict aggregate volume over larger areas (many hundreds of sites) smooth out granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined arbitrarily by extraneous factors. The analysis shows a 15-20% gain in gas volume when these simple models are applied to order drilling prospects strategically rather than to choose drilling locations randomly. Copyright ?? 2008 Society of Petroleum Engineers.
Abreu, P C; Greenberg, D A; Hodge, S E
1999-09-01
Several methods have been proposed for linkage analysis of complex traits with unknown mode of inheritance. These methods include the LOD score maximized over disease models (MMLS) and the "nonparametric" linkage (NPL) statistic. In previous work, we evaluated the increase of type I error when maximizing over two or more genetic models, and we compared the power of MMLS to detect linkage, in a number of complex modes of inheritance, with analysis assuming the true model. In the present study, we compare MMLS and NPL directly. We simulated 100 data sets with 20 families each, using 26 generating models: (1) 4 intermediate models (penetrance of heterozygote between that of the two homozygotes); (2) 6 two-locus additive models; and (3) 16 two-locus heterogeneity models (admixture alpha = 1.0,.7,.5, and.3; alpha = 1.0 replicates simple Mendelian models). For LOD scores, we assumed dominant and recessive inheritance with 50% penetrance. We took the higher of the two maximum LOD scores and subtracted 0.3 to correct for multiple tests (MMLS-C). We compared expected maximum LOD scores and power, using MMLS-C and NPL as well as the true model. Since NPL uses only the affected family members, we also performed an affecteds-only analysis using MMLS-C. The MMLS-C was both uniformly more powerful than NPL for most cases we examined, except when linkage information was low, and close to the results for the true model under locus heterogeneity. We still found better power for the MMLS-C compared with NPL in affecteds-only analysis. The results show that use of two simple modes of inheritance at a fixed penetrance can have more power than NPL when the trait mode of inheritance is complex and when there is heterogeneity in the data set.
Linkage disequilibrium in wild mice.
Directory of Open Access Journals (Sweden)
Cathy C Laurie
2007-08-01
Full Text Available Crosses between laboratory strains of mice provide a powerful way of detecting quantitative trait loci for complex traits related to human disease. Hundreds of these loci have been detected, but only a small number of the underlying causative genes have been identified. The main difficulty is the extensive linkage disequilibrium (LD in intercross progeny and the slow process of fine-scale mapping by traditional methods. Recently, new approaches have been introduced, such as association studies with inbred lines and multigenerational crosses. These approaches are very useful for interval reduction, but generally do not provide single-gene resolution because of strong LD extending over one to several megabases. Here, we investigate the genetic structure of a natural population of mice in Arizona to determine its suitability for fine-scale LD mapping and association studies. There are three main findings: (1 Arizona mice have a high level of genetic variation, which includes a large fraction of the sequence variation present in classical strains of laboratory mice; (2 they show clear evidence of local inbreeding but appear to lack stable population structure across the study area; and (3 LD decays with distance at a rate similar to human populations, which is considerably more rapid than in laboratory populations of mice. Strong associations in Arizona mice are limited primarily to markers less than 100 kb apart, which provides the possibility of fine-scale association mapping at the level of one or a few genes. Although other considerations, such as sample size requirements and marker discovery, are serious issues in the implementation of association studies, the genetic variation and LD results indicate that wild mice could provide a useful tool for identifying genes that cause variation in complex traits.
A genetic linkage map for the saltwater crocodile (Crocodylus porosus
Directory of Open Access Journals (Sweden)
Lance Stacey L
2009-07-01
Full Text Available Abstract Background Genome elucidation is now in high gear for many organisms, and whilst genetic maps have been developed for a broad array of species, surprisingly, no such maps exist for a crocodilian, or indeed any other non-avian member of the Class Reptilia. Genetic linkage maps are essential tools for the mapping and dissection of complex quantitative trait loci (QTL, and in order to permit systematic genome scans for the identification of genes affecting economically important traits in farmed crocodilians, a comprehensive genetic linage map will be necessary. Results A first-generation genetic linkage map for the saltwater crocodile (Crocodylus porosus was constructed using 203 microsatellite markers amplified across a two-generation pedigree comprising ten full-sib families from a commercial population at Darwin Crocodile Farm, Northern Territory, Australia. Linkage analyses identified fourteen linkage groups comprising a total of 180 loci, with 23 loci remaining unlinked. Markers were ordered within linkage groups employing a heuristic approach using CRIMAP v3.0 software. The estimated female and male recombination map lengths were 1824.1 and 319.0 centimorgans (cM respectively, revealing an uncommonly large disparity in recombination map lengths between sexes (ratio of 5.7:1. Conclusion We have generated the first genetic linkage map for a crocodilian, or indeed any other non-avian reptile. The uncommonly large disparity in recombination map lengths confirms previous preliminary evidence of major differences in sex-specific recombination rates in a species that exhibits temperature-dependent sex determination (TSD. However, at this point the reason for this disparity in saltwater crocodiles remains unclear. This map will be a valuable resource for crocodilian researchers, facilitating the systematic genome scans necessary for identifying genes affecting complex traits of economic importance in the crocodile industry. In addition
Resource linkages and sustainable development
Anouti, Yahya
Historically, fossil fuel consumers in most developing hydrocarbon-rich countries have enjoyed retail prices at a discount from international benchmarks. Governments of these countries consider the subsidy transfer to be a means for sharing the wealth from their resource endowment. These subsidies create negative economic, environmental, and social distortions, which can only increase over time with a fast growing, young, and rich population. The pressure to phase out these subsidies has been mounting over the last years. At the same time, policy makers in resource-rich developing countries are keen to obtain the greatest benefits for their economies from the extraction of their exhaustible resources. To this end, they are deploying local content policies with the aim of increasing the economic linkages from extracting their resources. Against this background, this dissertation's three essays evaluate (1) the global impact of rationalizing transport fuel prices, (2) how resource-rich countries can achieve the objectives behind fuel subsidies more efficiently through direct cash transfers, and (3) the economic tradeoffs from deploying local content policies and the presence of an optimal path. We begin by reviewing the literature and building the case for rationalizing transport fuel prices to reflect their direct costs (production), indirect costs (road maintenance) and negative externalities (climate change, local pollutants, traffic accidents and congestion). To do so, we increase the scope of the economic literature by presenting an algorithm to evaluate the rationalized prices in different countries. Then, we apply this algorithm to quantify the rationalized prices across 123 countries in a partial equilibrium setting. Finally, we present the first comprehensive measure of the impact of rationalizing fuel prices on the global demand for gasoline and diesel, environmental emissions, government revenues, and consumers' welfare. By rationalizing transport fuel
Trend Analysis of Pahang River Using Non-Parametric Analysis: Mann Kendalls Trend Test
International Nuclear Information System (INIS)
Nur Hishaam Sulaiman; Mohd Khairul Amri Kamarudin; Mohd Khairul Amri Kamarudin; Ahmad Dasuki Mustafa; Muhammad Azizi Amran; Fazureen Azaman; Ismail Zainal Abidin; Norsyuhada Hairoma
2015-01-01
Flood is common in Pahang especially during northeast monsoon season from November to February. Three river cross station: Lubuk Paku, Sg. Yap and Temerloh were selected as area of this study. The stream flow and water level data were gathered from DID record. Data set for this study were analysed by using non-parametric analysis, Mann-Kendall Trend Test. The results that obtained from stream flow and water level analysis indicate that there are positively significant trend for Lubuk Paku (0.001) and Sg. Yap (<0.0001) from 1972-2011 with the p-value < 0.05. Temerloh (0.178) data from 1963-2011 recorded no trend for stream flow parameter but negative trend for water level parameter. Hydrological pattern and trend are extremely affected by outside factors such as north east monsoon season that occurred in South China Sea and affected Pahang during November to March. There are other factors such as development and management of the areas which can be considered as factors affected the data and results. Hydrological Pattern is important to indicate the river trend such as stream flow and water level. It can be used as flood mitigation by local authorities. (author)
A framework for Bayesian nonparametric inference for causal effects of mediation.
Kim, Chanmin; Daniels, Michael J; Marcus, Bess H; Roy, Jason A
2017-06-01
We propose a Bayesian non-parametric (BNP) framework for estimating causal effects of mediation, the natural direct, and indirect, effects. The strategy is to do this in two parts. Part 1 is a flexible model (using BNP) for the observed data distribution. Part 2 is a set of uncheckable assumptions with sensitivity parameters that in conjunction with Part 1 allows identification and estimation of the causal parameters and allows for uncertainty about these assumptions via priors on the sensitivity parameters. For Part 1, we specify a Dirichlet process mixture of multivariate normals as a prior on the joint distribution of the outcome, mediator, and covariates. This approach allows us to obtain a (simple) closed form of each marginal distribution. For Part 2, we consider two sets of assumptions: (a) the standard sequential ignorability (Imai et al., 2010) and (b) weakened set of the conditional independence type assumptions introduced in Daniels et al. (2012) and propose sensitivity analyses for both. We use this approach to assess mediation in a physical activity promotion trial. © 2016, The International Biometric Society.
Rodríguez-Álvarez, María Xosé; Roca-Pardiñas, Javier; Cadarso-Suárez, Carmen; Tahoces, Pablo G
2018-03-01
Prior to using a diagnostic test in a routine clinical setting, the rigorous evaluation of its diagnostic accuracy is essential. The receiver-operating characteristic curve is the measure of accuracy most widely used for continuous diagnostic tests. However, the possible impact of extra information about the patient (or even the environment) on diagnostic accuracy also needs to be assessed. In this paper, we focus on an estimator for the covariate-specific receiver-operating characteristic curve based on direct regression modelling and nonparametric smoothing techniques. This approach defines the class of generalised additive models for the receiver-operating characteristic curve. The main aim of the paper is to offer new inferential procedures for testing the effect of covariates on the conditional receiver-operating characteristic curve within the above-mentioned class. Specifically, two different bootstrap-based tests are suggested to check (a) the possible effect of continuous covariates on the receiver-operating characteristic curve and (b) the presence of factor-by-curve interaction terms. The validity of the proposed bootstrap-based procedures is supported by simulations. To facilitate the application of these new procedures in practice, an R-package, known as npROCRegression, is provided and briefly described. Finally, data derived from a computer-aided diagnostic system for the automatic detection of tumour masses in breast cancer is analysed.
Ramajo, Julián; Cordero, José Manuel; Márquez, Miguel Ángel
2017-10-01
This paper analyses region-level technical efficiency in nine European countries over the 1995-2007 period. We propose the application of a nonparametric conditional frontier approach to account for the presence of heterogeneous conditions in the form of geographical externalities. Such environmental factors are beyond the control of regional authorities, but may affect the production function. Therefore, they need to be considered in the frontier estimation. Specifically, a spatial autoregressive term is included as an external conditioning factor in a robust order- m model. Thus we can test the hypothesis of non-separability (the external factor impacts both the input-output space and the distribution of efficiencies), demonstrating the existence of significant global interregional spillovers into the production process. Our findings show that geographical externalities affect both the frontier level and the probability of being more or less efficient. Specifically, the results support the fact that the spatial lag variable has an inverted U-shaped non-linear impact on the performance of regions. This finding can be interpreted as a differential effect of interregional spillovers depending on the size of the neighboring economies: positive externalities for small values, possibly related to agglomeration economies, and negative externalities for high values, indicating the possibility of production congestion. Additionally, evidence of the existence of a strong geographic pattern of European regional efficiency is reported and the levels of technical efficiency are acknowledged to have converged during the period under analysis.
Nonparametric Monitoring for Geotechnical Structures Subject to Long-Term Environmental Change
Directory of Open Access Journals (Sweden)
Hae-Bum Yun
2011-01-01
Full Text Available A nonparametric, data-driven methodology of monitoring for geotechnical structures subject to long-term environmental change is discussed. Avoiding physical assumptions or excessive simplification of the monitored structures, the nonparametric monitoring methodology presented in this paper provides reliable performance-related information particularly when the collection of sensor data is limited. For the validation of the nonparametric methodology, a field case study was performed using a full-scale retaining wall, which had been monitored for three years using three tilt gauges. Using the very limited sensor data, it is demonstrated that important performance-related information, such as drainage performance and sensor damage, could be disentangled from significant daily, seasonal and multiyear environmental variations. Extensive literature review on recent developments of parametric and nonparametric data processing techniques for geotechnical applications is also presented.
Kernel bandwidth estimation for non-parametric density estimation: a comparative study
CSIR Research Space (South Africa)
Van der Walt, CM
2013-12-01
Full Text Available We investigate the performance of conventional bandwidth estimators for non-parametric kernel density estimation on a number of representative pattern-recognition tasks, to gain a better understanding of the behaviour of these estimators in high...
NParCov3: A SAS/IML Macro for Nonparametric Randomization-Based Analysis of Covariance
Directory of Open Access Journals (Sweden)
Richard C. Zink
2012-07-01
Full Text Available Analysis of covariance serves two important purposes in a randomized clinical trial. First, there is a reduction of variance for the treatment effect which provides more powerful statistical tests and more precise confidence intervals. Second, it provides estimates of the treatment effect which are adjusted for random imbalances of covariates between the treatment groups. The nonparametric analysis of covariance method of Koch, Tangen, Jung, and Amara (1998 defines a very general methodology using weighted least-squares to generate covariate-adjusted treatment effects with minimal assumptions. This methodology is general in its applicability to a variety of outcomes, whether continuous, binary, ordinal, incidence density or time-to-event. Further, its use has been illustrated in many clinical trial settings, such as multi-center, dose-response and non-inferiority trials.NParCov3 is a SAS/IML macro written to conduct the nonparametric randomization-based covariance analyses of Koch et al. (1998. The software can analyze a variety of outcomes and can account for stratification. Data from multiple clinical trials will be used for illustration.
Missing Linkages in California's Landscape [ds420
California Natural Resource Agency — The critical need for conserving landscape linkages first came to the forefront of conservation thinking in California in November 2000, when a statewide interagency...
Multiobjective optimization of a steering linkage
Energy Technology Data Exchange (ETDEWEB)
Sleesonsom, S.; Bureerat, S. [Sustainable and Infrastructure Research and Development Center, Dept. of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen (Thailand)
2016-08-15
In this paper, multi-objective optimization of a rack-and-pinion steering linkage is proposed. This steering linkage is a common mechanism used in small cars with three advantages as it is simple to construct, economical to manufacture, and compact and easy to operate. In the previous works, many researchers tried to minimize a steering error but minimization of a turning radius is somewhat ignored. As a result, a multi-objective optimization problem is assigned to simultaneously minimize a steering error and a turning radius. The design variables are linkage dimensions. The design problem is solved by the hybrid of multi-objective population-based incremental learning and differential evolution with various constraint handling schemes. The new design strategy leads to effective design of rack-and-pinion steering linkages satisfying both steering error and turning radius criteria.
EDITORIAL Development Linkages between Tree Breeding ...
African Journals Online (AJOL)
EDITORIAL Development Linkages between Tree Breeding Programmes and National/Regional Tree Seed Centres in Africa. ... Discovery and Innovation. Journal Home · ABOUT THIS JOURNAL · Advanced Search · Current Issue · Archives.
Missing Linkages in California's Landscape [ds420
California Department of Resources — The critical need for conserving landscape linkages first came to the forefront of conservation thinking in California in November 2000, when a statewide interagency...
Multiobjective optimization of a steering linkage
International Nuclear Information System (INIS)
Sleesonsom, S.; Bureerat, S.
2016-01-01
In this paper, multi-objective optimization of a rack-and-pinion steering linkage is proposed. This steering linkage is a common mechanism used in small cars with three advantages as it is simple to construct, economical to manufacture, and compact and easy to operate. In the previous works, many researchers tried to minimize a steering error but minimization of a turning radius is somewhat ignored. As a result, a multi-objective optimization problem is assigned to simultaneously minimize a steering error and a turning radius. The design variables are linkage dimensions. The design problem is solved by the hybrid of multi-objective population-based incremental learning and differential evolution with various constraint handling schemes. The new design strategy leads to effective design of rack-and-pinion steering linkages satisfying both steering error and turning radius criteria
Examples of the Application of Nonparametric Information Geometry to Statistical Physics
Directory of Open Access Journals (Sweden)
Giovanni Pistone
2013-09-01
Full Text Available We review a nonparametric version of Amari’s information geometry in which the set of positive probability densities on a given sample space is endowed with an atlas of charts to form a differentiable manifold modeled on Orlicz Banach spaces. This nonparametric setting is used to discuss the setting of typical problems in machine learning and statistical physics, such as black-box optimization, Kullback-Leibler divergence, Boltzmann-Gibbs entropy and the Boltzmann equation.
Screen Wars, Star Wars, and Sequels: Nonparametric Reanalysis of Movie Profitability
W. D. Walls
2012-01-01
In this paper we use nonparametric statistical tools to quantify motion-picture profit. We quantify the unconditional distribution of profit, the distribution of profit conditional on stars and sequels, and we also model the conditional expectation of movie profits using a non- parametric data-driven regression model. The flexibility of the non-parametric approach accommodates the full range of possible relationships among the variables without prior specification of a functional form, thereb...
Froelich, Markus; Puhani, Patrick
2004-01-01
Based on a nonparametrically estimated model of labor market classifications, this paper makes suggestions for immigration policy using data from western Germany in the 1990s. It is demonstrated that nonparametric regression is feasible in higher dimensions with only a few thousand observations. In sum, labor markets able to absorb immigrants are characterized by above average age and by professional occupations. On the other hand, labor markets for young workers in service occupations are id...
Nonparametric Identification and Estimation of Finite Mixture Models of Dynamic Discrete Choices
Hiroyuki Kasahara; Katsumi Shimotsu
2006-01-01
In dynamic discrete choice analysis, controlling for unobserved heterogeneity is an important issue, and finite mixture models provide flexible ways to account for unobserved heterogeneity. This paper studies nonparametric identifiability of type probabilities and type-specific component distributions in finite mixture models of dynamic discrete choices. We derive sufficient conditions for nonparametric identification for various finite mixture models of dynamic discrete choices used in appli...
Asian Financial Linkages: The Case of Japan
Fialová, Anežka
2014-01-01
This work reviews the topic of international financial linkages, including theoretical definitions and the main methodological approaches of the empirical measurement based on vector autoregressive models. One of the approaches, the Spillover Index methodology based on Diebold & Yilmaz (2009), is then used to analyze the developments of financial linkages of the Japanese stock market in the period from 1995 to 2012. The attention is paid both to the relations with western developed economies ...
Nonparametric Change Point Diagnosis Method of Concrete Dam Crack Behavior Abnormality
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Zhanchao Li
2013-01-01
Full Text Available The study on diagnosis method of concrete crack behavior abnormality has always been a hot spot and difficulty in the safety monitoring field of hydraulic structure. Based on the performance of concrete dam crack behavior abnormality in parametric statistical model and nonparametric statistical model, the internal relation between concrete dam crack behavior abnormality and statistical change point theory is deeply analyzed from the model structure instability of parametric statistical model and change of sequence distribution law of nonparametric statistical model. On this basis, through the reduction of change point problem, the establishment of basic nonparametric change point model, and asymptotic analysis on test method of basic change point problem, the nonparametric change point diagnosis method of concrete dam crack behavior abnormality is created in consideration of the situation that in practice concrete dam crack behavior may have more abnormality points. And the nonparametric change point diagnosis method of concrete dam crack behavior abnormality is used in the actual project, demonstrating the effectiveness and scientific reasonableness of the method established. Meanwhile, the nonparametric change point diagnosis method of concrete dam crack behavior abnormality has a complete theoretical basis and strong practicality with a broad application prospect in actual project.
An adaptive distance measure for use with nonparametric models
International Nuclear Information System (INIS)
Garvey, D. R.; Hines, J. W.
2006-01-01
Distance measures perform a critical task in nonparametric, locally weighted regression. Locally weighted regression (LWR) models are a form of 'lazy learning' which construct a local model 'on the fly' by comparing a query vector to historical, exemplar vectors according to a three step process. First, the distance of the query vector to each of the exemplar vectors is calculated. Next, these distances are passed to a kernel function, which converts the distances to similarities or weights. Finally, the model output or response is calculated by performing locally weighted polynomial regression. To date, traditional distance measures, such as the Euclidean, weighted Euclidean, and L1-norm have been used as the first step in the prediction process. Since these measures do not take into consideration sensor failures and drift, they are inherently ill-suited for application to 'real world' systems. This paper describes one such LWR model, namely auto associative kernel regression (AAKR), and describes a new, Adaptive Euclidean distance measure that can be used to dynamically compensate for faulty sensor inputs. In this new distance measure, the query observations that lie outside of the training range (i.e. outside the minimum and maximum input exemplars) are dropped from the distance calculation. This allows for the distance calculation to be robust to sensor drifts and failures, in addition to providing a method for managing inputs that exceed the training range. In this paper, AAKR models using the standard and Adaptive Euclidean distance are developed and compared for the pressure system of an operating nuclear power plant. It is shown that using the standard Euclidean distance for data with failed inputs, significant errors in the AAKR predictions can result. By using the Adaptive Euclidean distance it is shown that high fidelity predictions are possible, in spite of the input failure. In fact, it is shown that with the Adaptive Euclidean distance prediction
Haplotype analysis and linkage disequilibrium for DGAT1
Strucken, Eva M.; Rahmatalla, Siham; De Koning, Dirk-Jan; Brockmann, Gudrun A.
2010-01-01
This study focused on haplotype effects and linkage disequilibrium (LD) for the K232A locus and the promoter VNTR in the DGAT1 gene. Analyses were carried out in three German Holstein Frisian populations (including 492, 305, and 518 animals) for milk yield, milk fat and protein yield, and milk fat and protein content. We found that effects of the promoter VNTR were not significant and explain only a small amount of the variation of the QTL on BTA14. Haplotype effects were less significant tha...
Dimensional threshold for fracture linkage and hooking
Lamarche, Juliette; Chabani, Arezki; Gauthier, Bertrand D. M.
2018-03-01
Fracture connectivity in rocks depends on spatial properties of the pattern including length, abundance and orientation. When fractures form a single-strike set, they hardly cross-cut each other and the connectivity is limited. Linkage probability increases with increasing fracture abundance and length as small fractures connect to each other to form longer ones. A process for parallel fracture linkage is the "hooking", where two converging fracture tips mutually deviate and then converge to connect due to the interaction of their crack-tip stresses. Quantifying the processes and conditions for fracture linkage in single-strike fracture sets is crucial to better predicting fluid flow in Naturally Fractured Reservoirs. For 1734 fractures in Permian shales of the Lodève Basin, SE France, we measured geometrical parameters in 2D, characterizing three stages of the hooking process: underlapping, overlapping and linkage. We deciphered the threshold values, shape ratios and limiting conditions to switch from one stage to another one. The hook set up depends on the spacing (S) and fracture length (Lh) with the relation S ≈ 0.15 Lh. Once the hooking is initiated, with the fracture deviation length (L) L ≈ 0.4 Lh, the fractures reaches the linkage stage only when the spacing is reduced to S ≈ 0.02 Lh and the convergence (C) is < 0.1 L. These conditions apply to multi-scale fractures with a shape ratio L/S = 10 and for fracture curvature of 10°-20°.
Prioritizing tiger conservation through landscape genetics and habitat linkages.
Yumnam, Bibek; Jhala, Yadvendradev V; Qureshi, Qamar; Maldonado, Jesus E; Gopal, Rajesh; Saini, Swati; Srinivas, Y; Fleischer, Robert C
2014-01-01
Even with global support for tiger (Panthera tigris) conservation their survival is threatened by poaching, habitat loss and isolation. Currently about 3,000 wild tigers persist in small fragmented populations within seven percent of their historic range. Identifying and securing habitat linkages that connect source populations for maintaining landscape-level gene flow is an important long-term conservation strategy for endangered carnivores. However, habitat corridors that link regional tiger populations are often lost to development projects due to lack of objective evidence on their importance. Here, we use individual based genetic analysis in combination with landscape permeability models to identify and prioritize movement corridors across seven tiger populations within the Central Indian Landscape. By using a panel of 11 microsatellites we identified 169 individual tigers from 587 scat and 17 tissue samples. We detected four genetic clusters within Central India with limited gene flow among three of them. Bayesian and likelihood analyses identified 17 tigers as having recent immigrant ancestry. Spatially explicit tiger occupancy obtained from extensive landscape-scale surveys across 76,913 km(2) of forest habitat was found to be only 21,290 km(2). After accounting for detection bias, the covariates that best explained tiger occupancy were large, remote, dense forest patches; large ungulate abundance, and low human footprint. We used tiger occupancy probability to parameterize habitat permeability for modeling habitat linkages using least-cost and circuit theory pathway analyses. Pairwise genetic differences (FST) between populations were better explained by modeled linkage costs (r>0.5, p<0.05) compared to Euclidean distances, which was in consonance with observed habitat fragmentation. The results of our study highlight that many corridors may still be functional as there is evidence of contemporary migration. Conservation efforts should provide legal status
Prioritizing tiger conservation through landscape genetics and habitat linkages.
Directory of Open Access Journals (Sweden)
Bibek Yumnam
Full Text Available Even with global support for tiger (Panthera tigris conservation their survival is threatened by poaching, habitat loss and isolation. Currently about 3,000 wild tigers persist in small fragmented populations within seven percent of their historic range. Identifying and securing habitat linkages that connect source populations for maintaining landscape-level gene flow is an important long-term conservation strategy for endangered carnivores. However, habitat corridors that link regional tiger populations are often lost to development projects due to lack of objective evidence on their importance. Here, we use individual based genetic analysis in combination with landscape permeability models to identify and prioritize movement corridors across seven tiger populations within the Central Indian Landscape. By using a panel of 11 microsatellites we identified 169 individual tigers from 587 scat and 17 tissue samples. We detected four genetic clusters within Central India with limited gene flow among three of them. Bayesian and likelihood analyses identified 17 tigers as having recent immigrant ancestry. Spatially explicit tiger occupancy obtained from extensive landscape-scale surveys across 76,913 km(2 of forest habitat was found to be only 21,290 km(2. After accounting for detection bias, the covariates that best explained tiger occupancy were large, remote, dense forest patches; large ungulate abundance, and low human footprint. We used tiger occupancy probability to parameterize habitat permeability for modeling habitat linkages using least-cost and circuit theory pathway analyses. Pairwise genetic differences (FST between populations were better explained by modeled linkage costs (r>0.5, p<0.05 compared to Euclidean distances, which was in consonance with observed habitat fragmentation. The results of our study highlight that many corridors may still be functional as there is evidence of contemporary migration. Conservation efforts should
Intragroup emotions: physiological linkage and social presence
Directory of Open Access Journals (Sweden)
Simo eJärvelä
2016-02-01
Full Text Available We investigated how technologically mediating two different components of emotion – communicative expression and physiological state – to group members affects physiological linkage and self-reported feelings in a small group during video viewing. In different conditions the availability of second screen text chat (communicative expression and visualization of group level physiological heart rates and their dyadic linkage (physiology was varied. Within this four person group two participants formed a physically co-located dyad and the other two were individually situated in two separate rooms. We found that text chat always increased heart rate synchrony but HR visualization only with non-co-located dyads. We also found that physiological linkage was strongly connected to self-reported social presence. The results encourage further exploration of the possibilities of sharing group member’s physiological components of emotion by technological means to enhance mediated communication and strengthen social presence.
Intragroup Emotions: Physiological Linkage and Social Presence.
Järvelä, Simo; Kätsyri, Jari; Ravaja, Niklas; Chanel, Guillaume; Henttonen, Pentti
2016-01-01
We investigated how technologically mediating two different components of emotion-communicative expression and physiological state-to group members affects physiological linkage and self-reported feelings in a small group during video viewing. In different conditions the availability of second screen text chat (communicative expression) and visualization of group level physiological heart rates and their dyadic linkage (physiology) was varied. Within this four person group two participants formed a physically co-located dyad and the other two were individually situated in two separate rooms. We found that text chat always increased heart rate synchrony but HR visualization only with non-co-located dyads. We also found that physiological linkage was strongly connected to self-reported social presence. The results encourage further exploration of the possibilities of sharing group member's physiological components of emotion by technological means to enhance mediated communication and strengthen social presence.
A microsatellite linkage map of Drosophila mojavensis
Directory of Open Access Journals (Sweden)
Schully Sheri
2004-05-01
Full Text Available Abstract Background Drosophila mojavensis has been a model system for genetic studies of ecological adaptation and speciation. However, despite its use for over half a century, no linkage map has been produced for this species or its close relatives. Results We have developed and mapped 90 microsatellites in D. mojavensis, and we present a detailed recombinational linkage map of 34 of these microsatellites. A slight excess of repetitive sequence was observed on the X-chromosome relative to the autosomes, and the linkage groups have a greater recombinational length than the homologous D. melanogaster chromosome arms. We also confirmed the conservation of Muller's elements in 23 sequences between D. melanogaster and D. mojavensis. Conclusions The microsatellite primer sequences and localizations are presented here and made available to the public. This map will facilitate future quantitative trait locus mapping studies of phenotypes involved in adaptation or reproductive isolation using this species.
