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 This note is extracted from a long unpublished report with 58 figures available at , which extensively describes the evidence we have accumulated on these seven time series, in particular by presenting all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.
Parametric and nonparametric Granger causality testing: Linkages between international stock markets
de Gooijer, J.G.; Sivarajasingham, S.
2008-01-01
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 t
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....
HLA region excluded by linkage analyses of early onset periodontitis
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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.
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.
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 analyses of cannabis dependence, craving, and withdrawal in the San Francisco family study.
Ehlers, Cindy L; Gizer, Ian R; Vieten, Cassandra; Wilhelmsen, Kirk C
2010-04-05
Cannabis is the most widely used illicit drug in the United States. There is ample evidence that cannabis use has a heritable component, yet the genes underlying cannabis use disorders are yet to be completely identified. This study's aims were to map susceptibility loci for cannabis use and dependence and two narrower cannabis-related phenotypes of "craving" and "withdrawal" using a family study design. Participants were 2,524 adults participating in the University of California San Francisco (UCSF) Family Alcoholism Study. DSM-IV diagnoses of cannabis dependence, as well as indices of cannabis craving and withdrawal, were obtained using a modified version of the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA). Genotypes were determined for a panel of 791 microsatellite polymorphisms. Multipoint variance component LOD scores were obtained using SOLAR. Genome-wide significance for linkage (LOD > 3.0) was not found for the DSM-IV cannabis dependence diagnosis; however, linkage analyses of cannabis "craving" and the cannabis withdrawal symptom of "nervous, tense, restless, or irritable" revealed five sites with LOD scores over 3.0 on chromosomes 1, 3, 6, 7, and 9. These results identify new regions of the genome associated with cannabis use phenotypes as well as corroborate the importance of several chromosome regions highlighted in previous linkage analyses for other substance dependence phenotypes.
Alhaddad, Hasan; Gandolfi, Barbara; Grahn, Robert A; Rah, Hyung-Chul; Peterson, Carlyn B; Maggs, David J; Good, Kathryn L; Pedersen, Niels C; Lyons, Leslie A
2014-08-01
Hereditary eye diseases of animals serve as excellent models of human ocular disorders and assist in the development of gene and drug therapies for inherited forms of blindness. Several primary hereditary eye conditions affecting various ocular tissues and having different rates of progression have been documented in domestic cats. Gene therapy for canine retinopathies has been successful, thus the cat could be a gene therapy candidate for other forms of retinal degenerations. The current study investigates a hereditary, autosomal recessive, retinal degeneration specific to Persian cats. A multi-generational pedigree segregating for this progressive retinal atrophy was genotyped using a 63 K SNP array and analyzed via genome-wide linkage and association methods. A multi-point parametric linkage analysis localized the blindness phenotype to a ~1.75 Mb region with significant LOD scores (Z ≈ 14, θ = 0.00) on cat chromosome E1. Genome-wide TDT, sib-TDT, and case-control analyses also consistently supported significant association within the same region on chromosome E1, which is homologous to human chromosome 17. Using haplotype analysis, a ~1.3 Mb region was identified as highly associated for progressive retinal atrophy in Persian cats. Several candidate genes within the region are reasonable candidates as a potential causative gene and should be considered for molecular analyses.
Linkage analyses of chromosome 6 loci, including HLA, in familial aggregations of Crohn disease
Energy Technology Data Exchange (ETDEWEB)
Hugot, J.P.; Laurent-Puig, P.; Gower-Rousseau, C.; Caillat-Zueman, S.; Beaugerie, L.; Dupas, J.L.; Van Gossum, A.; Bonaiti-Pellie, C.; Cortot, A.
1994-08-15
Segregation analyses of familial aggregations of Crohn disease have provided consistent results pointing to the involvement of a predisposing gene with a recessive mode of inheritance. Although extensively investigated, the role played by human leucocyte antigen (HLA) genes in this inflammatory bowel disease remains elusive and the major histocompatibility complex is a candidate region for the mapping of the Crohn disease susceptibility gene. A total of 25 families with multiple cases of Crohn disease was genotyped for HLA DRB1 and for 16 highly polymorphic loci evenly distributed on chromosome 6. The data were subjected to linkage analysis using the lod score method. Neither individual nor combined lod scores for any family and for any locus tested reached values suggesting linkage or genetic heterogeneity. The Crohn disease predisposing locus was excluded from the whole chromosome 6 with lod scores less than -2. It was excluded from the major histocompatibility complex and from 91% of the chromosome 6 genetic map with lod scores less than -4. The major recessive gene involved in genetic predisposition to Crohn disease does not reside on the major histocompatibility complex nor on any locus mapping to chromosome 6. 37 refs., 2 figs., 2 tabs.
Raman, Harsh; Raman, Rosy; Nelson, Matthew N; Aslam, M N; Rajasekaran, Ravikesavan; Wratten, Neil; Cowling, Wallace A; Kilian, A; Sharpe, Andrew G; Schondelmaier, Joerg
2012-01-01
We developed Diversity Array Technology (DArT) markers for application in genetic studies of Brassica napus and other Brassica species with A or C genomes. Genomic representation from 107 diverse genotypes of B. napus L. var. oleifera (rapeseed, AACC genomes) and B. rapa (AA genome) was used to develop a DArT array comprising 11 520 clones generated using PstI/BanII and PstI/BstN1 complexity reduction methods. In total, 1547 polymorphic DArT markers of high technical quality were identified and used to assess molecular diversity among 89 accessions of B. napus, B. rapa, B. juncea, and B. carinata collected from different parts of the world. Hierarchical cluster and principal component analyses based on genetic distance matrices identified distinct populations clustering mainly according to their origin/pedigrees. DArT markers were also mapped in a new doubled haploid population comprising 131 lines from a cross between spring rapeseed lines 'Lynx-037DH' and 'Monty-028DH'. Linkage groups were assigned on the basis of previously mapped simple sequence repeat (SSRs), intron polymorphism (IP), and gene-based markers. The map consisted of 437 DArT, 135 SSR, 6 IP, and 6 gene-based markers and spanned 2288 cM. Our results demonstrate that DArT markers are suitable for genetic diversity analysis and linkage map construction in rapeseed.
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Phillips Evelyn
2010-03-01
Full Text Available Abstract Background Evidence for a high degree of heritability of EEG alpha phenotypes has been demonstrated in twin and family studies in a number of populations. However, information on linkage of this phenotype to specific chromosome locations is still limited. This study's aims were to map loci linked to EEG alpha phenotypes and to determine if there was overlap with loci previously mapped for alcohol dependence in an American Indian community at high risk for substance dependence. Methods Each participant gave a blood sample and completed a structured diagnostic interview using the Semi Structured Assessment for the Genetics of Alcoholism. Bipolar EEGs were collected and spectral power determined in the alpha (7.5-12.0 Hz frequency band for two composite scalp locations previously identified by principal components analyses (bilateral fronto-central and bilateral centro-parietal-occipital. Genotypes were determined for a panel of 791 micro-satellite polymorphisms in 410 members of multiplex families using SOLAR. Results Sixty percent of this study population had a lifetime diagnosis of alcohol dependence. Analyses of multipoint variance component LOD scores, for the EEG alpha power phenotype, revealed two loci that had a LOD score of 3.0 or above for the fronto-central scalp region on chromosomes 1 and 6. Additionally, 4 locations