Intragroup Emotions: Physiological Linkage and Social Presence
Järvelä, Simo; Kätsyri, Jari; Ravaja, Niklas; Chanel, Guillaume; Henttonen, Pentti
2016-01-01
We investigated how technologically mediating two different components of emotion—communicative expression and physiological state—to group members affects physiological linkage and self-reported feelings in a small group during video viewing. In different conditions the availability of second screen text chat (communicative expression) and visualization of group level physiological heart rates and their dyadic linkage (physiology) was varied. Within this four person group two participants formed a physically co-located dyad and the other two were individually situated in two separate rooms. We found that text chat always increased heart rate synchrony but HR visualization only with non-co-located dyads. We also found that physiological linkage was strongly connected to self-reported social presence. The results encourage further exploration of the possibilities of sharing group member's physiological components of emotion by technological means to enhance mediated communication and strengthen social presence. PMID:26903913
Bioprocess iterative batch-to-batch optimization based on hybrid parametric/nonparametric models.
Teixeira, Ana P; Clemente, João J; Cunha, António E; Carrondo, Manuel J T; Oliveira, Rui
2006-01-01
This paper presents a novel method for iterative batch-to-batch dynamic optimization of bioprocesses. The relationship between process performance and control inputs is established by means of hybrid grey-box models combining parametric and nonparametric structures. The bioreactor dynamics are defined by material balance equations, whereas the cell population subsystem is represented by an adjustable mixture of nonparametric and parametric models. Thus optimizations are possible without detailed mechanistic knowledge concerning the biological system. A clustering technique is used to supervise the reliability of the nonparametric subsystem during the optimization. Whenever the nonparametric outputs are unreliable, the objective function is penalized. The technique was evaluated with three simulation case studies. The overall results suggest that the convergence to the optimal process performance may be achieved after a small number of batches. The model unreliability risk constraint along with sampling scheduling are crucial to minimize the experimental effort required to attain a given process performance. In general terms, it may be concluded that the proposed method broadens the application of the hybrid parametric/nonparametric modeling technique to "newer" processes with higher potential for optimization.
Some methods for blindfolded record linkage
Directory of Open Access Journals (Sweden)
Christen Peter
2004-06-01
Full Text Available Abstract Background The linkage of records which refer to the same entity in separate data collections is a common requirement in public health and biomedical research. Traditionally, record linkage techniques have required that all the identifying data in which links are sought be revealed to at least one party, often a third party. This necessarily invades personal privacy and requires complete trust in the intentions of that party and their ability to maintain security and confidentiality. Dusserre, Quantin, Bouzelat and colleagues have demonstrated that it is possible to use secure one-way hash transformations to carry out follow-up epidemiological studies without any party having to reveal identifying information about any of the subjects – a technique which we refer to as "blindfolded record linkage". A limitation of their method is that only exact comparisons of values are possible, although phonetic encoding of names and other strings can be used to allow for some types of typographical variation and data errors. Methods A method is described which permits the calculation of a general similarity measure, the n-gram score, without having to reveal the data being compared, albeit at some cost in computation and data communication. This method can be combined with public key cryptography and automatic estimation of linkage model parameters to create an overall system for blindfolded record linkage. Results The system described offers good protection against misdeeds or security failures by any one party, but remains vulnerable to collusion between or simultaneous compromise of two or more parties involved in the linkage operation. In order to reduce the likelihood of this, the use of last-minute allocation of tasks to substitutable servers is proposed. Proof-of-concept computer programmes written in the Python programming language are provided to illustrate the similarity comparison protocol. Conclusion Although the protocols described in
The Barley Chromosome 5 Linkage Map
DEFF Research Database (Denmark)
Jensen, J.; Jørgensen, Jørgen Helms
1975-01-01
The distances between nine loci on barley chromosome 5 have been studied in five two-point tests, three three-point tests, and one four-point test. Our previous chromosome 5 linkage map, which contained eleven loci mapped from literature data (Jensen and Jørgensen 1975), is extended with four loci......-position is fixed on the map by a locus (necl), which has a good marker gene located centrally in the linkage group. The positions of the other loci are their distances in centimorgans from the 0-position; loci in the direction of the short chromosome arm are assigned positive values and those...
An Evaluation of Parametric and Nonparametric Models of Fish Population Response.
Energy Technology Data Exchange (ETDEWEB)
Haas, Timothy C.; Peterson, James T.; Lee, Danny C.
1999-11-01
Predicting the distribution or status of animal populations at large scales often requires the use of broad-scale information describing landforms, climate, vegetation, etc. These data, however, often consist of mixtures of continuous and categorical covariates and nonmultiplicative interactions among covariates, complicating statistical analyses. Using data from the interior Columbia River Basin, USA, we compared four methods for predicting the distribution of seven salmonid taxa using landscape information. Subwatersheds (mean size, 7800 ha) were characterized using a set of 12 covariates describing physiography, vegetation, and current land-use. The techniques included generalized logit modeling, classification trees, a nearest neighbor technique, and a modular neural network. We evaluated model performance using out-of-sample prediction accuracy via leave-one-out cross-validation and introduce a computer-intensive Monte Carlo hypothesis testing approach for examining the statistical significance of landscape covariates with the non-parametric methods. We found the modular neural network and the nearest-neighbor techniques to be the most accurate, but were difficult to summarize in ways that provided ecological insight. The modular neural network also required the most extensive computer resources for model fitting and hypothesis testing. The generalized logit models were readily interpretable, but were the least accurate, possibly due to nonlinear relationships and nonmultiplicative interactions among covariates. Substantial overlap among the statistically significant (P<0.05) covariates for each method suggested that each is capable of detecting similar relationships between responses and covariates. Consequently, we believe that employing one or more methods may provide greater biological insight without sacrificing prediction accuracy.
Trends in three decades of HIV/AIDS epidemic in Thailand by nonparametric backcalculation method.
Punyacharoensin, Narat; Viwatwongkasem, Chukiat
2009-06-01
To reconstruct the past HIV incidence and prevalence in Thailand from 1980 to 2008 and predict the country's AIDS incidence from 2009 to 2011. Nonparametric backcalculation was adopted utilizing 100 quarterly observed new AIDS counts excluding pediatric cases. The accuracy of data was enhanced through a series of data adjustments using the weight method to account for several surveillance reporting issues. The mixture of time-dependent distributions allowed the effects of age at seroconversion and antiretroviral therapy to be incorporated simultaneously. Sensitivity analyses were conducted to assess model variations that were subject to major uncertainties. Future AIDS incidence was projected for various predetermined HIV incidence patterns. HIV incidence in Thailand reached its peak in 1992 with approximately 115,000 cases. A steep decline thereafter discontinued in 1997 and was followed by another strike of 42,000 cases in 1999. The second surge, which happened concurrently with the major economic crisis, brought on 60,000 new infections. As of December 2008, more than 1 million individuals had been infected and around 430,000 adults were living with HIV corresponding to a prevalence rate of 1.2%. The incidence rate had become less than 0.1% since 2002. The backcalculated estimates were dominated by postulated median AIDS progression time and adjustments to surveillance data. Our analysis indicated that, thus far, the 1990s was the most severe era of HIV/AIDS epidemic in Thailand with two HIV incidence peaks. A drop in new infections led to a decrease in recent AIDS incidence, and this tendency is likely to remain unchanged until 2011, if not further.
Nonparametric Bayesian density estimation on manifolds with applications to planar shapes.
Bhattacharya, Abhishek; Dunson, David B
2010-12-01
Statistical analysis on landmark-based shape spaces has diverse applications in morphometrics, medical diagnostics, machine vision and other areas. These shape spaces are non-Euclidean quotient manifolds. To conduct nonparametric inferences, one may define notions of centre and spread on this manifold and work with their estimates. However, it is useful to consider full likelihood-based methods, which allow nonparametric estimation of the probability density. This article proposes a broad class of mixture models constructed using suitable kernels on a general compact metric space and then on the planar shape space in particular. Following a Bayesian approach with a nonparametric prior on the mixing distribution, conditions are obtained under which the Kullback-Leibler property holds, implying large support and weak posterior consistency. Gibbs sampling methods are developed for posterior computation, and the methods are applied to problems in density estimation and classification with shape-based predictors. Simulation studies show improved estimation performance relative to existing approaches.
DEFF Research Database (Denmark)
Brazauskas, Gintaras; Pašakinskienė, Izolda; Asp, Torben
2010-01-01
Knowledge on nucleotide diversity and linkage disequilibrium (LD) patterns is prerequisite for association analyses. However, little is known about the nucleotide diversity in the evolutionary important ryegrass shoot morphology genes. Five candidate genes, LpIAA1, LpRUB1, LpBRI1, LpSHOOT1 and Lp...
Industry-academe linkages in the Philippines: Embedding foreign investors, capturing institutions?
Kleibert, J.M.
2015-01-01
The global sourcing of services from developing countries has made human capital a key local asset for attracting foreign direct investment. This paper analyses to what extent individual companies engage in linkages and collaborations with universities and how the education sector responds to the
Zhao, Zhibiao
2011-06-01
We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.
Multivariate nonparametric regression and visualization with R and applications to finance
Klemelä, Jussi
2014-01-01
A modern approach to statistical learning and its applications through visualization methods With a unique and innovative presentation, Multivariate Nonparametric Regression and Visualization provides readers with the core statistical concepts to obtain complete and accurate predictions when given a set of data. Focusing on nonparametric methods to adapt to the multiple types of data generatingmechanisms, the book begins with an overview of classification and regression. The book then introduces and examines various tested and proven visualization techniques for learning samples and functio
Directory of Open Access Journals (Sweden)
Rabia Ece OMAY
2013-06-01
Full Text Available In this study, relationship between gross domestic product (GDP per capita and sulfur dioxide (SO2 and particulate matter (PM10 per capita is modeled for Turkey. Nonparametric fixed effect panel data analysis is used for the modeling. The panel data covers 12 territories, in first level of Nomenclature of Territorial Units for Statistics (NUTS, for period of 1990-2001. Modeling of the relationship between GDP and SO2 and PM10 for Turkey, the non-parametric models have given good results.
Nonparametric method for failures diagnosis in the actuating subsystem of aircraft control system
Terentev, M. N.; Karpenko, S. S.; Zybin, E. Yu; Kosyanchuk, V. V.
2018-02-01
In this paper we design a nonparametric method for failures diagnosis in the aircraft control system that uses the measurements of the control signals and the aircraft states only. It doesn’t require a priori information of the aircraft model parameters, training or statistical calculations, and is based on analytical nonparametric one-step-ahead state prediction approach. This makes it possible to predict the behavior of unidentified and failure dynamic systems, to weaken the requirements to control signals, and to reduce the diagnostic time and problem complexity.
Feng, Jinchao; Lansford, Joshua; Mironenko, Alexander; Pourkargar, Davood Babaei; Vlachos, Dionisios G.; Katsoulakis, Markos A.
2018-03-01
We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data). The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.
Directory of Open Access Journals (Sweden)
Jinchao Feng
2018-03-01
Full Text Available We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data. The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.
A Bayesian approach to the analysis of quantal bioassay studies using nonparametric mixture models.
Fronczyk, Kassandra; Kottas, Athanasios
2014-03-01
We develop a Bayesian nonparametric mixture modeling framework for quantal bioassay settings. The approach is built upon modeling dose-dependent response distributions. We adopt a structured nonparametric prior mixture model, which induces a monotonicity restriction for the dose-response curve. Particular emphasis is placed on the key risk assessment goal of calibration for the dose level that corresponds to a specified response. The proposed methodology yields flexible inference for the dose-response relationship as well as for other inferential objectives, as illustrated with two data sets from the literature. © 2013, The International Biometric Society.
Modern nonparametric, robust and multivariate methods festschrift in honour of Hannu Oja
Taskinen, Sara
2015-01-01
Written by leading experts in the field, this edited volume brings together the latest findings in the area of nonparametric, robust and multivariate statistical methods. The individual contributions cover a wide variety of topics ranging from univariate nonparametric methods to robust methods for complex data structures. Some examples from statistical signal processing are also given. The volume is dedicated to Hannu Oja on the occasion of his 65th birthday and is intended for researchers as well as PhD students with a good knowledge of statistics.
Linkage disequilibrium and association mapping of drought ...
African Journals Online (AJOL)
Drought stress is a major abiotic stress that limits crop production. Molecular association mapping techniques through linkage disequilibrium (LD) can be effectively used to tag genomic regions involved in drought stress tolerance. With the association mapping approach, 90 genotypes of cotton Gossypium hirsutum, from ...
principles, realities and challenges regarding institutional linkages ...
African Journals Online (AJOL)
p2333147
(2) Compromise between proximity to community and effective coordination. If organisational linkage structures are to facilitate effective participation and ownership, it stands to reason that they should be as close to the grassroots community as possible. Unless community members regard such organisational structures as.
Karabatsos, George
2017-02-01
Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected
Salmonid Chromosome Evolution as Revealed by a Novel Method for Comparing RADseq Linkage Maps
Gosselin, Thierry; Normandeau, Eric; Lamothe, Manuel; Isabel, Nathalie; Audet, Céline; Bernatchez, Louis
2016-01-01
Whole genome duplication (WGD) can provide material for evolutionary innovation. Family Salmonidae is ideal for studying the effects of WGD as the ancestral salmonid underwent WGD relatively recently, ∼65 Ma, then rediploidized and diversified. Extensive synteny between homologous chromosome arms occurs in extant salmonids, but each species has both conserved and unique chromosome arm fusions and fissions. Assembly of large, outbred eukaryotic genomes can be difficult, but structural rearrangements within such taxa can be investigated using linkage maps. RAD sequencing provides unprecedented ability to generate high-density linkage maps for nonmodel species, but can result in low numbers of homologous markers between species due to phylogenetic distance or differences in library preparation. Here, we generate a high-density linkage map (3,826 markers) for the Salvelinus genera (Brook Charr S. fontinalis), and then identify corresponding chromosome arms among the other available salmonid high-density linkage maps, including six species of Oncorhynchus, and one species for each of Salmo, Coregonus, and the nonduplicated sister group for the salmonids, Northern Pike Esox lucius for identifying post-duplicated homeologs. To facilitate this process, we developed MapComp to identify identical and proximate (i.e. nearby) markers between linkage maps using a reference genome of a related species as an intermediate, increasing the number of comparable markers between linkage maps by 5-fold. This enabled a characterization of the most likely history of retained chromosomal rearrangements post-WGD, and several conserved chromosomal inversions. Analyses of RADseq-based linkage maps from other taxa will also benefit from MapComp, available at: https://github.com/enormandeau/mapcomp/ PMID:28173098
African American church-based HIV testing and linkage to care: assets, challenges and needs.
Stewart, Jennifer M; Thompson, Keitra; Rogers, Christopher
2016-01-01
The US National HIV AIDS strategy promotes the use of faith communities to lessen the burden of HIV in African American communities. One specific strategy presented is the use of these non-traditional venues for HIV testing and co-location of services. African American churches can be at the forefront of this endeavour through the provision of HIV testing and linkage to care. However, there are few interventions to promote the churches' involvement in both HIV testing and linkage to care. We conducted 4 focus groups (n = 39 participants), 4 interviews and 116 surveys in a mixed-methods study to examine the feasibility of a church-based HIV testing and linkage to care intervention in Philadelphia, PA, USA. Our objectives were to examine: (1) available assets, (2) challenges and barriers and (3) needs associated with church-based HIV testing and linkage to care. Analyses revealed several factors of importance, including the role of the church as an access point for testing in low-income neighbourhoods, challenges in openly discussing the relationship between sexuality and HIV, and buy-in among church leadership. These findings can support intervention development and necessitate situating African American church-based HIV testing and linkage to care interventions within a multi-level framework.
Linkage studies on Gilles de la Tourette syndrome: What is the strategy of choice?
Energy Technology Data Exchange (ETDEWEB)
Heutink, P.; Wetering, J.M. van de; Oostra, B.A. [Erasmus Univ. Rotterdam (Netherlands)] [and others
1995-08-01
For a linkage study it is important to ascertain family material that is sufficiently informative. The statistical power of linkage sample can be determined via computer simulation. For complex traits uncertain parameters such as incomplete penetrance, frequency of phenocopies, gene frequency and variable expression have to be taken into account. One can either include only the most severe phenotype in the analysis or apply multiple linkage tests for a gradually broadened disease phenotype. Gilles de la Tourette syndrome (GTS) is a chronic neurological disorder characterized by multiple, intermittent motor and vocal tics. Segregation analyses suggests that GTS and milder phenotypes are caused by a single dominant gene. We report here the results of an extensive simulation study on a large set of families. We compared the effectiveness of linkage tests with only the GTS phenotype versus multiple tests that included various milder phenotypes and different gene frequencies. The scenario of multiple tests yielded superior power. Our results show that computer simulation can indicate the strategy of choice in linkage studies of multiple, complex phenotypes. 33 refs., 2 figs., 3 tabs.
Supremum Norm Posterior Contraction and Credible Sets for Nonparametric Multivariate Regression
Yoo, W.W.; Ghosal, S
2016-01-01
In the setting of nonparametric multivariate regression with unknown error variance, we study asymptotic properties of a Bayesian method for estimating a regression function f and its mixed partial derivatives. We use a random series of tensor product of B-splines with normal basis coefficients as a
Does Private Tutoring Work? The Effectiveness of Private Tutoring: A Nonparametric Bounds Analysis
Hof, Stefanie
2014-01-01
Private tutoring has become popular throughout the world. However, evidence for the effect of private tutoring on students' academic outcome is inconclusive; therefore, this paper presents an alternative framework: a nonparametric bounds method. The present examination uses, for the first time, a large representative data-set in a European setting…
Testing a parametric function against a nonparametric alternative in IV and GMM settings
DEFF Research Database (Denmark)
Gørgens, Tue; Wurtz, Allan
This paper develops a specification test for functional form for models identified by moment restrictions, including IV and GMM settings. The general framework is one where the moment restrictions are specified as functions of data, a finite-dimensional parameter vector, and a nonparametric real ...
A structural nonparametric reappraisal of the CO2 emissions-income relationship
Azomahou, T.T.; Goedhuys - Degelin, Micheline; Nguyen-Van, P.
Relying on a structural nonparametric estimation, we show that co2 emissions clearly increase with income at low income levels. For higher income levels, we observe a decreasing relationship, though not significant. We also find thatco2 emissions monotonically increases with energy use at a
Wei, Jiawei
2011-07-01
We consider the problem of testing for a constant nonparametric effect in a general semi-parametric regression model when there is the potential for interaction between the parametrically and nonparametrically modeled variables. The work was originally motivated by a unique testing problem in genetic epidemiology (Chatterjee, et al., 2006) that involved a typical generalized linear model but with an additional term reminiscent of the Tukey one-degree-of-freedom formulation, and their interest was in testing for main effects of the genetic variables, while gaining statistical power by allowing for a possible interaction between genes and the environment. Later work (Maity, et al., 2009) involved the possibility of modeling the environmental variable nonparametrically, but they focused on whether there was a parametric main effect for the genetic variables. In this paper, we consider the complementary problem, where the interest is in testing for the main effect of the nonparametrically modeled environmental variable. We derive a generalized likelihood ratio test for this hypothesis, show how to implement it, and provide evidence that our method can improve statistical power when compared to standard partially linear models with main effects only. We use the method for the primary purpose of analyzing data from a case-control study of colorectal adenoma.
Assessing pupil and school performance by non-parametric and parametric techniques
de Witte, K.; Thanassoulis, E.; Simpson, G.; Battisti, G.; Charlesworth-May, A.
2010-01-01
This paper discusses the use of the non-parametric free disposal hull (FDH) and the parametric multi-level model (MLM) as alternative methods for measuring pupil and school attainment where hierarchical structured data are available. Using robust FDH estimates, we show how to decompose the overall
Nonparametric Estimation of Interval Reliability for Discrete-Time Semi-Markov Systems
DEFF Research Database (Denmark)
Georgiadis, Stylianos; Limnios, Nikolaos
2016-01-01
In this article, we consider a repairable discrete-time semi-Markov system with finite state space. The measure of the interval reliability is given as the probability of the system being operational over a given finite-length time interval. A nonparametric estimator is proposed for the interval...
Low default credit scoring using two-class non-parametric kernel density estimation
CSIR Research Space (South Africa)
Rademeyer, E
2016-12-01
Full Text Available This paper investigates the performance of two-class classification credit scoring data sets with low default ratios. The standard two-class parametric Gaussian and non-parametric Parzen classifiers are extended, using Bayes’ rule, to include either...
DEFF Research Database (Denmark)
Ramirez, José Rangel; Sørensen, John Dalsgaard
2011-01-01
This work illustrates the updating and incorporation of information in the assessment of fatigue reliability for offshore wind turbine. The new information, coming from external and condition monitoring can be used to direct updating of the stochastic variables through a non-parametric Bayesian u...
Non-parametric production analysis of pesticides use in the Netherlands
Oude Lansink, A.G.J.M.; Silva, E.
2004-01-01
Many previous empirical studies on the productivity of pesticides suggest that pesticides are under-utilized in agriculture despite the general held believe that these inputs are substantially over-utilized. This paper uses data envelopment analysis (DEA) to calculate non-parametric measures of the
Analyzing cost efficient production behavior under economies of scope : A nonparametric methodology
Cherchye, L.J.H.; de Rock, B.; Vermeulen, F.M.P.
2008-01-01
In designing a production model for firms that generate multiple outputs, we take as a starting point that such multioutput production refers to economies of scope, which in turn originate from joint input use and input externalities. We provide a nonparametric characterization of cost-efficient
Analyzing Cost Efficient Production Behavior Under Economies of Scope : A Nonparametric Methodology
Cherchye, L.J.H.; de Rock, B.; Vermeulen, F.M.P.
2006-01-01
In designing a production model for firms that generate multiple outputs, we take as a starting point that such multi-output production refers to economies of scope, which in turn originate from joint input use and input externalities. We provide a nonparametric characterization of cost efficient
The Support Reduction Algorithm for Computing Non-Parametric Function Estimates in Mixture Models
GROENEBOOM, PIET; JONGBLOED, GEURT; WELLNER, JON A.
2008-01-01
In this paper, we study an algorithm (which we call the support reduction algorithm) that can be used to compute non-parametric M-estimators in mixture models. The algorithm is compared with natural competitors in the context of convex regression and the ‘Aspect problem’ in quantum physics.
A Bayesian Beta-Mixture Model for Nonparametric IRT (BBM-IRT)
Arenson, Ethan A.; Karabatsos, George
2017-01-01
Item response models typically assume that the item characteristic (step) curves follow a logistic or normal cumulative distribution function, which are strictly monotone functions of person test ability. Such assumptions can be overly-restrictive for real item response data. We propose a simple and more flexible Bayesian nonparametric IRT model…
Non-parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
DEFF Research Database (Denmark)
Høg, Esben
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean-reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
Non-Parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods
DEFF Research Database (Denmark)
Høg, Esben
2003-01-01
In continuous time, diffusion processes have been used for modelling financial dynamics for a long time. For example the Ornstein-Uhlenbeck process (the simplest mean--reverting process) has been used to model non-speculative price processes. We discuss non--parametric estimation of these processes...
A non-parametric Bayesian approach to decompounding from high frequency data
Gugushvili, Shota; van der Meulen, F.H.; Spreij, Peter
2016-01-01
Given a sample from a discretely observed compound Poisson process, we consider non-parametric estimation of the density f0 of its jump sizes, as well as of its intensity λ0. We take a Bayesian approach to the problem and specify the prior on f0 as the Dirichlet location mixture of normal densities.
Mittag, Kathleen Cage
Most researchers using factor analysis extract factors from a matrix of Pearson product-moment correlation coefficients. A method is presented for extracting factors in a non-parametric way, by extracting factors from a matrix of Spearman rho (rank correlation) coefficients. It is possible to factor analyze a matrix of association such that…
Nonparametric estimation of the stationary M/G/1 workload distribution function
DEFF Research Database (Denmark)
Hansen, Martin Bøgsted
2005-01-01
In this paper it is demonstrated how a nonparametric estimator of the stationary workload distribution function of the M/G/1-queue can be obtained by systematic sampling the workload process. Weak convergence results and bootstrap methods for empirical distribution functions for stationary associ...
A non-parametric method for correction of global radiation observations
DEFF Research Database (Denmark)
Bacher, Peder; Madsen, Henrik; Perers, Bengt
2013-01-01
in the observations are corrected. These are errors such as: tilt in the leveling of the sensor, shadowing from surrounding objects, clipping and saturation in the signal processing, and errors from dirt and wear. The method is based on a statistical non-parametric clear-sky model which is applied to both...
Nonparametric estimation in an "illness-death" model when all transition times are interval censored
DEFF Research Database (Denmark)
Frydman, Halina; Gerds, Thomas; Grøn, Randi
2013-01-01
We develop nonparametric maximum likelihood estimation for the parameters of an irreversible Markov chain on states {0,1,2} from the observations with interval censored times of 0 → 1, 0 → 2 and 1 → 2 transitions. The distinguishing aspect of the data is that, in addition to all transition times ...
Non-parametric Tuning of PID Controllers A Modified Relay-Feedback-Test Approach
Boiko, Igor
2013-01-01
The relay feedback test (RFT) has become a popular and efficient tool used in process identification and automatic controller tuning. Non-parametric Tuning of PID Controllers couples new modifications of classical RFT with application-specific optimal tuning rules to form a non-parametric method of test-and-tuning. Test and tuning are coordinated through a set of common parameters so that a PID controller can obtain the desired gain or phase margins in a system exactly, even with unknown process dynamics. The concept of process-specific optimal tuning rules in the nonparametric setup, with corresponding tuning rules for flow, level pressure, and temperature control loops is presented in the text. Common problems of tuning accuracy based on parametric and non-parametric approaches are addressed. In addition, the text treats the parametric approach to tuning based on the modified RFT approach and the exact model of oscillations in the system under test using the locus of a perturbedrelay system (LPRS) meth...
A comparative study of non-parametric models for identification of ...
African Journals Online (AJOL)
However, the frequency response method using random binary signals was good for unpredicted white noise characteristics and considered the best method for non-parametric system identifica-tion. The autoregressive external input (ARX) model was very useful for system identification, but on applicati-on, few input ...
A non-parametric hierarchical model to discover behavior dynamics from tracks
Kooij, J.F.P.; Englebienne, G.; Gavrila, D.M.
2012-01-01
We present a novel non-parametric Bayesian model to jointly discover the dynamics of low-level actions and high-level behaviors of tracked people in open environments. Our model represents behaviors as Markov chains of actions which capture high-level temporal dynamics. Actions may be shared by
Verrelst, Jochem; Rivera, Juan Pablo; Veroustraete, Frank; Muñoz-Marí, Jordi; Clevers, J.G.P.W.; Camps-Valls, Gustau; Moreno, José
2015-01-01
Given the forthcoming availability of Sentinel-2 (S2) images, this paper provides a systematic comparison of retrieval accuracy and processing speed of a multitude of parametric, non-parametric and physically-based retrieval methods using simulated S2 data. An experimental field dataset (SPARC),
International Nuclear Information System (INIS)
Janurová, Kateřina; Briš, Radim
2014-01-01
Medical survival right-censored data of about 850 patients are evaluated to analyze the uncertainty related to the risk of mortality on one hand and compare two basic surgery techniques in the context of risk of mortality on the other hand. Colorectal data come from patients who underwent colectomy in the University Hospital of Ostrava. Two basic surgery operating techniques are used for the colectomy: either traditional (open) or minimally invasive (laparoscopic). Basic question arising at the colectomy operation is, which type of operation to choose to guarantee longer overall survival time. Two non-parametric approaches have been used to quantify probability of mortality with uncertainties. In fact, complement of the probability to one, i.e. survival function with corresponding confidence levels is calculated and evaluated. First approach considers standard nonparametric estimators resulting from both the Kaplan–Meier estimator of survival function in connection with Greenwood's formula and the Nelson–Aalen estimator of cumulative hazard function including confidence interval for survival function as well. The second innovative approach, represented by Nonparametric Predictive Inference (NPI), uses lower and upper probabilities for quantifying uncertainty and provides a model of predictive survival function instead of the population survival function. The traditional log-rank test on one hand and the nonparametric predictive comparison of two groups of lifetime data on the other hand have been compared to evaluate risk of mortality in the context of mentioned surgery techniques. The size of the difference between two groups of lifetime data has been considered and analyzed as well. Both nonparametric approaches led to the same conclusion, that the minimally invasive operating technique guarantees the patient significantly longer survival time in comparison with the traditional operating technique
Chuan, Zun Liang; Ismail, Noriszura; Shinyie, Wendy Ling; Lit Ken, Tan; Fam, Soo-Fen; Senawi, Azlyna; Yusoff, Wan Nur Syahidah Wan
2018-04-01
Due to the limited of historical precipitation records, agglomerative hierarchical clustering algorithms widely used to extrapolate information from gauged to ungauged precipitation catchments in yielding a more reliable projection of extreme hydro-meteorological events such as extreme precipitation events. However, identifying the optimum number of homogeneous precipitation catchments accurately based on the dendrogram resulted using agglomerative hierarchical algorithms are very subjective. The main objective of this study is to propose an efficient regionalized algorithm to identify the homogeneous precipitation catchments for non-stationary precipitation time series. The homogeneous precipitation catchments are identified using average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling, while uncentered correlation coefficient as the similarity measure. The regionalized homogeneous precipitation is consolidated using K-sample Anderson Darling non-parametric test. The analysis result shows the proposed regionalized algorithm performed more better compared to the proposed agglomerative hierarchical clustering algorithm in previous studies.
Kempe's Linkages and the Universality Theorem
Indian Academy of Sciences (India)
sriranga
that between BO and OA (i.e., \\BOA). Let these two angles be denoted by µ. Thus, if link OA makes an angle. µ with link OB, OE makes the same angle on the other side of OB. Kempe therefore referred to this linkage as the (angle) reversor. This is true irrespective of how OA and OB are placed relative to each other. Also ...
Methods for genetic linkage analysis using trisomies.
Feingold, E; Lamb, N E; Sherman, S L
1995-01-01
Certain genetic disorders are rare in the general population, but more common in individuals with specific trisomies. Examples of this include leukemia and duodenal atresia in trisomy 21. This paper presents a linkage analysis method for using trisomic individuals to map genes for such traits. It is based on a very general gene-specific dosage model that posits that the trait is caused by specific effects of different alleles at one or a few loci and that duplicate copies of "susceptibility" ...
Hidden linkages between urbanization and food systems.
Seto, Karen C; Ramankutty, Navin
2016-05-20
Global societies are becoming increasingly urban. This shift toward urban living is changing our relationship with food, including how we shop and what we buy, as well as ideas about sanitation and freshness. Achieving food security in an era of rapid urbanization will require considerably more understanding about how urban and food systems are intertwined. Here we discuss some potential understudied linkages that are ripe for further examination. Copyright © 2016, American Association for the Advancement of Science.
'Linkage' pharmaceutical evergreening in Canada and Australia
Faunce, Thomas A; Lexchin, Joel
2007-01-01
'Evergreening' is not a formal concept of patent law. It is best understood as a social idea used to refer to the myriad ways in which pharmaceutical patent owners utilise the law and related regulatory processes to extend their high rent-earning intellectual monopoly privileges, particularly over highly profitable (either in total sales volume or price per unit) 'blockbuster' drugs. Thus, while the courts are an instrument frequently used by pharmaceutical brand name manufacturers to prolong their patent royalties, 'evergreening' is rarely mentioned explicitly by judges in patent protection cases. The term usually refers to threats made to competitors about a brand-name manufacturer's tactical use of pharmaceutical patents (including over uses, delivery systems and even packaging), not to extension of any particular patent over an active product ingredient. This article focuses in particular on the 'evergreening' potential of so-called 'linkage' provisions, imposed on the regulatory (safety, quality and efficacy) approval systems for generic pharmaceuticals of Canada and Australia, by specific articles in trade agreements with the US. These 'linkage' provisions have also recently appeared in the Korea-US Free Trade Agreement (KORUSFTA). They require such drug regulators to facilitate notification of, or even prevent, any potential patent infringement by a generic pharmaceutical manufacturer. This article explores the regulatory lessons to be learnt from Canada's and Australia's shared experience in terms of minimizing potential adverse impacts of such 'linkage evergreening' provisions on drug costs and thereby potentially on citizen's access to affordable, essential medicines. PMID:17543113
Spin Transfer in Polymer Degradation of Abnormal Linkage
Yu, Tianrong; Tian, Chuanjin; Liu, Xizhe; Wang, Jia; Gao, Yang; Wang, Zhigang
2017-07-01
The degradation of polymer materials plays an important role in production and life. In this work, the degradation mechanism of poly-α-methylstyrene (PAMS) tetramers with abnormal linkage was investigated by using density functional theory (DFT). Calculated results indicate that the head-to-head and the tail-to-tail reactions needed to overcome the energy barriers are about 0.15 eV and about 1.26 eV, respectively. The broken C-C bond at the unsaturated end of the chain leads to the dissociation of alpha-methylstyrene (AMS) monomers one by one. Furthermore, the analyses of bond characteristics are in good agreement with the results of energy barriers. In addition, the spin population analysis presents an interesting net spin transfer process in depolymerization reactions. We hope that the current theoretical results provide useful help to understand the degradation mechanism of polymers.
Cliff´s Delta Calculator: A non-parametric effect size program for two groups of observations
Directory of Open Access Journals (Sweden)
Guillermo Macbeth
2011-05-01
Full Text Available The Cliff´s Delta statistic is an effect size measure that quantifies the amount of difference between two non-parametric variables beyond p-values interpretation. This measure can be understood as a useful complementary analysis for the corresponding hypothesis testing. During the last two decades the use of effect size measures has been strongly encouraged by methodologists and leading institutions of behavioral sciences. The aim of this contribution is to introduce the Cliff´s Delta Calculator software that performs such analysis and offers some interpretation tips. Differences and similarities with the parametric case are analysed and illustrated. The implementation of this free program is fully described and compared with other calculators. Alternative algorithmic approaches are mathematically analysed and a basic linear algebra proof of its equivalence is formally presented. Two worked examples in cognitive psychology are commented. A visual interpretation of Cliff´s Delta is suggested. Availability, installation and applications of the program are presented and discussed.
The score statistic of the LD-lod analysis: detecting linkage adaptive to linkage disequilibrium.
Huang, J; Jiang, Y
2001-01-01
We study the properties of a modified lod score method for testing linkage that incorporates linkage disequilibrium (LD-lod). By examination of its score statistic, we show that the LD-lod score method adaptively combines two sources of information: (a) the IBD sharing score which is informative for linkage regardless of the existence of LD and (b) the contrast between allele-specific IBD sharing scores which is informative for linkage only in the presence of LD. We also consider the connection between the LD-lod score method and the transmission-disequilibrium test (TDT) for triad data and the mean test for affected sib pair (ASP) data. We show that, for triad data, the recessive LD-lod test is asymptotically equivalent to the TDT; and for ASP data, it is an adaptive combination of the TDT and the ASP mean test. We demonstrate that the LD-lod score method has relatively good statistical efficiency in comparison with the ASP mean test and the TDT for a broad range of LD and the genetic models considered in this report. Therefore, the LD-lod score method is an interesting approach for detecting linkage when the extent of LD is unknown, such as in a genome-wide screen with a dense set of genetic markers. Copyright 2001 S. Karger AG, Basel
Rethinking health sector procurement as developmental linkages in East Africa.
Mackintosh, Maureen; Tibandebage, Paula; Karimi Njeru, Mercy; Kariuki Kungu, Joan; Israel, Caroline; Mujinja, Phares G M
2018-03-01
Health care forms a large economic sector in all countries, and procurement of medicines and other essential commodities necessarily creates economic linkages between a country's health sector and local and international industrial development. These procurement processes may be positive or negative in their effects on populations' access to appropriate treatment and on local industrial development, yet procurement in low and middle income countries (LMICs) remains under-studied: generally analysed, when addressed at all, as a public sector technical and organisational challenge rather than a social and economic element of health system governance shaping its links to the wider economy. This article uses fieldwork in Tanzania and Kenya in 2012-15 to analyse procurement of essential medicines and supplies as a governance process for the health system and its industrial links, drawing on aspects of global value chain theory. We describe procurement work processes as experienced by front line staff in public, faith-based and private sectors, linking these experiences to wholesale funding sources and purchasing practices, and examining their implications for medicines access and for local industrial development within these East African countries. We show that in a context of poor access to reliable medicines, extensive reliance on private medicines purchase, and increasing globalisation of procurement systems, domestic linkages between health and industrial sectors have been weakened, especially in Tanzania. We argue in consequence for a more developmental perspective on health sector procurement design, including closer policy attention to strengthening vertical and horizontal relational working within local health-industry value chains, in the interests of both wider access to treatment and improved industrial development in Africa. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.
DEFF Research Database (Denmark)
Linnet, Kristian
2005-01-01
Bootstrap, HPLC, limit of blank, limit of detection, non-parametric statistics, type I and II errors......Bootstrap, HPLC, limit of blank, limit of detection, non-parametric statistics, type I and II errors...
Hagger-Johnson, Gareth; Harron, Katie; Goldstein, Harvey; Aldridge, Robert; Gilbert, Ruth
2017-06-30
BACKGROUND: The pseudonymisation algorithm used to link together episodes of care belonging to the same patients in England (HESID) has never undergone any formal evaluation, to determine the extent of data linkage error. To quantify improvements in linkage accuracy from adding probabilistic linkage to existing deterministic HESID algorithms. Inpatient admissions to NHS hospitals in England (Hospital Episode Statistics, HES) over 17 years (1998 to 2015) for a sample of patients (born 13/28th of months in 1992/1998/2005/2012). We compared the existing deterministic algorithm with one that included an additional probabilistic step, in relation to a reference standard created using enhanced probabilistic matching with additional clinical and demographic information. Missed and false matches were quantified and the impact on estimates of hospital readmission within one year were determined. HESID produced a high missed match rate, improving over time (8.6% in 1998 to 0.4% in 2015). Missed matches were more common for ethnic minorities, those living in areas of high socio-economic deprivation, foreign patients and those with 'no fixed abode'. Estimates of the readmission rate were biased for several patient groups owing to missed matches, which was reduced for nearly all groups. CONCLUSION: Probabilistic linkage of HES reduced missed matches and bias in estimated readmission rates, with clear implications for commissioning, service evaluation and performance monitoring of hospitals. The existing algorithm should be modified to address data linkage error, and a retrospective update of the existing data would address existing linkage errors and their implications.
Meta-analysis of 32 genome-wide linkage studies of schizophrenia
Ng, MYM; Levinson, DF; Faraone, SV; Suarez, BK; DeLisi, LE; Arinami, T; Riley, B; Paunio, T; Pulver, AE; Irmansyah; Holmans, PA; Escamilla, M; Wildenauer, DB; Williams, NM; Laurent, C; Mowry, BJ; Brzustowicz, LM; Maziade, M; Sklar, P; Garver, DL; Abecasis, GR; Lerer, B; Fallin, MD; Gurling, HMD; Gejman, PV; Lindholm, E; Moises, HW; Byerley, W; Wijsman, EM; Forabosco, P; Tsuang, MT; Hwu, H-G; Okazaki, Y; Kendler, KS; Wormley, B; Fanous, A; Walsh, D; O’Neill, FA; Peltonen, L; Nestadt, G; Lasseter, VK; Liang, KY; Papadimitriou, GM; Dikeos, DG; Schwab, SG; Owen, MJ; O’Donovan, MC; Norton, N; Hare, E; Raventos, H; Nicolini, H; Albus, M; Maier, W; Nimgaonkar, VL; Terenius, L; Mallet, J; Jay, M; Godard, S; Nertney, D; Alexander, M; Crowe, RR; Silverman, JM; Bassett, AS; Roy, M-A; Mérette, C; Pato, CN; Pato, MT; Roos, J Louw; Kohn, Y; Amann-Zalcenstein, D; Kalsi, G; McQuillin, A; Curtis, D; Brynjolfson, J; Sigmundsson, T; Petursson, H; Sanders, AR; Duan, J; Jazin, E; Myles-Worsley, M; Karayiorgou, M; Lewis, CM
2009-01-01
A genome scan meta-analysis (GSMA) was carried out on 32 independent genome-wide linkage scan analyses that included 3255 pedigrees with 7413 genotyped cases affected with schizophrenia (SCZ) or related disorders. The primary GSMA divided the autosomes into 120 bins, rank-ordered the bins within each study according to the most positive linkage result in each bin, summed these ranks (weighted for study size) for each bin across studies and determined the empirical probability of a given summed rank (PSR) by simulation. Suggestive evidence for linkage was observed in two single bins, on chromosomes 5q (142-168 Mb) and 2q (103-134 Mb). Genome-wide evidence for linkage was detected on chromosome 2q (119-152 Mb) when bin boundaries were shifted to the middle of the previous bins. The primary analysis met empirical criteria for ‘aggregate’ genome-wide significance, indicating that some or all of 10 bins are likely to contain loci linked to SCZ, including regions of chromosomes 1, 2q, 3q, 4q, 5q, 8p and 10q. In a secondary analysis of 22 studies of European-ancestry samples, suggestive evidence for linkage was observed on chromosome 8p (16-33 Mb). Although the newer genome-wide association methodology has greater power to detect weak associations to single common DNA sequence variants, linkage analysis can detect diverse genetic effects that segregate in families, including multiple rare variants within one locus or several weakly associated loci in the same region. Therefore, the regions supported by this meta-analysis deserve close attention in future studies. PMID:19349958
Bunn, Terry L; Slavova, Svetla; Bathke, Arne
2007-07-01
The identification of industry, occupation, and associated injury costs for worker falls in Kentucky have not been fully examined. The purpose of this study was to determine the associations between industry and occupation and 1) hospitalization length of stay; 2) hospitalization charges; and 3) workers' claims costs in workers suffering falls, using linked inpatient hospitalization discharge and workers' claims data sets. Hospitalization cases were selected with ICD-9-CM external cause of injury codes for falls and payer code of workers' claims for years 2000-2004. Selection criteria for workers'claims cases were International Association of Industrial Accident Boards and Commissions Electronic Data Interchange Nature (IAIABCEDIN) injuries coded as falls and/or slips. Common data variables between the two data sets such as date of birth, gender, date of injury, and hospital admission date were used to perform probabilistic data linkage using LinkSolv software. Statistical analysis was performed with non-parametric tests. Construction falls were the most prevalent for male workers and incurred the highest hospitalization and workers' compensation costs, whereas most female worker falls occurred in the services industry. The largest percentage of male worker falls was from one level to another, while the largest percentage of females experienced a fall, slip, or trip (not otherwise classified). When male construction worker falls were further analyzed, laborers and helpers had longer hospital stays as well as higher total charges when the worker fell from one level to another. Data linkage of hospitalization and workers' claims falls data provides additional information on industry, occupation, and costs that are not available when examining either data set alone.
Hadron Energy Reconstruction for ATLAS Barrel Combined Calorimeter Using Non-Parametrical Method
Kulchitskii, Yu A
2000-01-01
Hadron energy reconstruction for the ATLAS barrel prototype combined calorimeter in the framework of the non-parametrical method is discussed. The non-parametrical method utilizes only the known e/h ratios and the electron calibration constants and does not require the determination of any parameters by a minimization technique. Thus, this technique lends itself to fast energy reconstruction in a first level trigger. The reconstructed mean values of the hadron energies are within \\pm1% of the true values and the fractional energy resolution is [(58\\pm 3)%{\\sqrt{GeV}}/\\sqrt{E}+(2.5\\pm0.3)%]\\bigoplus(1.7\\pm0.2) GeV/E. The value of the e/h ratio obtained for the electromagnetic compartment of the combined calorimeter is 1.74\\pm0.04. Results of a study of the longitudinal hadronic shower development are also presented.
On Wasserstein Two-Sample Testing and Related Families of Nonparametric Tests
Directory of Open Access Journals (Sweden)
Aaditya Ramdas
2017-01-01
Full Text Available Nonparametric two-sample or homogeneity testing is a decision theoretic problem that involves identifying differences between two random variables without making parametric assumptions about their underlying distributions. The literature is old and rich, with a wide variety of statistics having being designed and analyzed, both for the unidimensional and the multivariate setting. Inthisshortsurvey,wefocusonteststatisticsthatinvolvetheWassersteindistance. Usingan entropic smoothing of the Wasserstein distance, we connect these to very different tests including multivariate methods involving energy statistics and kernel based maximum mean discrepancy and univariate methods like the Kolmogorov–Smirnov test, probability or quantile (PP/QQ plots and receiver operating characteristic or ordinal dominance (ROC/ODC curves. Some observations are implicit in the literature, while others seem to have not been noticed thus far. Given nonparametric two-sample testing’s classical and continued importance, we aim to provide useful connections for theorists and practitioners familiar with one subset of methods but not others.
Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors
Directory of Open Access Journals (Sweden)
Xibin Zhang
2016-04-01
Full Text Available This paper develops a sampling algorithm for bandwidth estimation in a nonparametric regression model with continuous and discrete regressors under an unknown error density. The error density is approximated by the kernel density estimator of the unobserved errors, while the regression function is estimated using the Nadaraya-Watson estimator admitting continuous and discrete regressors. We derive an approximate likelihood and posterior for bandwidth parameters, followed by a sampling algorithm. Simulation results show that the proposed approach typically leads to better accuracy of the resulting estimates than cross-validation, particularly for smaller sample sizes. This bandwidth estimation approach is applied to nonparametric regression model of the Australian All Ordinaries returns and the kernel density estimation of gross domestic product (GDP growth rates among the organisation for economic co-operation and development (OECD and non-OECD countries.
Bootstrap Prediction Intervals in Non-Parametric Regression with Applications to Anomaly Detection
Kumar, Sricharan; Srivistava, Ashok N.
2012-01-01
Prediction intervals provide a measure of the probable interval in which the outputs of a regression model can be expected to occur. Subsequently, these prediction intervals can be used to determine if the observed output is anomalous or not, conditioned on the input. In this paper, a procedure for determining prediction intervals for outputs of nonparametric regression models using bootstrap methods is proposed. Bootstrap methods allow for a non-parametric approach to computing prediction intervals with no specific assumptions about the sampling distribution of the noise or the data. The asymptotic fidelity of the proposed prediction intervals is theoretically proved. Subsequently, the validity of the bootstrap based prediction intervals is illustrated via simulations. Finally, the bootstrap prediction intervals are applied to the problem of anomaly detection on aviation data.
A Bayesian nonparametric approach to reconstruction and prediction of random dynamical systems
Merkatas, Christos; Kaloudis, Konstantinos; Hatjispyros, Spyridon J.
2017-06-01
We propose a Bayesian nonparametric mixture model for the reconstruction and prediction from observed time series data, of discretized stochastic dynamical systems, based on Markov Chain Monte Carlo methods. Our results can be used by researchers in physical modeling interested in a fast and accurate estimation of low dimensional stochastic models when the size of the observed time series is small and the noise process (perhaps) is non-Gaussian. The inference procedure is demonstrated specifically in the case of polynomial maps of an arbitrary degree and when a Geometric Stick Breaking mixture process prior over the space of densities, is applied to the additive errors. Our method is parsimonious compared to Bayesian nonparametric techniques based on Dirichlet process mixtures, flexible and general. Simulations based on synthetic time series are presented.
Bayesian Non-Parametric Mixtures of GARCH(1,1 Models
Directory of Open Access Journals (Sweden)
John W. Lau
2012-01-01
Full Text Available Traditional GARCH models describe volatility levels that evolve smoothly over time, generated by a single GARCH regime. However, nonstationary time series data may exhibit abrupt changes in volatility, suggesting changes in the underlying GARCH regimes. Further, the number and times of regime changes are not always obvious. This article outlines a nonparametric mixture of GARCH models that is able to estimate the number and time of volatility regime changes by mixing over the Poisson-Kingman process. The process is a generalisation of the Dirichlet process typically used in nonparametric models for time-dependent data provides a richer clustering structure, and its application to time series data is novel. Inference is Bayesian, and a Markov chain Monte Carlo algorithm to explore the posterior distribution is described. The methodology is illustrated on the Standard and Poor's 500 financial index.
Bornkamp, Björn; Ickstadt, Katja
2009-03-01
In this article, we consider monotone nonparametric regression in a Bayesian framework. The monotone function is modeled as a mixture of shifted and scaled parametric probability distribution functions, and a general random probability measure is assumed as the prior for the mixing distribution. We investigate the choice of the underlying parametric distribution function and find that the two-sided power distribution function is well suited both from a computational and mathematical point of view. The model is motivated by traditional nonlinear models for dose-response analysis, and provides possibilities to elicitate informative prior distributions on different aspects of the curve. The method is compared with other recent approaches to monotone nonparametric regression in a simulation study and is illustrated on a data set from dose-response analysis.
Promotion time cure rate model with nonparametric form of covariate effects.
Chen, Tianlei; Du, Pang
2018-05-10
Survival data with a cured portion are commonly seen in clinical trials. Motivated from a biological interpretation of cancer metastasis, promotion time cure model is a popular alternative to the mixture cure rate model for analyzing such data. The existing promotion cure models all assume a restrictive parametric form of covariate effects, which can be incorrectly specified especially at the exploratory stage. In this paper, we propose a nonparametric approach to modeling the covariate effects under the framework of promotion time cure model. The covariate effect function is estimated by smoothing splines via the optimization of a penalized profile likelihood. Point-wise interval estimates are also derived from the Bayesian interpretation of the penalized profile likelihood. Asymptotic convergence rates are established for the proposed estimates. Simulations show excellent performance of the proposed nonparametric method, which is then applied to a melanoma study. Copyright © 2018 John Wiley & Sons, Ltd.
Filippi, Sarah; Holmes, Chris C; Nieto-Barajas, Luis E
2016-11-16
In this article we propose novel Bayesian nonparametric methods using Dirichlet Process Mixture (DPM) models for detecting pairwise dependence between random variables while accounting for uncertainty in the form of the underlying distributions. A key criteria is that the procedures should scale to large data sets. In this regard we find that the formal calculation of the Bayes factor for a dependent-vs.-independent DPM joint probability measure is not feasible computationally. To address this we present Bayesian diagnostic measures for characterising evidence against a "null model" of pairwise independence. In simulation studies, as well as for a real data analysis, we show that our approach provides a useful tool for the exploratory nonparametric Bayesian analysis of large multivariate data sets.
A Bayesian nonparametric approach to reconstruction and prediction of random dynamical systems.
Merkatas, Christos; Kaloudis, Konstantinos; Hatjispyros, Spyridon J
2017-06-01
We propose a Bayesian nonparametric mixture model for the reconstruction and prediction from observed time series data, of discretized stochastic dynamical systems, based on Markov Chain Monte Carlo methods. Our results can be used by researchers in physical modeling interested in a fast and accurate estimation of low dimensional stochastic models when the size of the observed time series is small and the noise process (perhaps) is non-Gaussian. The inference procedure is demonstrated specifically in the case of polynomial maps of an arbitrary degree and when a Geometric Stick Breaking mixture process prior over the space of densities, is applied to the additive errors. Our method is parsimonious compared to Bayesian nonparametric techniques based on Dirichlet process mixtures, flexible and general. Simulations based on synthetic time series are presented.
Scalable Bayesian nonparametric regression via a Plackett-Luce model for conditional ranks
Gray-Davies, Tristan; Holmes, Chris C.; Caron, François
2018-01-01
We present a novel Bayesian nonparametric regression model for covariates X and continuous response variable Y ∈ ℝ. The model is parametrized in terms of marginal distributions for Y and X and a regression function which tunes the stochastic ordering of the conditional distributions F (y|x). By adopting an approximate composite likelihood approach, we show that the resulting posterior inference can be decoupled for the separate components of the model. This procedure can scale to very large datasets and allows for the use of standard, existing, software from Bayesian nonparametric density estimation and Plackett-Luce ranking estimation to be applied. As an illustration, we show an application of our approach to a US Census dataset, with over 1,300,000 data points and more than 100 covariates. PMID:29623150
A nonparametric empirical Bayes framework for large-scale multiple testing.
Martin, Ryan; Tokdar, Surya T
2012-07-01
We propose a flexible and identifiable version of the 2-groups model, motivated by hierarchical Bayes considerations, that features an empirical null and a semiparametric mixture model for the nonnull cases. We use a computationally efficient predictive recursion (PR) marginal likelihood procedure to estimate the model parameters, even the nonparametric mixing distribution. This leads to a nonparametric empirical Bayes testing procedure, which we call PRtest, based on thresholding the estimated local false discovery rates. Simulations and real data examples demonstrate that, compared to existing approaches, PRtest's careful handling of the nonnull density can give a much better fit in the tails of the mixture distribution which, in turn, can lead to more realistic conclusions.
Yau, Christopher; Holmes, Chris
2011-07-01
We propose a hierarchical Bayesian nonparametric mixture model for clustering when some of the covariates are assumed to be of varying relevance to the clustering problem. This can be thought of as an issue in variable selection for unsupervised learning. We demonstrate that by defining a hierarchical population based nonparametric prior on the cluster locations scaled by the inverse covariance matrices of the likelihood we arrive at a 'sparsity prior' representation which admits a conditionally conjugate prior. This allows us to perform full Gibbs sampling to obtain posterior distributions over parameters of interest including an explicit measure of each covariate's relevance and a distribution over the number of potential clusters present in the data. This also allows for individual cluster specific variable selection. We demonstrate improved inference on a number of canonical problems.
DEFF Research Database (Denmark)
Carrao, Hugo; Sepulcre, Guadalupe; Horion, Stéphanie Marie Anne F
2013-01-01
This study evaluates the relationship between the frequency and duration of meteorological droughts and the subsequent temporal changes on the quantity of actively photosynthesizing biomass (greenness) estimated from satellite imagery on rainfed croplands in Latin America. An innovative non-parametric...... and non-supervised approach, based on the Fisher-Jenks optimal classification algorithm, is used to identify multi-scale meteorological droughts on the basis of empirical cumulative distributions of 1, 3, 6, and 12-monthly precipitation totals. As input data for the classifier, we use the gridded GPCC...... for the period between 1998 and 2010. The time-series analysis of vegetation greenness is performed during the growing season with a non-parametric method, namely the seasonal Relative Greenness (RG) of spatially accumulated fAPAR. The Global Land Cover map of 2000 and the GlobCover maps of 2005/2006 and 2009...
Ethnicity and the ethics of data linkage
Directory of Open Access Journals (Sweden)
Boyd Kenneth M
2007-11-01
Full Text Available Abstract Linking health data with census data on ethnicity has potential benefits for the health of ethnic minority groups. Ethical objections to linking these data however include concerns about informed consent and the possibility of the findings being misused against the interests of ethnic minority groups. While consent concerns may be allayed by procedures to safeguard anonymity and respect privacy, robust procedures to demonstrate public approval of data linkage also need to be devised. The possibility of findings being misused against the interests of ethnic minority groups may be diminished by informed and open public discussion in mature democracies, but remain a concern in the international context.
von Hirschhausen, Christian R.; Cullmann, Astrid
2005-01-01
Abstract This paper applies parametric and non-parametric and parametric tests to assess the efficiency of electricity distribution companies in Germany. We address traditional issues in electricity sector benchmarking, such as the role of scale effects and optimal utility size, as well as new evidence specific to the situation in Germany. We use labour, capital, and peak load capacity as inputs, and units sold and the number of customers as output. The data cover 307 (out of 553) ...
Ambrogioni, Luca; Güçlü, Umut; van Gerven, Marcel A. J.; Maris, Eric
2017-01-01
This paper introduces the kernel mixture network, a new method for nonparametric estimation of conditional probability densities using neural networks. We model arbitrarily complex conditional densities as linear combinations of a family of kernel functions centered at a subset of training points. The weights are determined by the outer layer of a deep neural network, trained by minimizing the negative log likelihood. This generalizes the popular quantized softmax approach, which can be seen ...
Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches
Wang, Wenshuo; Xi, Junqiang; Zhao, Ding
2017-01-01
Analysis and recognition of driving styles are profoundly important to intelligent transportation and vehicle calibration. This paper presents a novel driving style analysis framework using the primitive driving patterns learned from naturalistic driving data. In order to achieve this, first, a Bayesian nonparametric learning method based on a hidden semi-Markov model (HSMM) is introduced to extract primitive driving patterns from time series driving data without prior knowledge of the number...
Nonparametric Change Point Diagnosis Method of Concrete Dam Crack Behavior Abnormality
Li, Zhanchao; Gu, Chongshi; Wu, Zhongru
2013-01-01
The study on diagnosis method of concrete crack behavior abnormality has always been a hot spot and difficulty in the safety monitoring field of hydraulic structure. Based on the performance of concrete dam crack behavior abnormality in parametric statistical model and nonparametric statistical model, the internal relation between concrete dam crack behavior abnormality and statistical change point theory is deeply analyzed from the model structure instability of parametric statistical model ...
Adaptive nonparametric estimation for L\\'evy processes observed at low frequency
Kappus, Johanna
2013-01-01
This article deals with adaptive nonparametric estimation for L\\'evy processes observed at low frequency. For general linear functionals of the L\\'evy measure, we construct kernel estimators, provide upper risk bounds and derive rates of convergence under regularity assumptions. Our focus lies on the adaptive choice of the bandwidth, using model selection techniques. We face here a non-standard problem of model selection with unknown variance. A new approach towards this problem is proposed, ...
A simple non-parametric goodness-of-fit test for elliptical copulas
Directory of Open Access Journals (Sweden)
Jaser Miriam
2017-12-01
Full Text Available In this paper, we propose a simple non-parametric goodness-of-fit test for elliptical copulas of any dimension. It is based on the equality of Kendall’s tau and Blomqvist’s beta for all bivariate margins. Nominal level and power of the proposed test are investigated in a Monte Carlo study. An empirical application illustrates our goodness-of-fit test at work.
Bootstrapping the economy -- a non-parametric method of generating consistent future scenarios
Müller, Ulrich A; Bürgi, Roland; Dacorogna, Michel M
2004-01-01
The fortune and the risk of a business venture depends on the future course of the economy. There is a strong demand for economic forecasts and scenarios that can be applied to planning and modeling. While there is an ongoing debate on modeling economic scenarios, the bootstrapping (or resampling) approach presented here has several advantages. As a non-parametric method, it directly relies on past market behaviors rather than debatable assumptions on models and parameters. Simultaneous dep...
Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data
DEFF Research Database (Denmark)
Tan, Qihua; Thomassen, Mads; Burton, Mark
2017-01-01
the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray...... time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health....
Directory of Open Access Journals (Sweden)
Ibsen Chivatá Cárdenas
2008-05-01
Full Text Available This article presents a rainfall model constructed by applying non-parametric modelling and imprecise probabilities; these tools were used because there was not enough homogeneous information in the study area. The area’s hydro-logical information regarding rainfall was scarce and existing hydrological time series were not uniform. A distributed extended rainfall model was constructed from so-called probability boxes (p-boxes, multinomial probability distribu-tion and confidence intervals (a friendly algorithm was constructed for non-parametric modelling by combining the last two tools. This model confirmed the high level of uncertainty involved in local rainfall modelling. Uncertainty en-compassed the whole range (domain of probability values thereby showing the severe limitations on information, leading to the conclusion that a detailed estimation of probability would lead to significant error. Nevertheless, rele-vant information was extracted; it was estimated that maximum daily rainfall threshold (70 mm would be surpassed at least once every three years and the magnitude of uncertainty affecting hydrological parameter estimation. This paper’s conclusions may be of interest to non-parametric modellers and decisions-makers as such modelling and imprecise probability represents an alternative for hydrological variable assessment and maybe an obligatory proce-dure in the future. Its potential lies in treating scarce information and represents a robust modelling strategy for non-seasonal stochastic modelling conditions
Dai, Wenlin; Tong, Tiejun; Zhu, Lixing
2017-01-01
Difference-based methods do not require estimating the mean function in nonparametric regression and are therefore popular in practice. In this paper, we propose a unified framework for variance estimation that combines the linear regression method with the higher-order difference estimators systematically. The unified framework has greatly enriched the existing literature on variance estimation that includes most existing estimators as special cases. More importantly, the unified framework has also provided a smart way to solve the challenging difference sequence selection problem that remains a long-standing controversial issue in nonparametric regression for several decades. Using both theory and simulations, we recommend to use the ordinary difference sequence in the unified framework, no matter if the sample size is small or if the signal-to-noise ratio is large. Finally, to cater for the demands of the application, we have developed a unified R package, named VarED, that integrates the existing difference-based estimators and the unified estimators in nonparametric regression and have made it freely available in the R statistical program http://cran.r-project.org/web/packages/.
Smooth semi-nonparametric (SNP) estimation of the cumulative incidence function.
Duc, Anh Nguyen; Wolbers, Marcel
2017-08-15
This paper presents a novel approach to estimation of the cumulative incidence function in the presence of competing risks. The underlying statistical model is specified via a mixture factorization of the joint distribution of the event type and the time to the event. The time to event distributions conditional on the event type are modeled using smooth semi-nonparametric densities. One strength of this approach is that it can handle arbitrary censoring and truncation while relying on mild parametric assumptions. A stepwise forward algorithm for model estimation and adaptive selection of smooth semi-nonparametric polynomial degrees is presented, implemented in the statistical software R, evaluated in a sequence of simulation studies, and applied to data from a clinical trial in cryptococcal meningitis. The simulations demonstrate that the proposed method frequently outperforms both parametric and nonparametric alternatives. They also support the use of 'ad hoc' asymptotic inference to derive confidence intervals. An extension to regression modeling is also presented, and its potential and challenges are discussed. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
Dai, Wenlin
2017-09-01
Difference-based methods do not require estimating the mean function in nonparametric regression and are therefore popular in practice. In this paper, we propose a unified framework for variance estimation that combines the linear regression method with the higher-order difference estimators systematically. The unified framework has greatly enriched the existing literature on variance estimation that includes most existing estimators as special cases. More importantly, the unified framework has also provided a smart way to solve the challenging difference sequence selection problem that remains a long-standing controversial issue in nonparametric regression for several decades. Using both theory and simulations, we recommend to use the ordinary difference sequence in the unified framework, no matter if the sample size is small or if the signal-to-noise ratio is large. Finally, to cater for the demands of the application, we have developed a unified R package, named VarED, that integrates the existing difference-based estimators and the unified estimators in nonparametric regression and have made it freely available in the R statistical program http://cran.r-project.org/web/packages/.
Energy Technology Data Exchange (ETDEWEB)
Stein, J.; Hecht, T. [Univ. of Texas, Houston, TX (United States); Stal, S. [Texas Children`s Hospital, Houston, TX (United States)] [and others
1995-08-01
Nonsyndromic cleft lip with or without cleft palate (CL/P) is a common craniofacial developmental defect. Recent segregation analyses have suggested that major genes play a role in the etiology of CL/P. Linkage to 22 candidate genes was tested in 11 multigenerational families with CL/P, and 21 of these candidates were excluded. APOC2, 19q13.1, which is linked to the proto-oncogene BCL3, gave suggestive evidence for linkage to CL/P. The study was expanded to include a total of 39 multigenerational CL/P families. Linkage was tested in all families, using anonymous marker, D19S178, and intragenic markers in BCL3 and APOC2. Linkage was tested under two models, autosomal dominant with reduced penetrance and affecteds-only model. Both models showed evidence of heterogeneity, with 43% of families linked at zero recombination to BCL3 when marker data from BCL3 and APOC2 were included. A maximum multipoint LOD score of 7.00 at BCL3 was found among the 17 families that had posterior probabilities {ge}50% in favor of linkage. The transmission disequilibrium test provided additional evidence for linkage with the 3 allele of BCL3 more often transmitted to affected children. These results suggest that BCL3, or a nearby gene, plays a role in the etiology of CL/P in some families. 39 refs., 8 figs., 4 tabs.
Strike-slip tectonics during rift linkage
Pagli, C.; Yun, S. H.; Ebinger, C.; Keir, D.; Wang, H.
2017-12-01
The kinematics of triple junction linkage and the initiation of transforms in magmatic rifts remain debated. Strain patterns from the Afar triple junction provide tests of current models of how rifts grow to link in area of incipient oceanic spreading. Here we present a combined analysis of seismicity, InSAR and GPS derived strain rate maps to reveal that the plate boundary deformation in Afar is accommodated primarily by extensional tectonics in the Red Sea and Gulf of Aden rifts, and does not require large rotations about vertical axes (bookshelf faulting). Additionally, models of stress changes and seismicity induced by recent dykes in one sector of the Afar triple junction provide poor fit to the observed strike-slip earthquakes. Instead we explain these patterns as rift-perpendicular shearing at the tips of spreading rifts where extensional strains terminate against less stretched lithosphere. Our results demonstrate that rift-perpendicular strike-slip faulting between rift segments achieves plate boundary linkage during incipient seafloor spreading.
Directory of Open Access Journals (Sweden)
Fernandez Ana
2010-05-01
Full Text Available Abstract Background Previous studies have analyzed the psychometric properties of the World Health Organization Disability Assessment Schedule II (WHO-DAS II using classical omnibus measures of scale quality. These analyses are sample dependent and do not model item responses as a function of the underlying trait level. The main objective of this study was to examine the effectiveness of the WHO-DAS II items and their options in discriminating between changes in the underlying disability level by means of item response analyses. We also explored differential item functioning (DIF in men and women. Methods The participants were 3615 adult general practice patients from 17 regions of Spain, with a first diagnosed major depressive episode. The 12-item WHO-DAS II was administered by the general practitioners during the consultation. We used a non-parametric item response method (Kernel-Smoothing implemented with the TestGraf software to examine the effectiveness of each item (item characteristic curves and their options (option characteristic curves in discriminating between changes in the underliying disability level. We examined composite DIF to know whether women had a higher probability than men of endorsing each item. Results Item response analyses indicated that the twelve items forming the WHO-DAS II perform very well. All items were determined to provide good discrimination across varying standardized levels of the trait. The items also had option characteristic curves that showed good discrimination, given that each increasing option became more likely than the previous as a function of increasing trait level. No gender-related DIF was found on any of the items. Conclusions All WHO-DAS II items were very good at assessing overall disability. Our results supported the appropriateness of the weights assigned to response option categories and showed an absence of gender differences in item functioning.
Schiemann, R.; Erdin, R.; Willi, M.; Frei, C.; Berenguer, M.; Sempere-Torres, D.
2011-05-01
Modelling spatial covariance is an essential part of all geostatistical methods. Traditionally, parametric semivariogram models are fit from available data. More recently, it has been suggested to use nonparametric correlograms obtained from spatially complete data fields. Here, both estimation techniques are compared. Nonparametric correlograms are shown to have a substantial negative bias. Nonetheless, when combined with the sample variance of the spatial field under consideration, they yield an estimate of the semivariogram that is unbiased for small lag distances. This justifies the use of this estimation technique in geostatistical applications. Various formulations of geostatistical combination (Kriging) methods are used here for the construction of hourly precipitation grids for Switzerland based on data from a sparse realtime network of raingauges and from a spatially complete radar composite. Two variants of Ordinary Kriging (OK) are used to interpolate the sparse gauge observations. In both OK variants, the radar data are only used to determine the semivariogram model. One variant relies on a traditional parametric semivariogram estimate, whereas the other variant uses the nonparametric correlogram. The variants are tested for three cases and the impact of the semivariogram model on the Kriging prediction is illustrated. For the three test cases, the method using nonparametric correlograms performs equally well or better than the traditional method, and at the same time offers great practical advantages. Furthermore, two variants of Kriging with external drift (KED) are tested, both of which use the radar data to estimate nonparametric correlograms, and as the external drift variable. The first KED variant has been used previously for geostatistical radar-raingauge merging in Catalonia (Spain). The second variant is newly proposed here and is an extension of the first. Both variants are evaluated for the three test cases as well as an extended evaluation
Rights, Jason D; Sterba, Sonya K
2016-11-01
Multilevel data structures are common in the social sciences. Often, such nested data are analysed with multilevel models (MLMs) in which heterogeneity between clusters is modelled by continuously distributed random intercepts and/or slopes. Alternatively, the non-parametric multilevel regression mixture model (NPMM) can accommodate the same nested data structures through discrete latent class variation. The purpose of this article is to delineate analytic relationships between NPMM and MLM parameters that are useful for understanding the indirect interpretation of the NPMM as a non-parametric approximation of the MLM, with relaxed distributional assumptions. We define how seven standard and non-standard MLM specifications can be indirectly approximated by particular NPMM specifications. We provide formulas showing how the NPMM can serve as an approximation of the MLM in terms of intraclass correlation, random coefficient means and (co)variances, heteroscedasticity of residuals at level 1, and heteroscedasticity of residuals at level 2. Further, we discuss how these relationships can be useful in practice. The specific relationships are illustrated with simulated graphical demonstrations, and direct and indirect interpretations of NPMM classes are contrasted. We provide an R function to aid in implementing and visualizing an indirect interpretation of NPMM classes. An empirical example is presented and future directions are discussed. © 2016 The British Psychological Society.
Effects of Worldwide Population Subdivision on ALDH2 Linkage Disequilibrium
Peterson, Raymond J.; Goldman, David; Long, Jeffrey C.
1999-01-01
The effect of human population subdivision on linkage disequilibrium has previously been studied for unlinked genes. However, no study has focused on closely linked polymorphisms or formally partitioned linkage disequilibrium within and among worldwide populations. With an emphasis on population subdivision, the goal of this paper is to investigate the causes of linkage disequilibrium in ALDH2, the gene that encodes aldehyde dehydrogenase 2. Haplotypes for 756 people from 17 populations acros...
Colorectal cancer linkage on chromosomes 4q21, 8q13, 12q24, and 15q22.
Directory of Open Access Journals (Sweden)
Mine S Cicek
Full Text Available A substantial proportion of familial colorectal cancer (CRC is not a consequence of known susceptibility loci, such as mismatch repair (MMR genes, supporting the existence of additional loci. To identify novel CRC loci, we conducted a genome-wide linkage scan in 356 white families with no evidence of defective MMR (i.e., no loss of tumor expression of MMR proteins, no microsatellite instability (MSI-high tumors, or no evidence of linkage to MMR genes. Families were ascertained via the Colon Cancer Family Registry multi-site NCI-supported consortium (Colon CFR, the City of Hope Comprehensive Cancer Center, and Memorial University of Newfoundland. A total of 1,612 individuals (average 5.0 per family including 2.2 affected were genotyped using genome-wide single nucleotide polymorphism linkage arrays; parametric and non-parametric linkage analysis used MERLIN in a priori-defined family groups. Five lod scores greater than 3.0 were observed assuming heterogeneity. The greatest were among families with mean age of diagnosis less than 50 years at 4q21.1 (dominant HLOD = 4.51, α = 0.84, 145.40 cM, rs10518142 and among all families at 12q24.32 (dominant HLOD = 3.60, α = 0.48, 285.15 cM, rs952093. Among families with four or more affected individuals and among clinic-based families, a common peak was observed at 15q22.31 (101.40 cM, rs1477798; dominant HLOD = 3.07, α = 0.29; dominant HLOD = 3.03, α = 0.32, respectively. Analysis of families with only two affected individuals yielded a peak at 8q13.2 (recessive HLOD = 3.02, α = 0.51, 132.52 cM, rs1319036. These previously unreported linkage peaks demonstrate the continued utility of family-based data in complex traits and suggest that new CRC risk alleles remain to be elucidated.
Karamidehkordi, Esmail
2013-01-01
Purpose: This article aims to show the linkage of Iranian agricultural research centres with extension and farmers, using three case studies in 1999, 2005 and 2010. Design/methodology/approach: The data were collected through document analyses, structured and semi-structured interviews and observations. Findings: The 1999 and 2005 cases were…
A saturated SSR/DArT linkage map of Musa acuminata addressing genome rearrangements among bananas
Directory of Open Access Journals (Sweden)
Matsumoto Takashi
2010-04-01
Full Text Available Abstract Background The genus Musa is a large species complex which includes cultivars at diploid and triploid levels. These sterile and vegetatively propagated cultivars are based on the A genome from Musa acuminata, exclusively for sweet bananas such as Cavendish, or associated with the B genome (Musa balbisiana in cooking bananas such as Plantain varieties. In M. acuminata cultivars, structural heterozygosity is thought to be one of the main causes of sterility, which is essential for obtaining seedless fruits but hampers breeding. Only partial genetic maps are presently available due to chromosomal rearrangements within the parents of the mapping populations. This causes large segregation distortions inducing pseudo-linkages and difficulties in ordering markers in the linkage groups. The present study aims at producing a saturated linkage map of M. acuminata, taking into account hypotheses on the structural heterozygosity of the parents. Results An F1 progeny of 180 individuals was obtained from a cross between two genetically distant accessions of M. acuminata, 'Borneo' and 'Pisang Lilin' (P. Lilin. Based on the gametic recombination of each parent, two parental maps composed of SSR and DArT markers were established. A significant proportion of the markers (21.7% deviated (p Conclusions We propose a synthetic map with 11 linkage groups containing 489 markers (167 SSRs and 322 DArTs covering 1197 cM. This first saturated map is proposed as a "reference Musa map" for further analyses. We also propose two complete parental maps with interpretations of structural rearrangements localized on the linkage groups. The structural heterozygosity in P. Lilin is hypothesized to result from a duplication likely accompanied by an inversion on another chromosome. This paper also illustrates a methodological approach, transferable to other species, to investigate the mapping of structural rearrangements and determine their consequences on marker
Martinez Manzanera, Octavio; Elting, Jan Willem; van der Hoeven, Johannes H.; Maurits, Natasha M.
2016-01-01
In the clinic, tremor is diagnosed during a time-limited process in which patients are observed and the characteristics of tremor are visually assessed. For some tremor disorders, a more detailed analysis of these characteristics is needed. Accelerometry and electromyography can be used to obtain a better insight into tremor. Typically, routine clinical assessment of accelerometry and electromyography data involves visual inspection by clinicians and occasionally computational analysis to obtain objective characteristics of tremor. However, for some tremor disorders these characteristics may be different during daily activity. This variability in presentation between the clinic and daily life makes a differential diagnosis more difficult. A long-term recording of tremor by accelerometry and/or electromyography in the home environment could help to give a better insight into the tremor disorder. However, an evaluation of such recordings using routine clinical standards would take too much time. We evaluated a range of techniques that automatically detect tremor segments in accelerometer data, as accelerometer data is more easily obtained in the home environment than electromyography data. Time can be saved if clinicians only have to evaluate the tremor characteristics of segments that have been automatically detected in longer daily activity recordings. We tested four non-parametric methods and five parametric methods on clinical accelerometer data from 14 patients with different tremor disorders. The consensus between two clinicians regarding the presence or absence of tremor on 3943 segments of accelerometer data was employed as reference. The nine methods were tested against this reference to identify their optimal parameters. Non-parametric methods generally performed better than parametric methods on our dataset when optimal parameters were used. However, one parametric method, employing the high frequency content of the tremor bandwidth under consideration
Effects of aquaculture researchers' job characteristics on linkage ...
African Journals Online (AJOL)
The study examined the effects of researchers' job characteristics on linkage activities in Nigeria due to the fact that many fish farmers have not been properly reached with technologies and the problem of poor fish production has been attributed to the weak linkages existing between research, extension and fish farmers.
Electrostatic microactuators with integrated gear linkages for mechanical power transmission
Legtenberg, R.; Legtenberg, Rob; Berenschot, Johan W.; Elwenspoek, Michael Curt; Fluitman, J.H.J.
1996-01-01
In this paper a surface micromachining process is presented which has been used to fabricate electrostatic microactuators that are interconnected with each other and linked to other movable microstructures by integrated gear linkages. The gear linkages consist of rotational and linear gear
Privacy-preserving record linkage on large real world datasets.
Randall, Sean M; Ferrante, Anna M; Boyd, James H; Bauer, Jacqueline K; Semmens, James B
2014-08-01
Record linkage typically involves the use of dedicated linkage units who are supplied with personally identifying information to determine individuals from within and across datasets. The personally identifying information supplied to linkage units is separated from clinical information prior to release by data custodians. While this substantially reduces the risk of disclosure of sensitive information, some residual risks still exist and remain a concern for some custodians. In this paper we trial a method of record linkage which reduces privacy risk still further on large real world administrative data. The method uses encrypted personal identifying information (bloom filters) in a probability-based linkage framework. The privacy preserving linkage method was tested on ten years of New South Wales (NSW) and Western Australian (WA) hospital admissions data, comprising in total over 26 million records. No difference in linkage quality was found when the results were compared to traditional probabilistic methods using full unencrypted personal identifiers. This presents as a possible means of reducing privacy risks related to record linkage in population level research studies. It is hoped that through adaptations of this method or similar privacy preserving methods, risks related to information disclosure can be reduced so that the benefits of linked research taking place can be fully realised. Copyright © 2013 Elsevier Inc. All rights reserved.
Identifying and Mapping Linkages between Actors in the Climate ...
African Journals Online (AJOL)
Promoting innovations in climate change requires innovation partnerships and linkages and also creating an enabling environment for actors. The paper reviewed available information on the identification and mapping of linkages between actors in the climate change innovation system. The findings showed different ...
Agriculture–Tourism Linkages in Botswana: Evidence from the ...
African Journals Online (AJOL)
Tourism researchers are increasingly recognising that strengthened linkages between the sectors of tourism and agriculture are significant for maximising local multipliers and especially for pro-poor impacts. This article examines the linkages between the tourism and agriculture sectors in Botswana using evidence ...
Aldridge, Robert W; Shaji, Kunju; Hayward, Andrew C; Abubakar, Ibrahim
2015-01-01
The Enhanced Matching System (EMS) is a probabilistic record linkage program developed by the tuberculosis section at Public Health England to match data for individuals across two datasets. This paper outlines how EMS works and investigates its accuracy for linkage across public health datasets. EMS is a configurable Microsoft SQL Server database program. To examine the accuracy of EMS, two public health databases were matched using National Health Service (NHS) numbers as a gold standard unique identifier. Probabilistic linkage was then performed on the same two datasets without inclusion of NHS number. Sensitivity analyses were carried out to examine the effect of varying matching process parameters. Exact matching using NHS number between two datasets (containing 5931 and 1759 records) identified 1071 matched pairs. EMS probabilistic linkage identified 1068 record pairs. The sensitivity of probabilistic linkage was calculated as 99.5% (95%CI: 98.9, 99.8), specificity 100.0% (95%CI: 99.9, 100.0), positive predictive value 99.8% (95%CI: 99.3, 100.0), and negative predictive value 99.9% (95%CI: 99.8, 100.0). Probabilistic matching was most accurate when including address variables and using the automatically generated threshold for determining links with manual review. With the establishment of national electronic datasets across health and social care, EMS enables previously unanswerable research questions to be tackled with confidence in the accuracy of the linkage process. In scenarios where a small sample is being matched into a very large database (such as national records of hospital attendance) then, compared to results presented in this analysis, the positive predictive value or sensitivity may drop according to the prevalence of matches between databases. Despite this possible limitation, probabilistic linkage has great potential to be used where exact matching using a common identifier is not possible, including in low-income settings, and for vulnerable
DEFF Research Database (Denmark)
Christensen, Kim; Hounyo, Ulrich; Podolskij, Mark
In this paper, we propose a nonparametric way to test the hypothesis that time-variation in intraday volatility is caused solely by a deterministic and recurrent diurnal pattern. We assume that noisy high-frequency data from a discretely sampled jump-diffusion process are available. The test...... inference, we propose a new bootstrap approach, which leads to almost correctly sized tests of the null hypothesis. We apply the developed framework to a large cross-section of equity high-frequency data and find that the diurnal pattern accounts for a rather significant fraction of intraday variation...
Nonparametric bootstrap analysis with applications to demographic effects in demand functions.
Gozalo, P L
1997-12-01
"A new bootstrap proposal, labeled smooth conditional moment (SCM) bootstrap, is introduced for independent but not necessarily identically distributed data, where the classical bootstrap procedure fails.... A good example of the benefits of using nonparametric and bootstrap methods is the area of empirical demand analysis. In particular, we will be concerned with their application to the study of two important topics: what are the most relevant effects of household demographic variables on demand behavior, and to what extent present parametric specifications capture these effects." excerpt
Karpenko, S. S.; Zybin, E. Yu; Kosyanchuk, V. V.
2018-02-01
In this paper we design a nonparametric method for failures detection and localization in the aircraft control system that uses the measurements of the control signals and the aircraft states only. It doesn’t require a priori information of the aircraft model parameters, training or statistical calculations, and is based on algebraic solvability conditions for the aircraft model identification problem. This makes it possible to significantly increase the efficiency of detection and localization problem solution by completely eliminating errors, associated with aircraft model uncertainties.
Comparative Study of Parametric and Non-parametric Approaches in Fault Detection and Isolation
DEFF Research Database (Denmark)
Katebi, S.D.; Blanke, M.; Katebi, M.R.
This report describes a comparative study between two approaches to fault detection and isolation in dynamic systems. The first approach uses a parametric model of the system. The main components of such techniques are residual and signature generation for processing and analyzing. The second...... approach is non-parametric in the sense that the signature analysis is only dependent on the frequency or time domain information extracted directly from the input-output signals. Based on these approaches, two different fault monitoring schemes are developed where the feature extraction and fault decision...
Energy Technology Data Exchange (ETDEWEB)
Lopez Fontan, J.L.; Costa, J.; Ruso, J.M.; Prieto, G. [Dept. of Applied Physics, Univ. of Santiago de Compostela, Santiago de Compostela (Spain); Sarmiento, F. [Dept. of Mathematics, Faculty of Informatics, Univ. of A Coruna, A Coruna (Spain)
2004-02-01
The application of a statistical method, the local polynomial regression method, (LPRM), based on a nonparametric estimation of the regression function to determine the critical micelle concentration (cmc) is presented. The method is extremely flexible because it does not impose any parametric model on the subjacent structure of the data but rather allows the data to speak for themselves. Good concordance of cmc values with those obtained by other methods was found for systems in which the variation of a measured physical property with concentration showed an abrupt change. When this variation was slow, discrepancies between the values obtained by LPRM and others methods were found. (orig.)
Bhattacharya, Abhishek; Dunson, David B
2012-08-01
This article considers a broad class of kernel mixture density models on compact metric spaces and manifolds. Following a Bayesian approach with a nonparametric prior on the location mixing distribution, sufficient conditions are obtained on the kernel, prior and the underlying space for strong posterior consistency at any continuous density. The prior is also allowed to depend on the sample size n and sufficient conditions are obtained for weak and strong consistency. These conditions are verified on compact Euclidean spaces using multivariate Gaussian kernels, on the hypersphere using a von Mises-Fisher kernel and on the planar shape space using complex Watson kernels.
International Nuclear Information System (INIS)
Peterson, James R.; Haas, Timothy C.; Lee, Danny C.
2000-01-01
Natural resource professionals are increasingly required to develop rigorous statistical models that relate environmental data to categorical responses data. Recent advances in the statistical and computing sciences have led to the development of sophisticated methods for parametric and nonparametric analysis of data with categorical responses. The statistical software package CATDAT was designed to make some of these relatively new and powerful techniques available to scientists. The CATDAT statistical package includes 4 analytical techniques: generalized logit modeling; binary classification tree; extended K-nearest neighbor classification; and modular neural network
Kerschbamer, Rudolf
2015-05-01
This paper proposes a geometric delineation of distributional preference types and a non-parametric approach for their identification in a two-person context. It starts with a small set of assumptions on preferences and shows that this set (i) naturally results in a taxonomy of distributional archetypes that nests all empirically relevant types considered in previous work; and (ii) gives rise to a clean experimental identification procedure - the Equality Equivalence Test - that discriminates between archetypes according to core features of preferences rather than properties of specific modeling variants. As a by-product the test yields a two-dimensional index of preference intensity.
Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.
Tan, Qihua; Thomassen, Mads; Burton, Mark; Mose, Kristian Fredløv; Andersen, Klaus Ejner; Hjelmborg, Jacob; Kruse, Torben
2017-06-06
Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.
Non-parametric system identification from non-linear stochastic response
DEFF Research Database (Denmark)
Rüdinger, Finn; Krenk, Steen
2001-01-01
An estimation method is proposed for identification of non-linear stiffness and damping of single-degree-of-freedom systems under stationary white noise excitation. Non-parametric estimates of the stiffness and damping along with an estimate of the white noise intensity are obtained by suitable...... of the energy at mean-level crossings, which yields the damping relative to white noise intensity. Finally, an estimate of the noise intensity is extracted by estimating the absolute damping from the autocovariance functions of a set of modified phase plane variables at different energy levels. The method...
Trade-FDI Linkages in a System of Gravity Equations for German Regional Data
DEFF Research Database (Denmark)
Mitze, Timo; Alecke, Björn; Untiedt, Gerhard
We analyse the nature of German trade-FDI linkages within the EU27 based on a simultaneous equation gravity approach for imports, exports, in- and outward FDI stocks.We adopt both a Hausman-Taylor (1981) IV approach (3SLS-GMM) and rival non-IV estimation (the system extension to the Fixed Effects...... substitutive links between trade flows and outward FDI in line with earlier empirical evidence for Germany. Building upon German state level data we are also able to analyse the sensitivity of the results for regional sub-samples. The latter disaggregation hints at structural differences among the trade...
The Nonlinear Dynamic Relationship of Exchange Rates: Parametric and Nonparametric Causality testing
Bekiros, S.D.; Diks, C.
2007-01-01
The present study investigates the long-term linear and nonlinear causal linkages among six currencies, namely EUR/USD, GBP/USD, USD/JPY, USD/CHF, AUD/USD and USD/CAD. The prime motivation for choosing these exchange rates comes from the fact that they are the most liquid and widely traded, covering
Association and interaction analyses of eight genes under asthma linkage peaks
DEFF Research Database (Denmark)
Ferreira, M.A.R.; Zhao, Z.Z.; Thomsen, S.F.
2009-01-01
polymorphisms (SNP) located in eight genes (CD28, CTLA4, ICOS, ADAM23, ADAMTSL1, MS4A2, CDH26 and HRH3) were genotyped in > 5000 individuals from Australian (n = 1162), Dutch (n = 99) and Danish (n = 303) families. Traits tested included doctor-diagnosed asthma, atopy, airway obstruction, total serum...
Kraakman, A.T.W.
2005-01-01
Plants is mostly done through linkage analysis. A segregating mapping population Identification and mappping of Quantitative Trait Loci (QTLs) in is created from a bi-parental cross and linkages between trait values and mapped markers reveal the positions ofQTLs. In
Kraakman, A.T.W.
2005-01-01
Plants is mostly done through linkage analysis. A segregating mapping population Identification and mappping of Quantitative Trait Loci (QTLs) in is created from a bi-parental cross and linkages between trait values and mapped markers reveal the positions ofQTLs. Inthisstudyweexploredlinkagedisequilibrium(LD)mappingof traits in a set of modernbarleycultivars. LDbetweenmolecularmarkerswasfoundup to a distance of 10 centimorgan,whichislargecomparedtootherspecies.Thelarge distancemightbeinducedb...
Liu, Ren-Hu; Meng, Jin-Ling
2003-05-01
MAPMAKER is one of the most widely used computer software package for constructing genetic linkage maps.However, the PC version, MAPMAKER 3.0 for PC, could not draw the genetic linkage maps that its Macintosh version, MAPMAKER 3.0 for Macintosh,was able to do. Especially in recent years, Macintosh computer is much less popular than PC. Most of the geneticists use PC to analyze their genetic linkage data. So a new computer software to draw the same genetic linkage maps on PC as the MAPMAKER for Macintosh to do on Macintosh has been crying for. Microsoft Excel,one component of Microsoft Office package, is one of the most popular software in laboratory data processing. Microsoft Visual Basic for Applications (VBA) is one of the most powerful functions of Microsoft Excel. Using this program language, we can take creative control of Excel, including genetic linkage map construction, automatic data processing and more. In this paper, a Microsoft Excel macro called MapDraw is constructed to draw genetic linkage maps on PC computer based on given genetic linkage data. Use this software,you can freely construct beautiful genetic linkage map in Excel and freely edit and copy it to Word or other application. This software is just an Excel format file. You can freely copy it from ftp://211.69.140.177 or ftp://brassica.hzau.edu.cn and the source code can be found in Excel's Visual Basic Editor.
Nonsyndromic cleft lip and palate: No evidence of linkage to HLA or factor 13A
Energy Technology Data Exchange (ETDEWEB)
Hecht, J.T.; Yaping Wang; Connor, B.; Daiger, S.P. (Univ. of Texas, Houston (United States)); Blanton, S.H. (Univ. of Texas, Houston (United States) Univ. of Virginia, Charlottesville (United States))
1993-06-01
Nonsyndromic cleft lip with or without cleft palate (CLP) is a common craniofacial anomaly, the etiology of which is not known. Population studies have shown that a large proportion of cases occur sporadically. Recently, segregation analyses applied to CLP families have demonstrated that an autosomal dominant/codominant gene(s) may cause clefting in cases. Associations of autosomal dominant CLP and nonsyndromic cleft palate (CP) with HLA and F13A genes on chromosome 6p have been suggested previously. Linkage to these two areas on chromosome 6p were tested in 12 autosomal dominant families with CLP. With a LOD score of [minus]2 or less for exclusion, no evidence of linkage was found to four chromosome 6p markers. Multipoint analysis showed no evidence of a clefting locus in this region spanning 54 cM on chromosome 6p in these CLP families. 30 refs., 2 figs., 1 tab.
Energy Technology Data Exchange (ETDEWEB)
Jin, Sang Ok; Kim, Ji Hoon; Bae, Jang Ho; Kim, Jong Won [School of Mechanical and Aerospace Engineering, Seoul National University, Seoul (Korea, Republic of); Seo, Tae Won [School of Mechanical Engineering, Yeungnam University, Gyeongsan (Korea, Republic of)
2016-09-15
Underwater manipulators are very important for a robot to perform a specific operation in water. Conventional robot arm manipulators have been suggested for various operations but have not been suitable for repeated motion in gathering something. This paper presents a new underwater manipulator design for gathering things such as starfish on the sea floor. The manipulator is composed of a four-bar linkage to achieve repeated motion along a loop and compliant linkages to enhance the efficiency of the gathering work. Kinematic and quasi-static analyses were performed to calculate the loop path and the reaction force at the actuation point. Based on the analysis, optimal design was performed to maximize the working distance with the height difference and the reaction moments considered as constraints. A prototype was assembled to test the performance of the manipulator, and the empirical loop path was compared to simulation results.
International Nuclear Information System (INIS)
Jin, Sang Ok; Kim, Ji Hoon; Bae, Jang Ho; Kim, Jong Won; Seo, Tae Won
2016-01-01
Underwater manipulators are very important for a robot to perform a specific operation in water. Conventional robot arm manipulators have been suggested for various operations but have not been suitable for repeated motion in gathering something. This paper presents a new underwater manipulator design for gathering things such as starfish on the sea floor. The manipulator is composed of a four-bar linkage to achieve repeated motion along a loop and compliant linkages to enhance the efficiency of the gathering work. Kinematic and quasi-static analyses were performed to calculate the loop path and the reaction force at the actuation point. Based on the analysis, optimal design was performed to maximize the working distance with the height difference and the reaction moments considered as constraints. A prototype was assembled to test the performance of the manipulator, and the empirical loop path was compared to simulation results
Energy Technology Data Exchange (ETDEWEB)
Li, Nan-Nan; Zheng, Bing-Na [DSAPM Lab and PCFM Lab, Institute of Polymer Science, School of Chemistry and Chemical Engineering, Sun Yat-sen University, Guangzhou 510275 (China); Lin, Jian-Tao [DSAPM Lab and PCFM Lab, Institute of Polymer Science, School of Chemistry and Chemical Engineering, Sun Yat-sen University, Guangzhou 510275 (China); Guangdong Medical College, Dongguan 523808 (China); Zhang, Li-Ming, E-mail: ceszhlm@mail.sysu.edu.cn [DSAPM Lab and PCFM Lab, Institute of Polymer Science, School of Chemistry and Chemical Engineering, Sun Yat-sen University, Guangzhou 510275 (China)
2014-01-01
New heparin–indomethacin conjugate with an ester linkage was prepared by the carbodiimide-mediated condensation reaction, and then characterized by FTIR and {sup 1}HNMR analyses. Due to its amphiphilic character, such a conjugate could self-aggregate into spherical nanoparticles in aqueous system, as confirmed by fluorescence spectroscopy, dynamic light scattering and transmission electron microscopy. By the in vitro drug release tests, the resultant conjugate nanoparticles were found to have a sustained and esterase-sensitive release behavior for conjugated indomethacin. In addition, the uptake of these conjugate nanoparticles into human nasopharyngeal carcinoma CNE1 cells was confirmed by fluorescence microscopy. - Highlights: • New heparin–indomethacin conjugate with an ester linkage was prepared. • Such a conjugate could self-aggregate into spherical nanoparticles in aqueous system. • The resultant conjugate nanoparticles exhibited an esterase-sensitive drug release behavior. • The resultant conjugate nanoparticles showed the cellular uptake ability in CNE1 cells.
Measuring energy performance with sectoral heterogeneity: A non-parametric frontier approach
International Nuclear Information System (INIS)
Wang, H.; Ang, B.W.; Wang, Q.W.; Zhou, P.
2017-01-01
Evaluating economy-wide energy performance is an integral part of assessing the effectiveness of a country's energy efficiency policy. Non-parametric frontier approach has been widely used by researchers for such a purpose. This paper proposes an extended non-parametric frontier approach to studying economy-wide energy efficiency and productivity performances by accounting for sectoral heterogeneity. Relevant techniques in index number theory are incorporated to quantify the driving forces behind changes in the economy-wide energy productivity index. The proposed approach facilitates flexible modelling of different sectors' production processes, and helps to examine sectors' impact on the aggregate energy performance. A case study of China's economy-wide energy efficiency and productivity performances in its 11th five-year plan period (2006–2010) is presented. It is found that sectoral heterogeneities in terms of energy performance are significant in China. Meanwhile, China's economy-wide energy productivity increased slightly during the study period, mainly driven by the technical efficiency improvement. A number of other findings have also been reported. - Highlights: • We model economy-wide energy performance by considering sectoral heterogeneity. • The proposed approach can identify sectors' impact on the aggregate energy performance. • Obvious sectoral heterogeneities are identified in evaluating China's energy performance.
Nonparametric Identification of Glucose-Insulin Process in IDDM Patient with Multi-meal Disturbance
Bhattacharjee, A.; Sutradhar, A.
2012-12-01
Modern close loop control for blood glucose level in a diabetic patient necessarily uses an explicit model of the process. A fixed parameter full order or reduced order model does not characterize the inter-patient and intra-patient parameter variability. This paper deals with a frequency domain nonparametric identification of the nonlinear glucose-insulin process in an insulin dependent diabetes mellitus patient that captures the process dynamics in presence of uncertainties and parameter variations. An online frequency domain kernel estimation method has been proposed that uses the input-output data from the 19th order first principle model of the patient in intravenous route. Volterra equations up to second order kernels with extended input vector for a Hammerstein model are solved online by adaptive recursive least square (ARLS) algorithm. The frequency domain kernels are estimated using the harmonic excitation input data sequence from the virtual patient model. A short filter memory length of M = 2 was found sufficient to yield acceptable accuracy with lesser computation time. The nonparametric models are useful for closed loop control, where the frequency domain kernels can be directly used as the transfer function. The validation results show good fit both in frequency and time domain responses with nominal patient as well as with parameter variations.
kruX: matrix-based non-parametric eQTL discovery.
Qi, Jianlong; Asl, Hassan Foroughi; Björkegren, Johan; Michoel, Tom
2014-01-14
The Kruskal-Wallis test is a popular non-parametric statistical test for identifying expression quantitative trait loci (eQTLs) from genome-wide data due to its robustness against variations in the underlying genetic model and expression trait distribution, but testing billions of marker-trait combinations one-by-one can become computationally prohibitive. We developed kruX, an algorithm implemented in Matlab, Python and R that uses matrix multiplications to simultaneously calculate the Kruskal-Wallis test statistic for several millions of marker-trait combinations at once. KruX is more than ten thousand times faster than computing associations one-by-one on a typical human dataset. We used kruX and a dataset of more than 500k SNPs and 20k expression traits measured in 102 human blood samples to compare eQTLs detected by the Kruskal-Wallis test to eQTLs detected by the parametric ANOVA and linear model methods. We found that the Kruskal-Wallis test is more robust against data outliers and heterogeneous genotype group sizes and detects a higher proportion of non-linear associations, but is more conservative for calling additive linear associations. kruX enables the use of robust non-parametric methods for massive eQTL mapping without the need for a high-performance computing infrastructure and is freely available from http://krux.googlecode.com.
Chiu, Chun-Huo; Wang, Yi-Ting; Walther, Bruno A; Chao, Anne
2014-09-01
It is difficult to accurately estimate species richness if there are many almost undetectable species in a hyper-diverse community. Practically, an accurate lower bound for species richness is preferable to an inaccurate point estimator. The traditional nonparametric lower bound developed by Chao (1984, Scandinavian Journal of Statistics 11, 265-270) for individual-based abundance data uses only the information on the rarest species (the numbers of singletons and doubletons) to estimate the number of undetected species in samples. Applying a modified Good-Turing frequency formula, we derive an approximate formula for the first-order bias of this traditional lower bound. The approximate bias is estimated by using additional information (namely, the numbers of tripletons and quadrupletons). This approximate bias can be corrected, and an improved lower bound is thus obtained. The proposed lower bound is nonparametric in the sense that it is universally valid for any species abundance distribution. A similar type of improved lower bound can be derived for incidence data. We test our proposed lower bounds on simulated data sets generated from various species abundance models. Simulation results show that the proposed lower bounds always reduce bias over the traditional lower bounds and improve accuracy (as measured by mean squared error) when the heterogeneity of species abundances is relatively high. We also apply the proposed new lower bounds to real data for illustration and for comparisons with previously developed estimators. © 2014, The International Biometric Society.
Ryu, Duchwan
2010-09-28
We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves. © 2010, The International Biometric Society.
MEASURING DARK MATTER PROFILES NON-PARAMETRICALLY IN DWARF SPHEROIDALS: AN APPLICATION TO DRACO
International Nuclear Information System (INIS)
Jardel, John R.; Gebhardt, Karl; Fabricius, Maximilian H.; Williams, Michael J.; Drory, Niv
2013-01-01
We introduce a novel implementation of orbit-based (or Schwarzschild) modeling that allows dark matter density profiles to be calculated non-parametrically in nearby galaxies. Our models require no assumptions to be made about velocity anisotropy or the dark matter profile. The technique can be applied to any dispersion-supported stellar system, and we demonstrate its use by studying the Local Group dwarf spheroidal galaxy (dSph) Draco. We use existing kinematic data at larger radii and also present 12 new radial velocities within the central 13 pc obtained with the VIRUS-W integral field spectrograph on the 2.7 m telescope at McDonald Observatory. Our non-parametric Schwarzschild models find strong evidence that the dark matter profile in Draco is cuspy for 20 ≤ r ≤ 700 pc. The profile for r ≥ 20 pc is well fit by a power law with slope α = –1.0 ± 0.2, consistent with predictions from cold dark matter simulations. Our models confirm that, despite its low baryon content relative to other dSphs, Draco lives in a massive halo.
Robust non-parametric one-sample tests for the analysis of recurrent events.
Rebora, Paola; Galimberti, Stefania; Valsecchi, Maria Grazia
2010-12-30
One-sample non-parametric tests are proposed here for inference on recurring events. The focus is on the marginal mean function of events and the basis for inference is the standardized distance between the observed and the expected number of events under a specified reference rate. Different weights are considered in order to account for various types of alternative hypotheses on the mean function of the recurrent events process. A robust version and a stratified version of the test are also proposed. The performance of these tests was investigated through simulation studies under various underlying event generation processes, such as homogeneous and nonhomogeneous Poisson processes, autoregressive and renewal processes, with and without frailty effects. The robust versions of the test have been shown to be suitable in a wide variety of event generating processes. The motivating context is a study on gene therapy in a very rare immunodeficiency in children, where a major end-point is the recurrence of severe infections. Robust non-parametric one-sample tests for recurrent events can be useful to assess efficacy and especially safety in non-randomized studies or in epidemiological studies for comparison with a standard population. Copyright © 2010 John Wiley & Sons, Ltd.
Non-parametric transformation for data correlation and integration: From theory to practice
Energy Technology Data Exchange (ETDEWEB)
Datta-Gupta, A.; Xue, Guoping; Lee, Sang Heon [Texas A& M Univ., College Station, TX (United States)
1997-08-01
The purpose of this paper is two-fold. First, we introduce the use of non-parametric transformations for correlating petrophysical data during reservoir characterization. Such transformations are completely data driven and do not require a priori functional relationship between response and predictor variables which is the case with traditional multiple regression. The transformations are very general, computationally efficient and can easily handle mixed data types for example, continuous variables such as porosity, permeability and categorical variables such as rock type, lithofacies. The power of the non-parametric transformation techniques for data correlation has been illustrated through synthetic and field examples. Second, we utilize these transformations to propose a two-stage approach for data integration during heterogeneity characterization. The principal advantages of our approach over traditional cokriging or cosimulation methods are: (1) it does not require a linear relationship between primary and secondary data, (2) it exploits the secondary information to its fullest potential by maximizing the correlation between the primary and secondary data, (3) it can be easily applied to cases where several types of secondary or soft data are involved, and (4) it significantly reduces variance function calculations and thus, greatly facilitates non-Gaussian cosimulation. We demonstrate the data integration procedure using synthetic and field examples. The field example involves estimation of pore-footage distribution using well data and multiple seismic attributes.
Directory of Open Access Journals (Sweden)
Urbi Garay
2016-03-01
Full Text Available We define a dynamic and self-adjusting mixture of Gaussian Graphical Models to cluster financial returns, and provide a new method for extraction of nonparametric estimates of dynamic alphas (excess return and betas (to a choice set of explanatory factors in a multivariate setting. This approach, as well as the outputs, has a dynamic, nonstationary and nonparametric form, which circumvents the problem of model risk and parametric assumptions that the Kalman filter and other widely used approaches rely on. The by-product of clusters, used for shrinkage and information borrowing, can be of use to determine relationships around specific events. This approach exhibits a smaller Root Mean Squared Error than traditionally used benchmarks in financial settings, which we illustrate through simulation. As an illustration, we use hedge fund index data, and find that our estimated alphas are, on average, 0.13% per month higher (1.6% per year than alphas estimated through Ordinary Least Squares. The approach exhibits fast adaptation to abrupt changes in the parameters, as seen in our estimated alphas and betas, which exhibit high volatility, especially in periods which can be identified as times of stressful market events, a reflection of the dynamic positioning of hedge fund portfolio managers.
Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model
Directory of Open Access Journals (Sweden)
Isaac Mugume
2016-01-01
Full Text Available Numerical models are presently applied in many fields for simulation and prediction, operation, or research. The output from these models normally has both systematic and random errors. The study compared January 2015 temperature data for Uganda as simulated using the Weather Research and Forecast model with actual observed station temperature data to analyze the bias using parametric (the root mean square error (RMSE, the mean absolute error (MAE, mean error (ME, skewness, and the bias easy estimate (BES and nonparametric (the sign test, STM methods. The RMSE normally overestimates the error compared to MAE. The RMSE and MAE are not sensitive to direction of bias. The ME gives both direction and magnitude of bias but can be distorted by extreme values while the BES is insensitive to extreme values. The STM is robust for giving the direction of bias; it is not sensitive to extreme values but it does not give the magnitude of bias. The graphical tools (such as time series and cumulative curves show the performance of the model with time. It is recommended to integrate parametric and nonparametric methods along with graphical methods for a comprehensive analysis of bias of a numerical model.
Park, Taeyoung; Jeong, Jong-Hyeon; Lee, Jae Won
2012-08-15
There is often an interest in estimating a residual life function as a summary measure of survival data. For ease in presentation of the potential therapeutic effect of a new drug, investigators may summarize survival data in terms of the remaining life years of patients. Under heavy right censoring, however, some reasonably high quantiles (e.g., median) of a residual lifetime distribution cannot be always estimated via a popular nonparametric approach on the basis of the Kaplan-Meier estimator. To overcome the difficulties in dealing with heavily censored survival data, this paper develops a Bayesian nonparametric approach that takes advantage of a fully model-based but highly flexible probabilistic framework. We use a Dirichlet process mixture of Weibull distributions to avoid strong parametric assumptions on the unknown failure time distribution, making it possible to estimate any quantile residual life function under heavy censoring. Posterior computation through Markov chain Monte Carlo is straightforward and efficient because of conjugacy properties and partial collapse. We illustrate the proposed methods by using both simulated data and heavily censored survival data from a recent breast cancer clinical trial conducted by the National Surgical Adjuvant Breast and Bowel Project. Copyright © 2012 John Wiley & Sons, Ltd.
A semi-nonparametric mixture model for selecting functionally consistent proteins.
Yu, Lianbo; Doerge, Rw
2010-09-28
High-throughput technologies have led to a new era of proteomics. Although protein microarray experiments are becoming more common place there are a variety of experimental and statistical issues that have yet to be addressed, and that will carry over to new high-throughput technologies unless they are investigated. One of the largest of these challenges is the selection of functionally consistent proteins. We present a novel semi-nonparametric mixture model for classifying proteins as consistent or inconsistent while controlling the false discovery rate and the false non-discovery rate. The performance of the proposed approach is compared to current methods via simulation under a variety of experimental conditions. We provide a statistical method for selecting functionally consistent proteins in the context of protein microarray experiments, but the proposed semi-nonparametric mixture model method can certainly be generalized to solve other mixture data problems. The main advantage of this approach is that it provides the posterior probability of consistency for each protein.
Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification.
Fan, Jianqing; Feng, Yang; Jiang, Jiancheng; Tong, Xin
We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing.
Transformation-invariant and nonparametric monotone smooth estimation of ROC curves.
Du, Pang; Tang, Liansheng
2009-01-30
When a new diagnostic test is developed, it is of interest to evaluate its accuracy in distinguishing diseased subjects from non-diseased subjects. The accuracy of the test is often evaluated by receiver operating characteristic (ROC) curves. Smooth ROC estimates are often preferable for continuous test results when the underlying ROC curves are in fact continuous. Nonparametric and parametric methods have been proposed by various authors to obtain smooth ROC curve estimates. However, there are certain drawbacks with the existing methods. Parametric methods need specific model assumptions. Nonparametric methods do not always satisfy the inherent properties of the ROC curves, such as monotonicity and transformation invariance. In this paper we propose a monotone spline approach to obtain smooth monotone ROC curves. Our method ensures important inherent properties of the underlying ROC curves, which include monotonicity, transformation invariance, and boundary constraints. We compare the finite sample performance of the newly proposed ROC method with other ROC smoothing methods in large-scale simulation studies. We illustrate our method through a real life example. Copyright (c) 2008 John Wiley & Sons, Ltd.
Akhmadaliev, S Z; Ambrosini, G; Amorim, A; Anderson, K; Andrieux, M L; Aubert, Bernard; Augé, E; Badaud, F; Baisin, L; Barreiro, F; Battistoni, G; Bazan, A; Bazizi, K; Belymam, A; Benchekroun, D; Berglund, S R; Berset, J C; Blanchot, G; Bogush, A A; Bohm, C; Boldea, V; Bonivento, W; Bosman, M; Bouhemaid, N; Breton, D; Brette, P; Bromberg, C; Budagov, Yu A; Burdin, S V; Calôba, L P; Camarena, F; Camin, D V; Canton, B; Caprini, M; Carvalho, J; Casado, M P; Castillo, M V; Cavalli, D; Cavalli-Sforza, M; Cavasinni, V; Chadelas, R; Chalifour, M; Chekhtman, A; Chevalley, J L; Chirikov-Zorin, I E; Chlachidze, G; Citterio, M; Cleland, W E; Clément, C; Cobal, M; Cogswell, F; Colas, Jacques; Collot, J; Cologna, S; Constantinescu, S; Costa, G; Costanzo, D; Crouau, M; Daudon, F; David, J; David, M; Davidek, T; Dawson, J; De, K; de La Taille, C; Del Peso, J; Del Prete, T; de Saintignon, P; Di Girolamo, B; Dinkespiler, B; Dita, S; Dodd, J; Dolejsi, J; Dolezal, Z; Downing, R; Dugne, J J; Dzahini, D; Efthymiopoulos, I; Errede, D; Errede, S; Evans, H; Eynard, G; Fassi, F; Fassnacht, P; Ferrari, A; Ferrer, A; Flaminio, Vincenzo; Fournier, D; Fumagalli, G; Gallas, E; Gaspar, M; Giakoumopoulou, V; Gianotti, F; Gildemeister, O; Giokaris, N; Glagolev, V; Glebov, V Yu; Gomes, A; González, V; González de la Hoz, S; Grabskii, V; Graugès-Pous, E; Grenier, P; Hakopian, H H; Haney, M; Hébrard, C; Henriques, A; Hervás, L; Higón, E; Holmgren, Sven Olof; Hostachy, J Y; Hoummada, A; Huston, J; Imbault, D; Ivanyushenkov, Yu M; Jézéquel, S; Johansson, E K; Jon-And, K; Jones, R; Juste, A; Kakurin, S; Karyukhin, A N; Khokhlov, Yu A; Khubua, J I; Klioukhine, V I; Kolachev, G M; Kopikov, S V; Kostrikov, M E; Kozlov, V; Krivkova, P; Kukhtin, V V; Kulagin, M; Kulchitskii, Yu A; Kuzmin, M V; Labarga, L; Laborie, G; Lacour, D; Laforge, B; Lami, S; Lapin, V; Le Dortz, O; Lefebvre, M; Le Flour, T; Leitner, R; Leltchouk, M; Li, J; Liablin, M V; Linossier, O; Lissauer, D; Lobkowicz, F; Lokajícek, M; Lomakin, Yu F; López-Amengual, J M; Lund-Jensen, B; Maio, A; Makowiecki, D S; Malyukov, S N; Mandelli, L; Mansoulié, B; Mapelli, Livio P; Marin, C P; Marrocchesi, P S; Marroquim, F; Martin, P; Maslennikov, A L; Massol, N; Mataix, L; Mazzanti, M; Mazzoni, E; Merritt, F S; Michel, B; Miller, R; Minashvili, I A; Miralles, L; Mnatzakanian, E A; Monnier, E; Montarou, G; Mornacchi, Giuseppe; Moynot, M; Muanza, G S; Nayman, P; Némécek, S; Nessi, Marzio; Nicoleau, S; Niculescu, M; Noppe, J M; Onofre, A; Pallin, D; Pantea, D; Paoletti, R; Park, I C; Parrour, G; Parsons, J; Pereira, A; Perini, L; Perlas, J A; Perrodo, P; Pilcher, J E; Pinhão, J; Plothow-Besch, Hartmute; Poggioli, Luc; Poirot, S; Price, L; Protopopov, Yu; Proudfoot, J; Puzo, P; Radeka, V; Rahm, David Charles; Reinmuth, G; Renzoni, G; Rescia, S; Resconi, S; Richards, R; Richer, J P; Roda, C; Rodier, S; Roldán, J; Romance, J B; Romanov, V; Romero, P; Rossel, F; Rusakovitch, N A; Sala, P; Sanchis, E; Sanders, H; Santoni, C; Santos, J; Sauvage, D; Sauvage, G; Sawyer, L; Says, L P; Schaffer, A C; Schwemling, P; Schwindling, J; Seguin-Moreau, N; Seidl, W; Seixas, J M; Selldén, B; Seman, M; Semenov, A; Serin, L; Shaldaev, E; Shochet, M J; Sidorov, V; Silva, J; Simaitis, V J; Simion, S; Sissakian, A N; Snopkov, R; Söderqvist, J; Solodkov, A A; Soloviev, A; Soloviev, I V; Sonderegger, P; Soustruznik, K; Spanó, F; Spiwoks, R; Stanek, R; Starchenko, E A; Stavina, P; Stephens, R; Suk, M; Surkov, A; Sykora, I; Takai, H; Tang, F; Tardell, S; Tartarelli, F; Tas, P; Teiger, J; Thaler, J; Thion, J; Tikhonov, Yu A; Tisserant, S; Tokar, S; Topilin, N D; Trka, Z; Turcotte, M; Valkár, S; Varanda, M J; Vartapetian, A H; Vazeille, F; Vichou, I; Vinogradov, V; Vorozhtsov, S B; Vuillemin, V; White, A; Wielers, M; Wingerter-Seez, I; Wolters, H; Yamdagni, N; Yosef, C; Zaitsev, A; Zitoun, R; Zolnierowski, Y
2002-01-01
This paper discusses hadron energy reconstruction for the ATLAS barrel prototype combined calorimeter (consisting of a lead-liquid argon electromagnetic part and an iron-scintillator hadronic part) in the framework of the nonparametrical method. The nonparametrical method utilizes only the known e/h ratios and the electron calibration constants and does not require the determination of any parameters by a minimization technique. Thus, this technique lends itself to an easy use in a first level trigger. The reconstructed mean values of the hadron energies are within +or-1% of the true values and the fractional energy resolution is [(58+or-3)%/ square root E+(2.5+or-0.3)%](+)(1.7+or-0.2)/E. The value of the e/h ratio obtained for the electromagnetic compartment of the combined calorimeter is 1.74+or-0.04 and agrees with the prediction that e/h >1.66 for this electromagnetic calorimeter. Results of a study of the longitudinal hadronic shower development are also presented. The data have been taken in the H8 beam...
Impulse response identification with deterministic inputs using non-parametric methods
International Nuclear Information System (INIS)
Bhargava, U.K.; Kashyap, R.L.; Goodman, D.M.
1985-01-01
This paper addresses the problem of impulse response identification using non-parametric methods. Although the techniques developed herein apply to the truncated, untruncated, and the circulant models, we focus on the truncated model which is useful in certain applications. Two methods of impulse response identification will be presented. The first is based on the minimization of the C/sub L/ Statistic, which is an estimate of the mean-square prediction error; the second is a Bayesian approach. For both of these methods, we consider the effects of using both the identity matrix and the Laplacian matrix as weights on the energy in the impulse response. In addition, we present a method for estimating the effective length of the impulse response. Estimating the length is particularly important in the truncated case. Finally, we develop a method for estimating the noise variance at the output. Often, prior information on the noise variance is not available, and a good estimate is crucial to the success of estimating the impulse response with a nonparametric technique
Macmillan, N A; Creelman, C D
1996-06-01
Can accuracy and response bias in two-stimulus, two-response recognition or detection experiments be measured nonparametrically? Pollack and Norman (1964) answered this question affirmatively for sensitivity, Hodos (1970) for bias: Both proposed measures based on triangular areas in receiver-operating characteristic space. Their papers, and especially a paper by Grier (1971) that provided computing formulas for the measures, continue to be heavily cited in a wide range of content areas. In our sample of articles, most authors described triangle-based measures as making fewer assumptions than measures associated with detection theory. However, we show that statistics based on products or ratios of right triangle areas, including a recently proposed bias index and a not-yetproposed but apparently plausible sensitivity index, are consistent with a decision process based on logistic distributions. Even the Pollack and Norman measure, which is based on non-right triangles, is approximately logistic for low values of sensitivity. Simple geometric models for sensitivity and bias are not nonparametric, even if their implications are not acknowledged in the defining publications.
Directory of Open Access Journals (Sweden)
Dharambir K Sanghera
Full Text Available In this investigation, we have carried out an autosomal genome-wide linkage analysis to map genes associated with type 2 diabetes (T2D and five quantitative traits of blood lipids including total cholesterol, high-density lipoprotein (HDL cholesterol, low-density lipoprotein (LDL cholesterol, very low-density lipoprotein (VLDL cholesterol, and triglycerides in a unique family-based cohort from the Sikh Diabetes Study (SDS. A total of 870 individuals (526 male/344 female from 321 families were successfully genotyped using 398 polymorphic microsatellite markers with an average spacing of 9.26 cM on the autosomes. Results of non-parametric multipoint linkage analysis using S(all statistics (implemented in Merlin did not reveal any chromosomal region to be significantly associated with T2D in this Sikh cohort. However, linkage analysis for lipid traits using QTL-ALL analysis revealed promising linkage signals with p≤0.005 for total cholesterol, LDL cholesterol, and HDL cholesterol at chromosomes 5p15, 9q21, 10p11, 10q21, and 22q13. The most significant signal (p = 0.0011 occurred at 10q21.2 for HDL cholesterol. We also observed linkage signals for total cholesterol at 22q13.32 (p = 0.0016 and 5p15.33 (p = 0.0031 and for LDL cholesterol at 10p11.23 (p = 0.0045. Interestingly, some of linkage regions identified in this Sikh population coincide with plausible candidate genes reported in recent genome-wide association and meta-analysis studies for lipid traits. Our study provides the first evidence of linkage for loci associated with quantitative lipid traits at four chromosomal regions in this Asian Indian population from Punjab. More detailed examination of these regions with more informative genotyping, sequencing, and functional studies should lead to rapid detection of novel targets of therapeutic importance.
The Structure-Function Linkage Database.
Akiva, Eyal; Brown, Shoshana; Almonacid, Daniel E; Barber, Alan E; Custer, Ashley F; Hicks, Michael A; Huang, Conrad C; Lauck, Florian; Mashiyama, Susan T; Meng, Elaine C; Mischel, David; Morris, John H; Ojha, Sunil; Schnoes, Alexandra M; Stryke, Doug; Yunes, Jeffrey M; Ferrin, Thomas E; Holliday, Gemma L; Babbitt, Patricia C
2014-01-01
The Structure-Function Linkage Database (SFLD, http://sfld.rbvi.ucsf.edu/) is a manually curated classification resource describing structure-function relationships for functionally diverse enzyme superfamilies. Members of such superfamilies are diverse in their overall reactions yet share a common ancestor and some conserved active site features associated with conserved functional attributes such as a partial reaction. Thus, despite their different functions, members of these superfamilies 'look alike', making them easy to misannotate. To address this complexity and enable rational transfer of functional features to unknowns only for those members for which we have sufficient functional information, we subdivide superfamily members into subgroups using sequence information, and lastly into families, sets of enzymes known to catalyze the same reaction using the same mechanistic strategy. Browsing and searching options in the SFLD provide access to all of these levels. The SFLD offers manually curated as well as automatically classified superfamily sets, both accompanied by search and download options for all hierarchical levels. Additional information includes multiple sequence alignments, tab-separated files of functional and other attributes, and sequence similarity networks. The latter provide a new and intuitively powerful way to visualize functional trends mapped to the context of sequence similarity.
The Structure–Function Linkage Database
Akiva, Eyal; Brown, Shoshana; Almonacid, Daniel E.; Barber, Alan E.; Custer, Ashley F.; Hicks, Michael A.; Huang, Conrad C.; Lauck, Florian; Mashiyama, Susan T.; Meng, Elaine C.; Mischel, David; Morris, John H.; Ojha, Sunil; Schnoes, Alexandra M.; Stryke, Doug; Yunes, Jeffrey M.; Ferrin, Thomas E.; Holliday, Gemma L.; Babbitt, Patricia C.
2014-01-01
The Structure–Function Linkage Database (SFLD, http://sfld.rbvi.ucsf.edu/) is a manually curated classification resource describing structure–function relationships for functionally diverse enzyme superfamilies. Members of such superfamilies are diverse in their overall reactions yet share a common ancestor and some conserved active site features associated with conserved functional attributes such as a partial reaction. Thus, despite their different functions, members of these superfamilies ‘look alike’, making them easy to misannotate. To address this complexity and enable rational transfer of functional features to unknowns only for those members for which we have sufficient functional information, we subdivide superfamily members into subgroups using sequence information, and lastly into families, sets of enzymes known to catalyze the same reaction using the same mechanistic strategy. Browsing and searching options in the SFLD provide access to all of these levels. The SFLD offers manually curated as well as automatically classified superfamily sets, both accompanied by search and download options for all hierarchical levels. Additional information includes multiple sequence alignments, tab-separated files of functional and other attributes, and sequence similarity networks. The latter provide a new and intuitively powerful way to visualize functional trends mapped to the context of sequence similarity. PMID:24271399
Linkages at Tourism Destinations: Challenges in Zanzibar
Directory of Open Access Journals (Sweden)
Wineaster Anderson
2011-06-01
Full Text Available This study explores challenges facing the linkages between the tourism industry and local suppliers at the destinations. During 2010 surveys involving hotel and restaurant operators, local suppliers and tourists were conducted in Zanzibar. Qualitative analysis of the perspectives of the respondents reveals the multitude of constraints. From operators, the main constraints include poor quality of the locally supplied products, business informalities, high transaction costs and violation of agreements by local suppliers. Low production levels, low prices offered by hotels and restaurants coupled with late payments for the products delivered were the most serious problems cited by local suppliers. There is also a certain degree of mistrust between the local suppliers and the operators. However, the source of the tourism products consumed in the hotels or restaurants was not a point of concern, at least from the tourists’ perspective. Strategies to bridge the demandsupply gaps in order to maximize the benefits of tourism, among the tools for fighting the rampant poverty, have been recommended.
ANALYSIS OF INTER SECTORAL LINKAGES IN SEMARANG REGENCY
Directory of Open Access Journals (Sweden)
Fafurida
2014-03-01
Full Text Available This research aims to analyze inter economic sectoral linkages and to arrange the Klassen typology of economic sectors in Semarang Regency. The Klassen typology is composed from the result of the linkage analysis. To construct the analysis, this paper also utulizes the input-output analysis. It finds that service sector has the highest backward linkage while farming sector has the highest forward linkage. Based on the Klassen typology analysis, sectors with the highest backward and forward linkages and potential to be the leading sector are farming sector, dan trade, hotel and restaurant sector.Keywords: Backward linkage,forward linkage, Klassen typologyJEL classification number: R15, O21AbstrakPenelitian ini bertujuan untuk mengkaji seberapa besar keterkaitan antar sektor ekonomi di Kabupaten Semarang dan memetakan tipologi Klassennya. Tipologi Klasen disusun berdasarkan hasil perhitungan analisis keterkaitannya. Untuk menyusun analisis tersebut, paper ini juga menggunakan analisis input-output. Hasil penelitian menunjukkan bahwa sektor jasa memiliki keterkaitan ke belakang tertinggi dibandingkan dengan sektor lainnya. Sementara itu, sektor pertanian merupakan sektor yang memiliki keterkaitan ke depan tertinggi. Berdasarkan hasil analisis tipologi Klassen, sektor yang memiliki keterkaitan ke depan dan ke belakang yang tinggi dan dapat menjadi sektor unggulan adalah sektor perdagangan, hotel dan sektor restoran.Kata kunci: Keterkaitan ke belakang, keterkaitan ke depan, tipologi KlassenJEL classification numbers: R15, O21
Posterior probability of linkage and maximal lod score.
Génin, E; Martinez, M; Clerget-Darpoux, F
1995-01-01
To detect linkage between a trait and a marker, Morton (1955) proposed to calculate the lod score z(theta 1) at a given value theta 1 of the recombination fraction. If z(theta 1) reaches +3 then linkage is concluded. However, in practice, lod scores are calculated for different values of the recombination fraction between 0 and 0.5 and the test is based on the maximum value of the lod score Zmax. The impact of this deviation of the test on the probability that in fact linkage does not exist, when linkage was concluded, is documented here. This posterior probability of no linkage can be derived by using Bayes' theorem. It is less than 5% when the lod score at a predetermined theta 1 is used for the test. But, for a Zmax of +3, we showed that it can reach 16.4%. Thus, considering a composite alternative hypothesis instead of a single one decreases the reliability of the test. The reliability decreases rapidly when Zmax is less than +3. Given a Zmax of +2.5, there is a 33% chance that linkage does not exist. Moreover, the posterior probability depends not only on the value of Zmax but also jointly on the family structures and on the genetic model. For a given Zmax, the chance that linkage exists may then vary.
Statistical reliability analyses of two wood plastic composite extrusion processes
International Nuclear Information System (INIS)
Crookston, Kevin A.; Mark Young, Timothy; Harper, David; Guess, Frank M.
2011-01-01
Estimates of the reliability of wood plastic composites (WPC) are explored for two industrial extrusion lines. The goal of the paper is to use parametric and non-parametric analyses to examine potential differences in the WPC metrics of reliability for the two extrusion lines that may be helpful for use by the practitioner. A parametric analysis of the extrusion lines reveals some similarities and disparities in the best models; however, a non-parametric analysis reveals unique and insightful differences between Kaplan-Meier survival curves for the modulus of elasticity (MOE) and modulus of rupture (MOR) of the WPC industrial data. The distinctive non-parametric comparisons indicate the source of the differences in strength between the 10.2% and 48.0% fractiles [3,183-3,517 MPa] for MOE and for MOR between the 2.0% and 95.1% fractiles [18.9-25.7 MPa]. Distribution fitting as related to selection of the proper statistical methods is discussed with relevance to estimating the reliability of WPC. The ability to detect statistical differences in the product reliability of WPC between extrusion processes may benefit WPC producers in improving product reliability and safety of this widely used house-decking product. The approach can be applied to many other safety and complex system lifetime comparisons.
Energy Technology Data Exchange (ETDEWEB)
Ford, Eric B.; /Florida U.; Fabrycky, Daniel C.; /Lick Observ.; Steffen, Jason H.; /Fermilab; Carter, Joshua A.; /Harvard-Smithsonian Ctr. Astrophys.; Fressin, Francois; /Harvard-Smithsonian Ctr. Astrophys.; Holman, Matthew J.; /Harvard-Smithsonian Ctr. Astrophys.; Lissauer, Jack J.; /NASA, Ames; Moorhead, Althea V.; /Florida U.; Morehead, Robert C.; /Florida U.; Ragozzine, Darin; /Harvard-Smithsonian Ctr. Astrophys.; Rowe, Jason F.; /NASA, Ames /SETI Inst., Mtn. View /San Diego State U., Astron. Dept.
2012-01-01
We present a new method for confirming transiting planets based on the combination of transit timing variations (TTVs) and dynamical stability. Correlated TTVs provide evidence that the pair of bodies are in the same physical system. Orbital stability provides upper limits for the masses of the transiting companions that are in the planetary regime. This paper describes a non-parametric technique for quantifying the statistical significance of TTVs based on the correlation of two TTV data sets. We apply this method to an analysis of the transit timing variations of two stars with multiple transiting planet candidates identified by Kepler. We confirm four transiting planets in two multiple planet systems based on their TTVs and the constraints imposed by dynamical stability. An additional three candidates in these same systems are not confirmed as planets, but are likely to be validated as real planets once further observations and analyses are possible. If all were confirmed, these systems would be near 4:6:9 and 2:4:6:9 period commensurabilities. Our results demonstrate that TTVs provide a powerful tool for confirming transiting planets, including low-mass planets and planets around faint stars for which Doppler follow-up is not practical with existing facilities. Continued Kepler observations will dramatically improve the constraints on the planet masses and orbits and provide sensitivity for detecting additional non-transiting planets. If Kepler observations were extended to eight years, then a similar analysis could likely confirm systems with multiple closely spaced, small transiting planets in or near the habitable zone of solar-type stars.
International Nuclear Information System (INIS)
Ford, Eric B.; Moorhead, Althea V.; Morehead, Robert C.; Fabrycky, Daniel C.; Steffen, Jason H.; Carter, Joshua A.; Fressin, Francois; Holman, Matthew J.; Ragozzine, Darin; Charbonneau, David; Lissauer, Jack J.; Rowe, Jason F.; Borucki, William J.; Bryson, Stephen T.; Burke, Christopher J.; Caldwell, Douglas A.; Welsh, William F.; Allen, Christopher; Batalha, Natalie M.; Buchhave, Lars A.
2012-01-01
We present a new method for confirming transiting planets based on the combination of transit timing variations (TTVs) and dynamical stability. Correlated TTVs provide evidence that the pair of bodies is in the same physical system. Orbital stability provides upper limits for the masses of the transiting companions that are in the planetary regime. This paper describes a non-parametric technique for quantifying the statistical significance of TTVs based on the correlation of two TTV data sets. We apply this method to an analysis of the TTVs of two stars with multiple transiting planet candidates identified by Kepler. We confirm four transiting planets in two multiple-planet systems based on their TTVs and the constraints imposed by dynamical stability. An additional three candidates in these same systems are not confirmed as planets, but are likely to be validated as real planets once further observations and analyses are possible. If all were confirmed, these systems would be near 4:6:9 and 2:4:6:9 period commensurabilities. Our results demonstrate that TTVs provide a powerful tool for confirming transiting planets, including low-mass planets and planets around faint stars for which Doppler follow-up is not practical with existing facilities. Continued Kepler observations will dramatically improve the constraints on the planet masses and orbits and provide sensitivity for detecting additional non-transiting planets. If Kepler observations were extended to eight years, then a similar analysis could likely confirm systems with multiple closely spaced, small transiting planets in or near the habitable zone of solar-type stars.
Energy Technology Data Exchange (ETDEWEB)
Ford, Eric B.; Moorhead, Althea V.; Morehead, Robert C. [Astronomy Department, University of Florida, 211 Bryant Space Sciences Center, Gainesville, FL 32611 (United States); Fabrycky, Daniel C. [UCO/Lick Observatory, University of California, Santa Cruz, CA 95064 (United States); Steffen, Jason H. [Fermilab Center for Particle Astrophysics, P.O. Box 500, MS 127, Batavia, IL 60510 (United States); Carter, Joshua A.; Fressin, Francois; Holman, Matthew J.; Ragozzine, Darin; Charbonneau, David [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Lissauer, Jack J.; Rowe, Jason F.; Borucki, William J.; Bryson, Stephen T.; Burke, Christopher J.; Caldwell, Douglas A. [NASA Ames Research Center, Moffett Field, CA 94035 (United States); Welsh, William F. [Astronomy Department, San Diego State University, San Diego, CA 92182-1221 (United States); Allen, Christopher [Orbital Sciences Corporation/NASA Ames Research Center, Moffett Field, CA 94035 (United States); Batalha, Natalie M. [Department of Physics and Astronomy, San Jose State University, San Jose, CA 95192 (United States); Buchhave, Lars A., E-mail: eford@astro.ufl.edu [Niels Bohr Institute, Copenhagen University, DK-2100 Copenhagen (Denmark); Collaboration: Kepler Science Team; and others
2012-05-10
We present a new method for confirming transiting planets based on the combination of transit timing variations (TTVs) and dynamical stability. Correlated TTVs provide evidence that the pair of bodies is in the same physical system. Orbital stability provides upper limits for the masses of the transiting companions that are in the planetary regime. This paper describes a non-parametric technique for quantifying the statistical significance of TTVs based on the correlation of two TTV data sets. We apply this method to an analysis of the TTVs of two stars with multiple transiting planet candidates identified by Kepler. We confirm four transiting planets in two multiple-planet systems based on their TTVs and the constraints imposed by dynamical stability. An additional three candidates in these same systems are not confirmed as planets, but are likely to be validated as real planets once further observations and analyses are possible. If all were confirmed, these systems would be near 4:6:9 and 2:4:6:9 period commensurabilities. Our results demonstrate that TTVs provide a powerful tool for confirming transiting planets, including low-mass planets and planets around faint stars for which Doppler follow-up is not practical with existing facilities. Continued Kepler observations will dramatically improve the constraints on the planet masses and orbits and provide sensitivity for detecting additional non-transiting planets. If Kepler observations were extended to eight years, then a similar analysis could likely confirm systems with multiple closely spaced, small transiting planets in or near the habitable zone of solar-type stars.
Touw, D J; Vinks, A A; Neef, C
1997-06-01
The availability of personal computer programs for individualizing drug dosage regimens has stimulated the interest in modelling population pharmacokinetics. Data from 82 adolescent and adult patients with cystic fibrosis (CF) who were treated with intravenous tobramycin because of an exacerbation of their pulmonary infection were analysed with a non-parametric expectation maximization (NPEM) algorithm. This algorithm estimates the entire discrete joint probability density of the pharmacokinetic parameters. It also provides traditional parametric statistics such as the means, standard deviation, median, covariances and correlations among the various parameters. It also provides graphic-2- and 3-dimensional representations of the marginal densities of the parameters investigated. Several models for intravenous tobramycin in adolescent and adult patients with CF were compared. Covariates were total body weight (for the volume of distribution) and creatinine clearance (for the total body clearance and elimination rate). Because of lack of data on patients with poor renal function, restricted models with non-renal clearance and the non-renal elimination rate constant fixed at literature values of 0.15 L/h and 0.01 h-1 were also included. In this population, intravenous tobramycin could be best described by median (+/-dispersion factor) volume of distribution per unit of total body weight of 0.28 +/- 0.05 L/kg, elimination rate constant of 0.25 +/- 0.10 h-1 and elimination rate constant per unit of creatinine clearance of 0.0008 +/- 0.0009 h-1/(ml/min/1.73 m2). Analysis of populations of increasing size showed that using a restricted model with a non-renal elimination rate constant fixed at 0.01 h-1, a model based on a population of only 10 to 20 patients, contained parameter values similar to those of the entire population and, using the full model, a larger population (at least 40 patients) was needed.
Distribution of lod scores in oligogenic linkage analysis.
Williams, J T; North, K E; Martin, L J; Comuzzie, A G; Göring, H H; Blangero, J
2001-01-01
In variance component oligogenic linkage analysis it can happen that the residual additive genetic variance bounds to zero when estimating the effect of the ith quantitative trait locus. Using quantitative trait Q1 from the Genetic Analysis Workshop 12 simulated general population data, we compare the observed lod scores from oligogenic linkage analysis with the empirical lod score distribution under a null model of no linkage. We find that zero residual additive genetic variance in the null model alters the usual distribution of the likelihood-ratio statistic.
Bayesian linkage and segregation analysis: factoring the problem.
Matthysse, S
2000-01-01
Complex segregation analysis and linkage methods are mathematical techniques for the genetic dissection of complex diseases. They are used to delineate complex modes of familial transmission and to localize putative disease susceptibility loci to specific chromosomal locations. The computational problem of Bayesian linkage and segregation analysis is one of integration in high-dimensional spaces. In this paper, three available techniques for Bayesian linkage and segregation analysis are discussed: Markov Chain Monte Carlo (MCMC), importance sampling, and exact calculation. The contribution of each to the overall integration will be explicitly discussed.
Markov chain Monte Carlo linkage analysis: effect of bin width on the probability of linkage.
Slager, S L; Juo, S H; Durner, M; Hodge, S E
2001-01-01
We analyzed part of the Genetic Analysis Workshop (GAW) 12 simulated data using Monte Carlo Markov chain (MCMC) methods that are implemented in the computer program Loki. The MCMC method reports the "probability of linkage" (PL) across the chromosomal regions of interest. The point of maximum PL can then be taken as a "location estimate" for the location of the quantitative trait locus (QTL). However, Loki does not provide a formal statistical test of linkage. In this paper, we explore how the bin width used in the calculations affects the max PL and the location estimate. We analyzed age at onset (AO) and quantitative trait number 5, Q5, from 26 replicates of the general simulated data in one region where we knew a major gene, MG5, is located. For each trait, we found the max PL and the corresponding location estimate, using four different bin widths. We found that bin width, as expected, does affect the max PL and the location estimate, and we recommend that users of Loki explore how their results vary with different bin widths.
Review of the Linkages between Gender Equity and Climate ...
African Journals Online (AJOL)
Review of the Linkages between Gender Equity and Climate Change Issues in ... thereby exacerbating inequalities in health status and access to adequate food, clean ... Again, in traditional societies, women are even more vulnerable to the ...
Solid-Phase Synthesis of RNA Analogs Containing Phosphorodithioate Linkages.
Yang, Xianbin
2017-09-18
The oligoribonucleotide phosphorodithioate (PS2-RNA) modification uses two sulfur atoms to replace two non-bridging oxygen atoms at an internucleotide phosphorodiester backbone linkage. Like a natural phosphodiester RNA backbone linkage, a PS2-modified backbone linkage is achiral at phosphorus. PS2-RNAs are highly stable to nucleases and several in vitro assays have demonstrated their biological activity. For example, PS2-RNAs silenced mRNA in vitro and bound to protein targets in the form of PS2-aptamers (thioaptamers). Thus, the interest in and promise of PS2-RNAs has drawn attention to synthesizing, isolating, and characterizing these compounds. RNA-thiophosphoramidite monomers are commercially available from AM Biotechnologies and this unit describes an effective methodology for solid-phase synthesis, deprotection, and purification of RNAs having PS2 internucleotide linkages. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.
Mapping organizational linkages in the agricultural innovation system of Azerbaijan
Temel, T.
2004-01-01
This study describes the evolving context and organisational linkages in the agricultural innovation system of Azerbaijan and suggests ways to promote effective organisational ties for the development, distribution and use of new or improved information and knowledge related to agriculture.
Strengthening industry-research linkage for small scale industrial ...
African Journals Online (AJOL)
Strengthening industry-research linkage for small scale industrial development in Ghana - the relevance of scientific and technological information. ... Journal of Applied Science and Technology. Journal Home · ABOUT · Advanced Search ...
Underreporting of maternal mortality in Taiwan: A data linkage study
Directory of Open Access Journals (Sweden)
Tung-Pi Wu
2015-12-01
Conclusion: Approximately two-thirds of the maternal deaths in Taiwan were unreported in the officially published mortality data. Hence, routine nationwide data linkage is essential to monitor maternal mortality in Taiwan accurately.
Hsueh, W C; Göring, H H; Blangero, J; Mitchell, B D
2001-01-01
Replication of linkage signals from independent samples is considered an important step toward verifying the significance of linkage signals in studies of complex traits. The purpose of this empirical investigation was to examine the variability in the precision of localizing a quantitative trait locus (QTL) by analyzing multiple replicates of a simulated data set with the use of variance components-based methods. Specifically, we evaluated across replicates the variation in both the magnitude and the location of the peak lod scores. We analyzed QTLs whose effects accounted for 10-37% of the phenotypic variance in the quantitative traits. Our analyses revealed that the precision of QTL localization was directly related to the magnitude of the QTL effect. For a QTL with effect accounting for > 20% of total phenotypic variation, > 90% of the linkage peaks fall within 10 cM from the true gene location. We found no evidence that, for a given magnitude of the lod score, the presence of interaction influenced the precision of QTL localization.
Genotyping of PCR-based polymorphisms and linkage-disequilibrium analysis at the NF1 locus
Energy Technology Data Exchange (ETDEWEB)
Purandare, S.M.; Viskochil, D.H.; Cawthon, R. [Univ. of Utah, Salt Lake City, UT (United States)] [and others
1996-07-01
Six polymorphism across the NF1 gene have been adapted for genotyping through application of PCR-based assays. Three exon-based polymorphisms - at positions 702, 2034, and 10647 in the NF1 cDNA - were genotyped by mutagenically separated PCR (MS-PCR). A fourth polymorphism, DV1.9, is an L1 insertion element in intron 30, and the other two polymorphisms, GXAlu and EVI-20, are short tandem repeats in intron 27b. All the polymorphisms were evaluated in a cohort of 110 CEPH individuals who previously had been analyzed by use of eight RFLPs at the NF1 locus. Pairwise linkage-disequilibrium analyses with the six PCR-based polymorphisms and their flanking markers demonstrated disequilibrium between all tested loci. Genotypes of the four diallelic polymorphisms (702, 2034, 10647, and DV1.9) were also evaluated in cohorts from the CEPH, African, and Japanese populations. The CEPH and Japanese cohorts showed similar heterozygosities and linkage-disequilibrium coefficients. The African cohort showed a higher degree of heterozygosity and lower linkage-disequilibrium values, compared with the CEPH and Japanese cohorts. 36 refs., 2 figs., 3 tabs.
Leontief Input-Output Method for The Fresh Milk Distribution Linkage Analysis
Directory of Open Access Journals (Sweden)
Riski Nur Istiqomah
2016-11-01
Full Text Available This research discusses about linkage analysis and identifies the key sector in the fresh milk distribution using Leontief Input-Output method. This method is one of the application of Mathematics in economy. The current fresh milk distribution system includes dairy farmers →collectors→fresh milk processing industries→processed milk distributors→consumers. Then, the distribution is merged between the collectors’ axctivity and the fresh milk processing industry. The data used are primary and secondary data taken in June 2016 in Kecamatan Jabung Kabupaten Malang. The collected data are then analysed using Leontief Input-Output Matriks and Python (PYIO 2.1 software. The result is that the merging of the collectors’ and the fresh milk processing industry’s activities shows high indices of forward linkages and backward linkages. It is shown that merging of the two activities is the key sector which has an important role in developing the whole activities in the fresh milk distribution.
Estimating parameters for probabilistic linkage of privacy-preserved datasets.
Brown, Adrian P; Randall, Sean M; Ferrante, Anna M; Semmens, James B; Boyd, James H
2017-07-10
Probabilistic record linkage is a process used to bring together person-based records from within the same dataset (de-duplication) or from disparate datasets using pairwise comparisons and matching probabilities. The linkage strategy and associated match probabilities are often estimated through investigations into data quality and manual inspection. However, as privacy-preserved datasets comprise encrypted data, such methods are not possible. In this paper, we present a method for estimating the probabilities and threshold values for probabilistic privacy-preserved record linkage using Bloom filters. Our method was tested through a simulation study using synthetic data, followed by an application using real-world administrative data. Synthetic datasets were generated with error rates from zero to 20% error. Our method was used to estimate parameters (probabilities and thresholds) for de-duplication linkages. Linkage quality was determined by F-measure. Each dataset was privacy-preserved using separate Bloom filters for each field. Match probabilities were estimated using the expectation-maximisation (EM) algorithm on the privacy-preserved data. Threshold cut-off values were determined by an extension to the EM algorithm allowing linkage quality to be estimated for each possible threshold. De-duplication linkages of each privacy-preserved dataset were performed using both estimated and calculated probabilities. Linkage quality using the F-measure at the estimated threshold values was also compared to the highest F-measure. Three large administrative datasets were used to demonstrate the applicability of the probability and threshold estimation technique on real-world data. Linkage of the synthetic datasets using the estimated probabilities produced an F-measure that was comparable to the F-measure using calculated probabilities, even with up to 20% error. Linkage of the administrative datasets using estimated probabilities produced an F-measure that was higher
Logue, Mark W; Bauver, Sarah R; Knowles, James A; Gameroff, Marc J; Weissman, Myrna M; Crowe, Raymond R; Fyer, Abby J; Hamilton, Steven P
2012-04-01
Replication has been difficult to achieve in linkage studies of psychiatric disease. Linkage studies of panic disorder have indicated regions of interest on chromosomes 1q, 2p, 2q, 3, 7, 9, 11, 12q13, 12q23, and 15. Few regions have been implicated in more than one study. We examine two samples, the Iowa (IA) and the Columba panic disorder families. We use the fuzzy-clustering method presented by Kaabi et al. [Kaabi et al. (2006); Am J Hum Genet 78: 543-553] to summarize liability to panic disorder, agoraphobia, simple phobia, and social phobia. Kaabi et al. applied this method to the Yale panic disorder linkage families and found evidence of linkage to chromosomes 4q21, 4q32, 7p, and 8. When we apply the same method to the IA families, we obtain overlapping evidence of linkage to chromosomes 4q21 and 7p. Additionally, we find evidence of linkage on chromosomes 1, 5, 6, 16, and 22. The Columbia (CO) data does not indicate linkage to any of the Kaabi et al. peaks, instead implicating chromosomes 2 and 22q11 (2 Mb from COMT). There is some evidence of overlapping linkage between the IA and CO datasets on chromosomes 1 and 14. While use of fuzzy clustering has not produced complete concordance across datasets, it has produced more than previously seen in analyses of panic disorder proper. We conclude that chromosomes 4q21 and 7p should be considered strong candidate regions for panic and fear-associated anxiety disorder loci. More generally, this suggests that analyses including multiple aspects of psychopathology may lead to greater consistency across datasets. Copyright © 2012 Wiley Periodicals, Inc.
Learning Expressive Linkage Rules for Entity Matching using Genetic Programming
Isele, Robert
2013-01-01
A central problem in data integration and data cleansing is to identify pairs of entities in data sets that describe the same real-world object. Many existing methods for matching entities rely on explicit linkage rules, which specify how two entities are compared for equivalence. Unfortunately, writing accurate linkage rules by hand is a non-trivial problem that requires detailed knowledge of the involved data sets. Another important issue is the efficient execution of link...
Data Linkage: A powerful research tool with potential problems
Directory of Open Access Journals (Sweden)
Scott Ian
2010-12-01
Full Text Available Abstract Background Policy makers, clinicians and researchers are demonstrating increasing interest in using data linked from multiple sources to support measurement of clinical performance and patient health outcomes. However, the utility of data linkage may be compromised by sub-optimal or incomplete linkage, leading to systematic bias. In this study, we synthesize the evidence identifying participant or population characteristics that can influence the validity and completeness of data linkage and may be associated with systematic bias in reported outcomes. Methods A narrative review, using structured search methods was undertaken. Key words "data linkage" and Mesh term "medical record linkage" were applied to Medline, EMBASE and CINAHL databases between 1991 and 2007. Abstract inclusion criteria were; the article attempted an empirical evaluation of methodological issues relating to data linkage and reported on patient characteristics, the study design included analysis of matched versus unmatched records, and the report was in English. Included articles were grouped thematically according to patient characteristics that were compared between matched and unmatched records. Results The search identified 1810 articles of which 33 (1.8% met inclusion criteria. There was marked heterogeneity in study methods and factors investigated. Characteristics that were unevenly distributed among matched and unmatched records were; age (72% of studies, sex (50% of studies, race (64% of studies, geographical/hospital site (93% of studies, socio-economic status (82% of studies and health status (72% of studies. Conclusion A number of relevant patient or population factors may be associated with incomplete data linkage resulting in systematic bias in reported clinical outcomes. Readers should consider these factors in interpreting the reported results of data linkage studies.
Donnelly, Aoife; Misstear, Bruce; Broderick, Brian
2011-02-15
Background concentrations of nitrogen dioxide (NO(2)) are not constant but vary temporally and spatially. The current paper presents a powerful tool for the quantification of the effects of wind direction and wind speed on background NO(2) concentrations, particularly in cases where monitoring data are limited. In contrast to previous studies which applied similar methods to sites directly affected by local pollution sources, the current study focuses on background sites with the aim of improving methods for predicting background concentrations adopted in air quality modelling studies. The relationship between measured NO(2) concentration in air at three such sites in Ireland and locally measured wind direction has been quantified using nonparametric regression methods. The major aim was to analyse a method for quantifying the effects of local wind direction on background levels of NO(2) in Ireland. The method was expanded to include wind speed as an added predictor variable. A Gaussian kernel function is used in the analysis and circular statistics employed for the wind direction variable. Wind direction and wind speed were both found to have a statistically significant effect on background levels of NO(2) at all three sites. Frequently environmental impact assessments are based on short term baseline monitoring producing a limited dataset. The presented non-parametric regression methods, in contrast to the frequently used methods such as binning of the data, allow concentrations for missing data pairs to be estimated and distinction between spurious and true peaks in concentrations to be made. The methods were found to provide a realistic estimation of long term concentration variation with wind direction and speed, even for cases where the data set is limited. Accurate identification of the actual variation at each location and causative factors could be made, thus supporting the improved definition of background concentrations for use in air quality modelling
Directory of Open Access Journals (Sweden)
Yin Wang
2014-01-01
Full Text Available We present a nonparametric shape constrained algorithm for segmentation of coronary arteries in computed tomography images within the framework of active contours. An adaptive scale selection scheme, based on the global histogram information of the image data, is employed to determine the appropriate window size for each point on the active contour, which improves the performance of the active contour model in the low contrast local image regions. The possible leakage, which cannot be identified by using intensity features alone, is reduced through the application of the proposed shape constraint, where the shape of circular sampled intensity profile is used to evaluate the likelihood of current segmentation being considered vascular structures. Experiments on both synthetic and clinical datasets have demonstrated the efficiency and robustness of the proposed method. The results on clinical datasets have shown that the proposed approach is capable of extracting more detailed coronary vessels with subvoxel accuracy.
[Do we always correctly interpret the results of statistical nonparametric tests].
Moczko, Jerzy A
2014-01-01
Mann-Whitney, Wilcoxon, Kruskal-Wallis and Friedman tests create a group of commonly used tests to analyze the results of clinical and laboratory data. These tests are considered to be extremely flexible and their asymptotic relative efficiency exceeds 95 percent. Compared with the corresponding parametric tests they do not require checking the fulfillment of the conditions such as the normality of data distribution, homogeneity of variance, the lack of correlation means and standard deviations, etc. They can be used both in the interval and or-dinal scales. The article presents an example Mann-Whitney test, that does not in any case the choice of these four nonparametric tests treated as a kind of gold standard leads to correct inference.
Xu, Zhiqiang
2017-02-16
Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.
Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method.
Zhang, Tingting; Kou, S C
2010-01-01
Doubly stochastic Poisson processes, also known as the Cox processes, frequently occur in various scientific fields. In this article, motivated primarily by analyzing Cox process data in biophysics, we propose a nonparametric kernel-based inference method. We conduct a detailed study, including an asymptotic analysis, of the proposed method, and provide guidelines for its practical use, introducing a fast and stable regression method for bandwidth selection. We apply our method to real photon arrival data from recent single-molecule biophysical experiments, investigating proteins' conformational dynamics. Our result shows that conformational fluctuation is widely present in protein systems, and that the fluctuation covers a broad range of time scales, highlighting the dynamic and complex nature of proteins' structure.
A Nonparametric Operational Risk Modeling Approach Based on Cornish-Fisher Expansion
Directory of Open Access Journals (Sweden)
Xiaoqian Zhu
2014-01-01
Full Text Available It is generally accepted that the choice of severity distribution in loss distribution approach has a significant effect on the operational risk capital estimation. However, the usually used parametric approaches with predefined distribution assumption might be not able to fit the severity distribution accurately. The objective of this paper is to propose a nonparametric operational risk modeling approach based on Cornish-Fisher expansion. In this approach, the samples of severity are generated by Cornish-Fisher expansion and then used in the Monte Carlo simulation to sketch the annual operational loss distribution. In the experiment, the proposed approach is employed to calculate the operational risk capital charge for the overall Chinese banking. The experiment dataset is the most comprehensive operational risk dataset in China as far as we know. The results show that the proposed approach is able to use the information of high order moments and might be more effective and stable than the usually used parametric approach.
Xu, Zhiqiang; Cheng, James; Xiao, Xiaokui; Fujimaki, Ryohei; Muraoka, Yusuke
2017-01-01
Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.
Non-parametric Bayesian graph models reveal community structure in resting state fMRI
DEFF Research Database (Denmark)
Andersen, Kasper Winther; Madsen, Kristoffer H.; Siebner, Hartwig Roman
2014-01-01
Modeling of resting state functional magnetic resonance imaging (rs-fMRI) data using network models is of increasing interest. It is often desirable to group nodes into clusters to interpret the communication patterns between nodes. In this study we consider three different nonparametric Bayesian...... models for node clustering in complex networks. In particular, we test their ability to predict unseen data and their ability to reproduce clustering across datasets. The three generative models considered are the Infinite Relational Model (IRM), Bayesian Community Detection (BCD), and the Infinite...... between clusters. BCD restricts the between-cluster link probabilities to be strictly lower than within-cluster link probabilities to conform to the community structure typically seen in social networks. IDM only models a single between-cluster link probability, which can be interpreted as a background...
Parametric, nonparametric and parametric modelling of a chaotic circuit time series
Timmer, J.; Rust, H.; Horbelt, W.; Voss, H. U.
2000-09-01
The determination of a differential equation underlying a measured time series is a frequently arising task in nonlinear time series analysis. In the validation of a proposed model one often faces the dilemma that it is hard to decide whether possible discrepancies between the time series and model output are caused by an inappropriate model or by bad estimates of parameters in a correct type of model, or both. We propose a combination of parametric modelling based on Bock's multiple shooting algorithm and nonparametric modelling based on optimal transformations as a strategy to test proposed models and if rejected suggest and test new ones. We exemplify this strategy on an experimental time series from a chaotic circuit where we obtain an extremely accurate reconstruction of the observed attractor.
A non-parametric consistency test of the ΛCDM model with Planck CMB data
Energy Technology Data Exchange (ETDEWEB)
Aghamousa, Amir; Shafieloo, Arman [Korea Astronomy and Space Science Institute, Daejeon 305-348 (Korea, Republic of); Hamann, Jan, E-mail: amir@aghamousa.com, E-mail: jan.hamann@unsw.edu.au, E-mail: shafieloo@kasi.re.kr [School of Physics, The University of New South Wales, Sydney NSW 2052 (Australia)
2017-09-01
Non-parametric reconstruction methods, such as Gaussian process (GP) regression, provide a model-independent way of estimating an underlying function and its uncertainty from noisy data. We demonstrate how GP-reconstruction can be used as a consistency test between a given data set and a specific model by looking for structures in the residuals of the data with respect to the model's best-fit. Applying this formalism to the Planck temperature and polarisation power spectrum measurements, we test their global consistency with the predictions of the base ΛCDM model. Our results do not show any serious inconsistencies, lending further support to the interpretation of the base ΛCDM model as cosmology's gold standard.
Semi-nonparametric estimates of interfuel substitution in US energy demand
Energy Technology Data Exchange (ETDEWEB)
Serletis, A.; Shahmoradi, A. [University of Calgary, Calgary, AB (Canada). Dept. of Economics
2008-09-15
This paper focuses on the demand for crude oil, natural gas, and coal in the United States in the context of two globally flexible functional forms - the Fourier and the Asymptotically Ideal Model (AIM) - estimated subject to full regularity, using methods suggested over 20 years ago by Gallant and Golub (Gallant, A. Ronald and Golub, Gene H. Imposing Curvature Restrictions on Flexible Functional Forms. Journal of Econometrics 26 (1984), 295-321) and recently used by Serletis and Shahmoradi (Serletis, A., Shahmoradi, A., 2005. Semi-nonparametric estimates of the demand for money in the United States. Macroeconomic Dynamics 9, 542-559) in the monetary demand systems literature. We provide a comparison in terms of a full set of elasticities and also a policy perspective, using (for the first time) parameter estimates that are consistent with global regularity.
Semi-nonparametric estimates of interfuel substitution in U.S. energy demand
Energy Technology Data Exchange (ETDEWEB)
Serletis, Apostolos [Department of Economics, University of Calgary, Calgary, Alberta (Canada); Shahmoradi, Asghar [Faculty of Economics, University of Tehran, Tehran (Iran)
2008-09-15
This paper focuses on the demand for crude oil, natural gas, and coal in the United States in the context of two globally flexible functional forms - the Fourier and the Asymptotically Ideal Model (AIM) - estimated subject to full regularity, using methods suggested over 20 years ago by Gallant and Golub [Gallant, A. Ronald and Golub, Gene H. Imposing Curvature Restrictions on Flexible Functional Forms. Journal of Econometrics 26 (1984), 295-321] and recently used by Serletis and Shahmoradi [Serletis, A., Shahmoradi, A., 2005. Semi-nonparametric estimates of the demand for money in the United States. Macroeconomic Dynamics 9, 542-559] in the monetary demand systems literature. We provide a comparison in terms of a full set of elasticities and also a policy perspective, using (for the first time) parameter estimates that are consistent with global regularity. (author)
A Nonparametric, Multiple Imputation-Based Method for the Retrospective Integration of Data Sets
Carrig, Madeline M.; Manrique-Vallier, Daniel; Ranby, Krista W.; Reiter, Jerome P.; Hoyle, Rick H.
2015-01-01
Complex research questions often cannot be addressed adequately with a single data set. One sensible alternative to the high cost and effort associated with the creation of large new data sets is to combine existing data sets containing variables related to the constructs of interest. The goal of the present research was to develop a flexible, broadly applicable approach to the integration of disparate data sets that is based on nonparametric multiple imputation and the collection of data from a convenient, de novo calibration sample. We demonstrate proof of concept for the approach by integrating three existing data sets containing items related to the extent of problematic alcohol use and associations with deviant peers. We discuss both necessary conditions for the approach to work well and potential strengths and weaknesses of the method compared to other data set integration approaches. PMID:26257437
International Nuclear Information System (INIS)
Morio, Jerome
2011-01-01
Importance sampling (IS) is a useful simulation technique to estimate critical probability with a better accuracy than Monte Carlo methods. It consists in generating random weighted samples from an auxiliary distribution rather than the distribution of interest. The crucial part of this algorithm is the choice of an efficient auxiliary PDF that has to be able to simulate more rare random events. The optimisation of this auxiliary distribution is often in practice very difficult. In this article, we propose to approach the IS optimal auxiliary density with non-parametric adaptive importance sampling (NAIS). We apply this technique for the probability estimation of spatial launcher impact position since it has currently become a more and more important issue in the field of aeronautics.
Bayesian nonparametric modeling for comparison of single-neuron firing intensities.
Kottas, Athanasios; Behseta, Sam
2010-03-01
We propose a fully inferential model-based approach to the problem of comparing the firing patterns of a neuron recorded under two distinct experimental conditions. The methodology is based on nonhomogeneous Poisson process models for the firing times of each condition with flexible nonparametric mixture prior models for the corresponding intensity functions. We demonstrate posterior inferences from a global analysis, which may be used to compare the two conditions over the entire experimental time window, as well as from a pointwise analysis at selected time points to detect local deviations of firing patterns from one condition to another. We apply our method on two neurons recorded from the primary motor cortex area of a monkey's brain while performing a sequence of reaching tasks.
A BAYESIAN NONPARAMETRIC MIXTURE MODEL FOR SELECTING GENES AND GENE SUBNETWORKS.
Zhao, Yize; Kang, Jian; Yu, Tianwei
2014-06-01
It is very challenging to select informative features from tens of thousands of measured features in high-throughput data analysis. Recently, several parametric/regression models have been developed utilizing the gene network information to select genes or pathways strongly associated with a clinical/biological outcome. Alternatively, in this paper, we propose a nonparametric Bayesian model for gene selection incorporating network information. In addition to identifying genes that have a strong association with a clinical outcome, our model can select genes with particular expressional behavior, in which case the regression models are not directly applicable. We show that our proposed model is equivalent to an infinity mixture model for which we develop a posterior computation algorithm based on Markov chain Monte Carlo (MCMC) methods. We also propose two fast computing algorithms that approximate the posterior simulation with good accuracy but relatively low computational cost. We illustrate our methods on simulation studies and the analysis of Spellman yeast cell cycle microarray data.
Assessing T cell clonal size distribution: a non-parametric approach.
Directory of Open Access Journals (Sweden)
Olesya V Bolkhovskaya
Full Text Available Clonal structure of the human peripheral T-cell repertoire is shaped by a number of homeostatic mechanisms, including antigen presentation, cytokine and cell regulation. Its accurate tuning leads to a remarkable ability to combat pathogens in all their variety, while systemic failures may lead to severe consequences like autoimmune diseases. Here we develop and make use of a non-parametric statistical approach to assess T cell clonal size distributions from recent next generation sequencing data. For 41 healthy individuals and a patient with ankylosing spondylitis, who undergone treatment, we invariably find power law scaling over several decades and for the first time calculate quantitatively meaningful values of decay exponent. It has proved to be much the same among healthy donors, significantly different for an autoimmune patient before the therapy, and converging towards a typical value afterwards. We discuss implications of the findings for theoretical understanding and mathematical modeling of adaptive immunity.
Assessing T cell clonal size distribution: a non-parametric approach.
Bolkhovskaya, Olesya V; Zorin, Daniil Yu; Ivanchenko, Mikhail V
2014-01-01
Clonal structure of the human peripheral T-cell repertoire is shaped by a number of homeostatic mechanisms, including antigen presentation, cytokine and cell regulation. Its accurate tuning leads to a remarkable ability to combat pathogens in all their variety, while systemic failures may lead to severe consequences like autoimmune diseases. Here we develop and make use of a non-parametric statistical approach to assess T cell clonal size distributions from recent next generation sequencing data. For 41 healthy individuals and a patient with ankylosing spondylitis, who undergone treatment, we invariably find power law scaling over several decades and for the first time calculate quantitatively meaningful values of decay exponent. It has proved to be much the same among healthy donors, significantly different for an autoimmune patient before the therapy, and converging towards a typical value afterwards. We discuss implications of the findings for theoretical understanding and mathematical modeling of adaptive immunity.
Nonparametric estimation of age-specific reference percentile curves with radial smoothing.
Wan, Xiaohai; Qu, Yongming; Huang, Yao; Zhang, Xiao; Song, Hanping; Jiang, Honghua
2012-01-01
Reference percentile curves represent the covariate-dependent distribution of a quantitative measurement and are often used to summarize and monitor dynamic processes such as human growth. We propose a new nonparametric method based on a radial smoothing (RS) technique to estimate age-specific reference percentile curves assuming the underlying distribution is relatively close to normal. We compared the RS method with both the LMS and the generalized additive models for location, scale and shape (GAMLSS) methods using simulated data and found that our method has smaller estimation error than the two existing methods. We also applied the new method to analyze height growth data from children being followed in a clinical observational study of growth hormone treatment, and compared the growth curves between those with growth disorders and the general population. Copyright © 2011 Elsevier Inc. All rights reserved.
Rock, N. M. S.; Duffy, T. R.
REGRES allows a range of regression equations to be calculated for paired sets of data values in which both variables are subject to error (i.e. neither is the "independent" variable). Nonparametric regressions, based on medians of all possible pairwise slopes and intercepts, are treated in detail. Estimated slopes and intercepts are output, along with confidence limits, Spearman and Kendall rank correlation coefficients. Outliers can be rejected with user-determined stringency. Parametric regressions can be calculated for any value of λ (the ratio of the variances of the random errors for y and x)—including: (1) major axis ( λ = 1); (2) reduced major axis ( λ = variance of y/variance of x); (3) Y on Xλ = infinity; or (4) X on Y ( λ = 0) solutions. Pearson linear correlation coefficients also are output. REGRES provides an alternative to conventional isochron assessment techniques where bivariate normal errors cannot be assumed, or weighting methods are inappropriate.
Nonparametric Methods in Astronomy: Think, Regress, Observe—Pick Any Three
Steinhardt, Charles L.; Jermyn, Adam S.
2018-02-01
Telescopes are much more expensive than astronomers, so it is essential to minimize required sample sizes by using the most data-efficient statistical methods possible. However, the most commonly used model-independent techniques for finding the relationship between two variables in astronomy are flawed. In the worst case they can lead without warning to subtly yet catastrophically wrong results, and even in the best case they require more data than necessary. Unfortunately, there is no single best technique for nonparametric regression. Instead, we provide a guide for how astronomers can choose the best method for their specific problem and provide a python library with both wrappers for the most useful existing algorithms and implementations of two new algorithms developed here.
Energy Technology Data Exchange (ETDEWEB)
Takamizawa, Hisashi, E-mail: takamizawa.hisashi@jaea.go.jp; Itoh, Hiroto, E-mail: ito.hiroto@jaea.go.jp; Nishiyama, Yutaka, E-mail: nishiyama.yutaka93@jaea.go.jp
2016-10-15
In order to understand neutron irradiation embrittlement in high fluence regions, statistical analysis using the Bayesian nonparametric (BNP) method was performed for the Japanese surveillance and material test reactor irradiation database. The BNP method is essentially expressed as an infinite summation of normal distributions, with input data being subdivided into clusters with identical statistical parameters, such as mean and standard deviation, for each cluster to estimate shifts in ductile-to-brittle transition temperature (DBTT). The clusters typically depend on chemical compositions, irradiation conditions, and the irradiation embrittlement. Specific variables contributing to the irradiation embrittlement include the content of Cu, Ni, P, Si, and Mn in the pressure vessel steels, neutron flux, neutron fluence, and irradiation temperatures. It was found that the measured shifts of DBTT correlated well with the calculated ones. Data associated with the same materials were subdivided into the same clusters even if neutron fluences were increased.
Using nonparametrics to specify a model to measure the value of travel time
DEFF Research Database (Denmark)
Fosgerau, Mogens
2007-01-01
Using a range of nonparametric methods, the paper examines the specification of a model to evaluate the willingness-to-pay (WTP) for travel time changes from binomial choice data from a simple time-cost trading experiment. The analysis favours a model with random WTP as the only source...... of randomness over a model with fixed WTP which is linear in time and cost and has an additive random error term. Results further indicate that the distribution of log WTP can be described as a sum of a linear index fixing the location of the log WTP distribution and an independent random variable representing...... unobserved heterogeneity. This formulation is useful for parametric modelling. The index indicates that the WTP varies systematically with income and other individual characteristics. The WTP varies also with the time difference presented in the experiment which is in contradiction of standard utility theory....
The application of non-parametric statistical method for an ALARA implementation
International Nuclear Information System (INIS)
Cho, Young Ho; Herr, Young Hoi
2003-01-01
The cost-effective reduction of Occupational Radiation Dose (ORD) at a nuclear power plant could not be achieved without going through an extensive analysis of accumulated ORD data of existing plants. Through the data analysis, it is required to identify what are the jobs of repetitive high ORD at the nuclear power plant. In this study, Percentile Rank Sum Method (PRSM) is proposed to identify repetitive high ORD jobs, which is based on non-parametric statistical theory. As a case study, the method is applied to ORD data of maintenance and repair jobs at Kori units 3 and 4 that are pressurized water reactors with 950 MWe capacity and have been operated since 1986 and 1987, respectively in Korea. The results was verified and validated, and PRSM has been demonstrated to be an efficient method of analyzing the data
Rogers, Jeffrey N.; Parrish, Christopher E.; Ward, Larry G.; Burdick, David M.
2018-03-01
Salt marsh vegetation tends to increase vertical uncertainty in light detection and ranging (lidar) derived elevation data, often causing the data to become ineffective for analysis of topographic features governing tidal inundation or vegetation zonation. Previous attempts at improving lidar data collected in salt marsh environments range from simply computing and subtracting the global elevation bias to more complex methods such as computing vegetation-specific, constant correction factors. The vegetation specific corrections can be used along with an existing habitat map to apply separate corrections to different areas within a study site. It is hypothesized here that correcting salt marsh lidar data by applying location-specific, point-by-point corrections, which are computed from lidar waveform-derived features, tidal-datum based elevation, distance from shoreline and other lidar digital elevation model based variables, using nonparametric regression will produce better results. The methods were developed and tested using full-waveform lidar and ground truth for three marshes in Cape Cod, Massachusetts, U.S.A. Five different model algorithms for nonparametric regression were evaluated, with TreeNet's stochastic gradient boosting algorithm consistently producing better regression and classification results. Additionally, models were constructed to predict the vegetative zone (high marsh and low marsh). The predictive modeling methods used in this study estimated ground elevation with a mean bias of 0.00 m and a standard deviation of 0.07 m (0.07 m root mean square error). These methods appear very promising for correction of salt marsh lidar data and, importantly, do not require an existing habitat map, biomass measurements, or image based remote sensing data such as multi/hyperspectral imagery.
Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in R
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Terrance D. Savitsky
2016-08-01
Full Text Available We present growfunctions for R that offers Bayesian nonparametric estimation models for analysis of dependent, noisy time series data indexed by a collection of domains. This data structure arises from combining periodically published government survey statistics, such as are reported in the Current Population Study (CPS. The CPS publishes monthly, by-state estimates of employment levels, where each state expresses a noisy time series. Published state-level estimates from the CPS are composed from household survey responses in a model-free manner and express high levels of volatility due to insufficient sample sizes. Existing software solutions borrow information over a modeled time-based dependence to extract a de-noised time series for each domain. These solutions, however, ignore the dependence among the domains that may be additionally leveraged to improve estimation efficiency. The growfunctions package offers two fully nonparametric mixture models that simultaneously estimate both a time and domain-indexed dependence structure for a collection of time series: (1 A Gaussian process (GP construction, which is parameterized through the covariance matrix, estimates a latent function for each domain. The covariance parameters of the latent functions are indexed by domain under a Dirichlet process prior that permits estimation of the dependence among functions across the domains: (2 An intrinsic Gaussian Markov random field prior construction provides an alternative to the GP that expresses different computation and estimation properties. In addition to performing denoised estimation of latent functions from published domain estimates, growfunctions allows estimation of collections of functions for observation units (e.g., households, rather than aggregated domains, by accounting for an informative sampling design under which the probabilities for inclusion of observation units are related to the response variable. growfunctions includes plot
Performance of non-parametric algorithms for spatial mapping of tropical forest structure
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Liang Xu
2016-08-01
Full Text Available Abstract Background Mapping tropical forest structure is a critical requirement for accurate estimation of emissions and removals from land use activities. With the availability of a wide range of remote sensing imagery of vegetation characteristics from space, development of finer resolution and more accurate maps has advanced in recent years. However, the mapping accuracy relies heavily on the quality of input layers, the algorithm chosen, and the size and quality of inventory samples for calibration and validation. Results By using airborne lidar data as the “truth” and focusing on the mean canopy height (MCH as a key structural parameter, we test two commonly-used non-parametric techniques of maximum entropy (ME and random forest (RF for developing maps over a study site in Central Gabon. Results of mapping show that both approaches have improved accuracy with more input layers in mapping canopy height at 100 m (1-ha pixels. The bias-corrected spatial models further improve estimates for small and large trees across the tails of height distributions with a trade-off in increasing overall mean squared error that can be readily compensated by increasing the sample size. Conclusions A significant improvement in tropical forest mapping can be achieved by weighting the number of inventory samples against the choice of image layers and the non-parametric algorithms. Without future satellite observations with better sensitivity to forest biomass, the maps based on existing data will remain slightly biased towards the mean of the distribution and under and over estimating the upper and lower tails of the distribution.
Palacios, Julia A; Minin, Vladimir N
2013-03-01
Changes in population size influence genetic diversity of the population and, as a result, leave a signature of these changes in individual genomes in the population. We are interested in the inverse problem of reconstructing past population dynamics from genomic data. We start with a standard framework based on the coalescent, a stochastic process that generates genealogies connecting randomly sampled individuals from the population of interest. These genealogies serve as a glue between the population demographic history and genomic sequences. It turns out that only the times of genealogical lineage coalescences contain information about population size dynamics. Viewing these coalescent times as a point process, estimating population size trajectories is equivalent to estimating a conditional intensity of this point process. Therefore, our inverse problem is similar to estimating an inhomogeneous Poisson process intensity function. We demonstrate how recent advances in Gaussian process-based nonparametric inference for Poisson processes can be extended to Bayesian nonparametric estimation of population size dynamics under the coalescent. We compare our Gaussian process (GP) approach to one of the state-of-the-art Gaussian Markov random field (GMRF) methods for estimating population trajectories. Using simulated data, we demonstrate that our method has better accuracy and precision. Next, we analyze two genealogies reconstructed from real sequences of hepatitis C and human Influenza A viruses. In both cases, we recover more believed aspects of the viral demographic histories than the GMRF approach. We also find that our GP method produces more reasonable uncertainty estimates than the GMRF method. Copyright © 2013, The International Biometric Society.
Nonparametric test of consistency between cosmological models and multiband CMB measurements
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Aghamousa, Amir [Asia Pacific Center for Theoretical Physics, Pohang, Gyeongbuk 790-784 (Korea, Republic of); Shafieloo, Arman, E-mail: amir@apctp.org, E-mail: shafieloo@kasi.re.kr [Korea Astronomy and Space Science Institute, Daejeon 305-348 (Korea, Republic of)
2015-06-01
We present a novel approach to test the consistency of the cosmological models with multiband CMB data using a nonparametric approach. In our analysis we calibrate the REACT (Risk Estimation and Adaptation after Coordinate Transformation) confidence levels associated with distances in function space (confidence distances) based on the Monte Carlo simulations in order to test the consistency of an assumed cosmological model with observation. To show the applicability of our algorithm, we confront Planck 2013 temperature data with concordance model of cosmology considering two different Planck spectra combination. In order to have an accurate quantitative statistical measure to compare between the data and the theoretical expectations, we calibrate REACT confidence distances and perform a bias control using many realizations of the data. Our results in this work using Planck 2013 temperature data put the best fit ΛCDM model at 95% (∼ 2σ) confidence distance from the center of the nonparametric confidence set while repeating the analysis excluding the Planck 217 × 217 GHz spectrum data, the best fit ΛCDM model shifts to 70% (∼ 1σ) confidence distance. The most prominent features in the data deviating from the best fit ΛCDM model seems to be at low multipoles 18 < ℓ < 26 at greater than 2σ, ℓ ∼ 750 at ∼1 to 2σ and ℓ ∼ 1800 at greater than 2σ level. Excluding the 217×217 GHz spectrum the feature at ℓ ∼ 1800 becomes substantially less significance at ∼1 to 2σ confidence level. Results of our analysis based on the new approach we propose in this work are in agreement with other analysis done using alternative methods.
Nonparametric identification of nonlinear dynamic systems using a synchronisation-based method
Kenderi, Gábor; Fidlin, Alexander
2014-12-01
The present study proposes an identification method for highly nonlinear mechanical systems that does not require a priori knowledge of the underlying nonlinearities to reconstruct arbitrary restoring force surfaces between degrees of freedom. This approach is based on the master-slave synchronisation between a dynamic model of the system as the slave and the real system as the master using measurements of the latter. As the model synchronises to the measurements, it becomes an observer of the real system. The optimal observer algorithm in a least-squares sense is given by the Kalman filter. Using the well-known state augmentation technique, the Kalman filter can be turned into a dual state and parameter estimator to identify parameters of a priori characterised nonlinearities. The paper proposes an extension of this technique towards nonparametric identification. A general system model is introduced by describing the restoring forces as bilateral spring-dampers with time-variant coefficients, which are estimated as augmented states. The estimation procedure is followed by an a posteriori statistical analysis to reconstruct noise-free restoring force characteristics using the estimated states and their estimated variances. Observability is provided using only one measured mechanical quantity per degree of freedom, which makes this approach less demanding in the number of necessary measurement signals compared with truly nonparametric solutions, which typically require displacement, velocity and acceleration signals. Additionally, due to the statistical rigour of the procedure, it successfully addresses signals corrupted by significant measurement noise. In the present paper, the method is described in detail, which is followed by numerical examples of one degree of freedom (1DoF) and 2DoF mechanical systems with strong nonlinearities of vibro-impact type to demonstrate the effectiveness of the proposed technique.
Non-parametric PSF estimation from celestial transit solar images using blind deconvolution
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González Adriana
2016-01-01
Full Text Available Context: Characterization of instrumental effects in astronomical imaging is important in order to extract accurate physical information from the observations. The measured image in a real optical instrument is usually represented by the convolution of an ideal image with a Point Spread Function (PSF. Additionally, the image acquisition process is also contaminated by other sources of noise (read-out, photon-counting. The problem of estimating both the PSF and a denoised image is called blind deconvolution and is ill-posed. Aims: We propose a blind deconvolution scheme that relies on image regularization. Contrarily to most methods presented in the literature, our method does not assume a parametric model of the PSF and can thus be applied to any telescope. Methods: Our scheme uses a wavelet analysis prior model on the image and weak assumptions on the PSF. We use observations from a celestial transit, where the occulting body can be assumed to be a black disk. These constraints allow us to retain meaningful solutions for the filter and the image, eliminating trivial, translated, and interchanged solutions. Under an additive Gaussian noise assumption, they also enforce noise canceling and avoid reconstruction artifacts by promoting the whiteness of the residual between the blurred observations and the cleaned data. Results: Our method is applied to synthetic and experimental data. The PSF is estimated for the SECCHI/EUVI instrument using the 2007 Lunar transit, and for SDO/AIA using the 2012 Venus transit. Results show that the proposed non-parametric blind deconvolution method is able to estimate the core of the PSF with a similar quality to parametric methods proposed in the literature. We also show that, if these parametric estimations are incorporated in the acquisition model, the resulting PSF outperforms both the parametric and non-parametric methods.
Carroll, Raymond J.
2011-03-01
In many applications we can expect that, or are interested to know if, a density function or a regression curve satisfies some specific shape constraints. For example, when the explanatory variable, X, represents the value taken by a treatment or dosage, the conditional mean of the response, Y , is often anticipated to be a monotone function of X. Indeed, if this regression mean is not monotone (in the appropriate direction) then the medical or commercial value of the treatment is likely to be significantly curtailed, at least for values of X that lie beyond the point at which monotonicity fails. In the case of a density, common shape constraints include log-concavity and unimodality. If we can correctly guess the shape of a curve, then nonparametric estimators can be improved by taking this information into account. Addressing such problems requires a method for testing the hypothesis that the curve of interest satisfies a shape constraint, and, if the conclusion of the test is positive, a technique for estimating the curve subject to the constraint. Nonparametric methodology for solving these problems already exists, but only in cases where the covariates are observed precisely. However in many problems, data can only be observed with measurement errors, and the methods employed in the error-free case typically do not carry over to this error context. In this paper we develop a novel approach to hypothesis testing and function estimation under shape constraints, which is valid in the context of measurement errors. Our method is based on tilting an estimator of the density or the regression mean until it satisfies the shape constraint, and we take as our test statistic the distance through which it is tilted. Bootstrap methods are used to calibrate the test. The constrained curve estimators that we develop are also based on tilting, and in that context our work has points of contact with methodology in the error-free case.
Xuan, Junyu; Lu, Jie; Zhang, Guangquan; Xu, Richard Yi Da; Luo, Xiangfeng
2018-05-01
Sparse nonnegative matrix factorization (SNMF) aims to factorize a data matrix into two optimized nonnegative sparse factor matrices, which could benefit many tasks, such as document-word co-clustering. However, the traditional SNMF typically assumes the number of latent factors (i.e., dimensionality of the factor matrices) to be fixed. This assumption makes it inflexible in practice. In this paper, we propose a doubly sparse nonparametric NMF framework to mitigate this issue by using dependent Indian buffet processes (dIBP). We apply a correlation function for the generation of two stick weights associated with each column pair of factor matrices while still maintaining their respective marginal distribution specified by IBP. As a consequence, the generation of two factor matrices will be columnwise correlated. Under this framework, two classes of correlation function are proposed: 1) using bivariate Beta distribution and 2) using Copula function. Compared with the single IBP-based NMF, this paper jointly makes two factor matrices nonparametric and sparse, which could be applied to broader scenarios, such as co-clustering. This paper is seen to be much more flexible than Gaussian process-based and hierarchial Beta process-based dIBPs in terms of allowing the two corresponding binary matrix columns to have greater variations in their nonzero entries. Our experiments on synthetic data show the merits of this paper compared with the state-of-the-art models in respect of factorization efficiency, sparsity, and flexibility. Experiments on real-world data sets demonstrate the efficiency of this paper in document-word co-clustering tasks.
A saturated SSR/DArT linkage map of Musa acuminata addressing genome rearrangements among bananas.
Hippolyte, Isabelle; Bakry, Frederic; Seguin, Marc; Gardes, Laetitia; Rivallan, Ronan; Risterucci, Ange-Marie; Jenny, Christophe; Perrier, Xavier; Carreel, Françoise; Argout, Xavier; Piffanelli, Pietro; Khan, Imtiaz A; Miller, Robert N G; Pappas, Georgios J; Mbéguié-A-Mbéguié, Didier; Matsumoto, Takashi; De Bernardinis, Veronique; Huttner, Eric; Kilian, Andrzej; Baurens, Franc-Christophe; D'Hont, Angélique; Cote, François; Courtois, Brigitte; Glaszmann, Jean-Christophe
2010-04-13
The genus Musa is a large species complex which includes cultivars at diploid and triploid levels. These sterile and vegetatively propagated cultivars are based on the A genome from Musa acuminata, exclusively for sweet bananas such as Cavendish, or associated with the B genome (Musa balbisiana) in cooking bananas such as Plantain varieties. In M. acuminata cultivars, structural heterozygosity is thought to be one of the main causes of sterility, which is essential for obtaining seedless fruits but hampers breeding. Only partial genetic maps are presently available due to chromosomal rearrangements within the parents of the mapping populations. This causes large segregation distortions inducing pseudo-linkages and difficulties in ordering markers in the linkage groups. The present study aims at producing a saturated linkage map of M. acuminata, taking into account hypotheses on the structural heterozygosity of the parents. An F1 progeny of 180 individuals was obtained from a cross between two genetically distant accessions of M. acuminata, 'Borneo' and 'Pisang Lilin' (P. Lilin). Based on the gametic recombination of each parent, two parental maps composed of SSR and DArT markers were established. A significant proportion of the markers (21.7%) deviated (p DArTs) covering 1197 cM. This first saturated map is proposed as a "reference Musa map" for further analyses. We also propose two complete parental maps with interpretations of structural rearrangements localized on the linkage groups. The structural heterozygosity in P. Lilin is hypothesized to result from a duplication likely accompanied by an inversion on another chromosome. This paper also illustrates a methodological approach, transferable to other species, to investigate the mapping of structural rearrangements and determine their consequences on marker segregation.
Construction of the first genetic linkage map of Japanese gentian (Gentianaceae
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Nakatsuka Takashi
2012-11-01
Full Text Available Abstract Background Japanese gentians (Gentiana triflora and Gentiana scabra are amongst the most popular floricultural plants in Japan. However, genomic resources for Japanese gentians have not yet been developed, mainly because of the heterozygous genome structure conserved by outcrossing, the long juvenile period, and limited knowledge about the inheritance of important traits. In this study, we developed a genetic linkage map to improve breeding programs of Japanese gentians. Results Enriched simple sequence repeat (SSR libraries from a G. triflora double haploid line yielded almost 20,000 clones using 454 pyrosequencing technology, 6.7% of which could be used to design SSR markers. To increase the number of molecular markers, we identified three putative long terminal repeat (LTR sequences using the recently developed inter-primer binding site (iPBS method. We also developed retrotransposon microsatellite amplified polymorphism (REMAP markers combining retrotransposon and inter-simple sequence repeat (ISSR markers. In addition to SSR and REMAP markers, modified amplified fragment length polymorphism (AFLP and random amplification polymorphic DNA (RAPD markers were developed. Using 93 BC1 progeny from G. scabra backcrossed with a G. triflora double haploid line, 19 linkage groups were constructed with a total of 263 markers (97 SSR, 97 AFLP, 39 RAPD, and 30 REMAP markers. One phenotypic trait (stem color and 10 functional markers related to genes controlling flower color, flowering time and cold tolerance were assigned to the linkage map, confirming its utility. Conclusions This is the first reported genetic linkage map for Japanese gentians and for any species belonging to the family Gentianaceae. As demonstrated by mapping of functional markers and the stem color trait, our results will help to explain the genetic basis of agronomic important traits, and will be useful for marker-assisted selection in gentian breeding programs. Our map
X linked neonatal centronuclear/myotubular myopathy: evidence for linkage to Xq28 DNA marker loci.
Thomas, N S; Williams, H; Cole, G; Roberts, K; Clarke, A; Liechti-Gallati, S; Braga, S; Gerber, A; Meier, C; Moser, H
1990-01-01
We have studied the inheritance of several polymorphic Xq27/28 DNA marker loci in two three generation families with the X linked neonatal lethal form of centronuclear/myotubular myopathy (XL MTM). We found complete linkage of XLMTM to all four informative Xq28 markers analysed, with GCP/RCP (Z = 3.876, theta = 0.00), with DXS15 (Z = 3.737, theta = 0.00), with DXS52 (Z = 2.709, theta = 0.00), and with F8C (Z = 1.020, theta = 0.00). In the absence of any observable recombination, we are unable...
Le Bihan, Nicolas; Margerin, Ludovic
2009-07-01
In this paper, we present a nonparametric method to estimate the heterogeneity of a random medium from the angular distribution of intensity of waves transmitted through a slab of random material. Our approach is based on the modeling of forward multiple scattering using compound Poisson processes on compact Lie groups. The estimation technique is validated through numerical simulations based on radiative transfer theory.
Maydeu-Olivares, Albert
2005-01-01
Chernyshenko, Stark, Chan, Drasgow, and Williams (2001) investigated the fit of Samejima's logistic graded model and Levine's non-parametric MFS model to the scales of two personality questionnaires and found that the graded model did not fit well. We attribute the poor fit of the graded model to small amounts of multidimensionality present in…
Lange, C; Lyon, H; DeMeo, D; Raby, B; Silverman, EK; Weiss, ST
2003-01-01
We introduce a new powerful nonparametric testing strategy for family-based association studies in which multiple quantitative traits are recorded and the phenotype with the strongest genetic component is not known prior to the analysis. In the first stage, using a population-based test based on the
Jiang, GJ
1998-01-01
This paper develops a nonparametric model of interest rate term structure dynamics based an a spot rate process that permits only positive interest rates and a market price of interest rate risk that precludes arbitrage opportunities. Both the spot rate process and the market price of interest rate
Sueiro, Manuel J.; Abad, Francisco J.
2011-01-01
The distance between nonparametric and parametric item characteristic curves has been proposed as an index of goodness of fit in item response theory in the form of a root integrated squared error index. This article proposes to use the posterior distribution of the latent trait as the nonparametric model and compares the performance of an index…
Using Bureaucratic and Cultural Linkages to Improve Instruction: The Principal's Contribution.
Firestone, William A.; Wilson, Bruce L.
1985-01-01
Principals can influence teachers and instructional behavior by working through linkage mechanisms within the organizational structure of the school. Two types of linkages are identified: bureaucratic and cultural. Principals have access to linkages of both kinds; using linkages effectively, they can generate a common purpose in their schools. (MD)
RLT-S: A Web System for Record Linkage.
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Abdullah-Al Mamun
Full Text Available Record linkage integrates records across multiple related data sources identifying duplicates and accounting for possible errors. Real life applications require efficient algorithms to merge these voluminous data sources to find out all records belonging to same individuals. Our recently devised highly efficient record linkage algorithms provide best-known solutions to this challenging problem.We have developed RLT-S, a freely available web tool, which implements our single linkage clustering algorithm for record linkage. This tool requires input data sets and a small set of configuration settings about these files to work efficiently. RLT-S employs exact match clustering, blocking on a specified attribute and single linkage based hierarchical clustering among these blocks.RLT-S is an implementation package of our sequential record linkage algorithm. It outperforms previous best-known implementations by a large margin. The tool is at least two times faster for any dataset than the previous best-known tools.RLT-S tool implements our record linkage algorithm that outperforms previous best-known algorithms in this area. This website also contains necessary information such as instructions, submission history, feedback, publications and some other sections to facilitate the usage of the tool.RLT-S is integrated into http://www.rlatools.com, which is currently serving this tool only. The tool is freely available and can be used without login. All data files used in this paper have been stored in https://github.com/abdullah009/DataRLATools. For copies of the relevant programs please see https://github.com/abdullah009/RLATools.
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Gonzalez-Neira Anna
2007-08-01
Full Text Available Abstract Background The recent development of new high-throughput technologies for SNP genotyping has opened the possibility of taking a genome-wide linkage approach to the search for new candidate genes involved in heredity diseases. The two major breast cancer susceptibility genes BRCA1 and BRCA2 are involved in 30% of hereditary breast cancer cases, but the discovery of additional breast cancer predisposition genes for the non-BRCA1/2 breast cancer families has so far been unsuccessful. Results In order to evaluate the power improvement provided by using SNP markers in a real situation, we have performed a whole genome screen of 19 non-BRCA1/2 breast cancer families using 4720 genomewide SNPs with Illumina technology (Illumina's Linkage III Panel, with an average distance of 615 Kb/SNP. We identified six regions on chromosomes 2, 3, 4, 7, 11 and 14 as candidates to contain genes involved in breast cancer susceptibility, and additional fine mapping genotyping using microsatellite markers around linkage peaks confirmed five of them, excluding the region on chromosome 3. These results were consistent in analyses that excluded SNPs in high linkage disequilibrium. The results were compared with those obtained previously using a 10 cM microsatellite scan (STR-GWS and we found lower or not significant linkage signals with STR-GWS data compared to SNP data in all cases. Conclusion Our results show the power increase that SNPs can supply in linkage studies.
Nature–society linkages in the Aral Sea region
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Kristopher D. White
2013-01-01
Full Text Available Central Asia's Aral Sea crisis represents a disaster of monumental proportions, a tragedy for both the region's ecology and its human inhabitants. While the human and natural environments had operated in a sustainable co-joined system for millennia, Tsarist Russian expansion into Central Asia, followed by Soviet expansion of both the cotton industry and unsustainable irrigation practices to anchor it spelled doom for the Aral Sea. Today, many of the political and economic stimuli for such misguided practices continue, as do the continued retreat of the Sea and the proliferation of poor human health. The Aral Sea crisis has received ample scholarly attention, though somewhat surprising is a relative dearth of research explicitly investigating the nature, variety, and directionality of nature–society linkages today within the region. The purpose of this paper is to elucidate the contemporary nature–society linkages operating within the Aral Sea region of Central Asia. Historical nexuses will provide necessary background, and the linkages operating currently within the spheres of regional economy, human health, and political considerations will be detailed. Couching the current crisis within the framework of coupled human–environment system contexts reveals a region in which these linkages are largely inextricable. This paper concludes with a call for a reconsideration of the nature-society linkages and a greater emphasis placed on the local region's ecological and social sustainability.
Autosomal dominant distal myopathy: Linkage to chromosome 14
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Laing, N.G.; Laing, B.A.; Wilton, S.D.; Dorosz, S.; Mastaglia, F.L.; Kakulas, B.A. [Australian Neuromuscular Research Institute, Perth (Australia); Robbins, P.; Meredith, C.; Honeyman, K.; Kozman, H.
1995-02-01
We have studied a family segregating a form of autosomal dominant distal myopathy (MIM 160500) and containing nine living affected individuals. The myopathy in this family is closest in clinical phenotype to that first described by Gowers in 1902. A search for linkage was conducted using microsatellite, VNTR, and RFLP markers. In total, 92 markers on all 22 autosomes were run. Positive linkage was obtained with 14 of 15 markers tested on chromosome 14, with little indication of linkage elsewhere in the genome. Maximum two-point LOD scores of 2.60 at recombination fraction .00 were obtained for the markers MYH7 and D14S64 - the family structure precludes a two-point LOD score {ge} 3. Recombinations with D14S72 and D14S49 indicate that this distal myopathy locus, MPD1, should lie between these markers. A multipoint analysis assuming 100% penetrance and using the markers D14S72, D14S50, MYH7, D14S64, D14S54, and D14S49 gave a LOD score of exactly 3 at MYH7. Analysis at a penetrance of 80% gave a LOD score of 2.8 at this marker. This probable localization of a gene for distal myopathy, MPD1, on chromosome 14 should allow other investigators studying distal myopathy families to test this region for linkage in other types of the disease, to confirm linkage or to demonstrate the likely genetic heterogeneity. 24 refs., 3 figs., 1 tab.
Preliminary genetic linkage map of the abalone Haliotis diversicolor Reeve
Shi, Yaohua; Guo, Ximing; Gu, Zhifeng; Wang, Aimin; Wang, Yan
2010-05-01
Haliotis diversicolor Reeve is one of the most important mollusks cultured in South China. Preliminary genetic linkage maps were constructed with amplified fragment length polymorphism (AFLP) markers. A total of 2 596 AFLP markers were obtained from 28 primer combinations in two parents and 78 offsprings. Among them, 412 markers (15.9%) were polymorphic and segregated in the mapping family. Chi-square tests showed that 151 (84.4%) markers segregated according to the expected 1:1 Mendelian ratio ( P<0.05) in the female parent, and 200 (85.8%) in the male parent. For the female map, 179 markers were used for linkage analysis and 90 markers were assigned to 17 linkage groups with an average interval length of 25.7 cm. For the male map, 233 markers were used and 94 were mapped into 18 linkage groups, with an average interval of 25.0 cm. The estimated genome length was 2 773.0 cm for the female and 2 817.1 cm for the male map. The observed length of the linkage map was 1 875.2 cm and 1 896.5 cm for the female and male maps, respectively. When doublets were considered, the map length increased to 2 152.8 cm for the female and 2 032.7 cm for the male map, corresponding to genome coverage of 77.6% and 72.2%, respectively.
Van Steenbergen, N.; Willems, P.
2012-04-01
Reliable flood forecasts are the most important non-structural measures to reduce the impact of floods. However flood forecasting systems are subject to uncertainty originating from the input data, model structure and model parameters of the different hydraulic and hydrological submodels. To quantify this uncertainty a non-parametric data-based approach has been developed. This approach analyses the historical forecast residuals (differences between the predictions and the observations at river gauging stations) without using a predefined statistical error distribution. Because the residuals are correlated with the value of the forecasted water level and the lead time, the residuals are split up into discrete classes of simulated water levels and lead times. For each class, percentile values are calculated of the model residuals and stored in a 'three dimensional error' matrix. By 3D interpolation in this error matrix, the uncertainty in new forecasted water levels can be quantified. In addition to the quantification of the uncertainty, the communication of this uncertainty is equally important. The communication has to be done in a consistent way, reducing the chance of misinterpretation. Also, the communication needs to be adapted to the audience; the majority of the larger public is not interested in in-depth information on the uncertainty on the predicted water levels, but only is interested in information on the likelihood of exceedance of certain alarm levels. Water managers need more information, e.g. time dependent uncertainty information, because they rely on this information to undertake the appropriate flood mitigation action. There are various ways in presenting uncertainty information (numerical, linguistic, graphical, time (in)dependent, etc.) each with their advantages and disadvantages for a specific audience. A useful method to communicate uncertainty of flood forecasts is by probabilistic flood mapping. These maps give a representation of the
Robust LOD scores for variance component-based linkage analysis.
Blangero, J; Williams, J T; Almasy, L
2000-01-01
The variance component method is now widely used for linkage analysis of quantitative traits. Although this approach offers many advantages, the importance of the underlying assumption of multivariate normality of the trait distribution within pedigrees has not been studied extensively. Simulation studies have shown that traits with leptokurtic distributions yield linkage test statistics that exhibit excessive Type I error when analyzed naively. We derive analytical formulae relating the deviation from the expected asymptotic distribution of the lod score to the kurtosis and total heritability of the quantitative trait. A simple correction constant yields a robust lod score for any deviation from normality and for any pedigree structure, and effectively eliminates the problem of inflated Type I error due to misspecification of the underlying probability model in variance component-based linkage analysis.
Kling, Daniel; Tillmar, Andreas; Egeland, Thore; Mostad, Petter
2015-09-01
Several applications necessitate an unbiased determination of relatedness, be it in linkage or association studies or in a forensic setting. An appropriate model to compute the joint probability of some genetic data for a set of persons given some hypothesis about the pedigree structure is then required. The increasing number of markers available through high-density SNP microarray typing and NGS technologies intensifies the demand, where using a large number of markers may lead to biased results due to strong dependencies between closely located loci, both within pedigrees (linkage) and in the population (allelic association or linkage disequilibrium (LD)). We present a new general model, based on a Markov chain for inheritance patterns and another Markov chain for founder allele patterns, the latter allowing us to account for LD. We also demonstrate a specific implementation for X chromosomal markers that allows for computation of likelihoods based on hypotheses of alleged relationships and genetic marker data. The algorithm can simultaneously account for linkage, LD, and mutations. We demonstrate its feasibility using simulated examples. The algorithm is implemented in the software FamLinkX, providing a user-friendly GUI for Windows systems (FamLinkX, as well as further usage instructions, is freely available at www.famlink.se ). Our software provides the necessary means to solve cases where no previous implementation exists. In addition, the software has the possibility to perform simulations in order to further study the impact of linkage and LD on computed likelihoods for an arbitrary set of markers.
Search for linkage to schizophrenia on the X and Y chromosomes
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Devoto, M.; Ott, J. [Columbia Univ., New York, NY (United States); Vita, A. [Univ. of Milan (Italy)] [and others
1994-06-15
Markers for X chromosome loci were used in linkage studies of a large group of small families (n = 126) with at least two schizophrenic members in one sibship. Based on the hypothesis that a gene for schizophrenia could be X-Y linked, with homologous loci on both X and Y, our analyses included all families regardless of the pattern of familial inheritance. Lod scores were computed with both standard X-linked and a novel X-Y model, and sib-pair analyses were performed for all markers examining the sharing of maternal alleles. Small positive lod scores were obtained for loci pericentromeric, from Xp11.4 to Xq12. Lod scores were also computed separately in families selected for evidence of maternal inheritance and absence of male to male transmission of psychosis. The lod scores for linkage to the locus DXS7 reached a maximum of 1.83 at 0.08% recombination, assuming dominant inheritance on the X chromosome in these families (n = 34). Further investigation of the X-Y homologous gene hypothesis focussing on this region is warranted. 39 refs. 1 fig., 6 tabs.
Kliem, Sören; Weusthoff, Sarah; Hahlweg, Kurt; Baucom, Katherine J W; Baucom, Brian R
2015-12-01
Identifying risk factors for divorce or separation is an important step in the prevention of negative individual outcomes and societal costs associated with relationship dissolution. Programs that aim to prevent relationship distress and dissolution typically focus on changing processes that occur during couple conflict, although the predictive ability of conflict-specific variables has not been examined in the context of other factors related to relationship dissolution. The authors examine whether emotional responding and communication during couple conflict predict relationship dissolution after controlling for overall relationship quality and individual well-being. Using nonparametric conditional survival trees, the study at hand simultaneously examined the predictive abilities of physiological (systolic and diastolic blood pressure, heart rate, cortisol) and behavioral (fundamental frequency; f0) indices of emotional responding, as well as observationally coded positive and negative communication behavior, on long-term relationship stability after controlling for relationship satisfaction and symptoms of depression. One hundred thirty-six spouses were assessed after participating in a randomized clinical trial of a relationship distress prevention program as well as 11 years thereafter; 32.5% of the couples' relationships had dissolved by follow up. For men, the only significant predictor of relationship dissolution was cortisol change score (p = .012). For women, only f0 range was a significant predictor of relationship dissolution (p = .034). These findings highlight the importance of emotional responding during couple conflict for long-term relationship stability. (c) 2015 APA, all rights reserved).
Yap, John Stephen; Fan, Jianqing; Wu, Rongling
2009-12-01
Estimation of the covariance structure of longitudinal processes is a fundamental prerequisite for the practical deployment of functional mapping designed to study the genetic regulation and network of quantitative variation in dynamic complex traits. We present a nonparametric approach for estimating the covariance structure of a quantitative trait measured repeatedly at a series of time points. Specifically, we adopt Huang et al.'s (2006, Biometrika 93, 85-98) approach of invoking the modified Cholesky decomposition and converting the problem into modeling a sequence of regressions of responses. A regularized covariance estimator is obtained using a normal penalized likelihood with an L(2) penalty. This approach, embedded within a mixture likelihood framework, leads to enhanced accuracy, precision, and flexibility of functional mapping while preserving its biological relevance. Simulation studies are performed to reveal the statistical properties and advantages of the proposed method. A real example from a mouse genome project is analyzed to illustrate the utilization of the methodology. The new method will provide a useful tool for genome-wide scanning for the existence and distribution of quantitative trait loci underlying a dynamic trait important to agriculture, biology, and health sciences.
Rank-based permutation approaches for non-parametric factorial designs.
Umlauft, Maria; Konietschke, Frank; Pauly, Markus
2017-11-01
Inference methods for null hypotheses formulated in terms of distribution functions in general non-parametric factorial designs are studied. The methods can be applied to continuous, ordinal or even ordered categorical data in a unified way, and are based only on ranks. In this set-up Wald-type statistics and ANOVA-type statistics are the current state of the art. The first method is asymptotically exact but a rather liberal statistical testing procedure for small to moderate sample size, while the latter is only an approximation which does not possess the correct asymptotic α level under the null. To bridge these gaps, a novel permutation approach is proposed which can be seen as a flexible generalization of the Kruskal-Wallis test to all kinds of factorial designs with independent observations. It is proven that the permutation principle is asymptotically correct while keeping its finite exactness property when data are exchangeable. The results of extensive simulation studies foster these theoretical findings. A real data set exemplifies its applicability. © 2017 The British Psychological Society.
Nonparametric estimates of drift and diffusion profiles via Fokker-Planck algebra.
Lund, Steven P; Hubbard, Joseph B; Halter, Michael
2014-11-06
Diffusion processes superimposed upon deterministic motion play a key role in understanding and controlling the transport of matter, energy, momentum, and even information in physics, chemistry, material science, biology, and communications technology. Given functions defining these random and deterministic components, the Fokker-Planck (FP) equation is often used to model these diffusive systems. Many methods exist for estimating the drift and diffusion profiles from one or more identifiable diffusive trajectories; however, when many identical entities diffuse simultaneously, it may not be possible to identify individual trajectories. Here we present a method capable of simultaneously providing nonparametric estimates for both drift and diffusion profiles from evolving density profiles, requiring only the validity of Langevin/FP dynamics. This algebraic FP manipulation provides a flexible and robust framework for estimating stationary drift and diffusion coefficient profiles, is not based on fluctuation theory or solved diffusion equations, and may facilitate predictions for many experimental systems. We illustrate this approach on experimental data obtained from a model lipid bilayer system exhibiting free diffusion and electric field induced drift. The wide range over which this approach provides accurate estimates for drift and diffusion profiles is demonstrated through simulation.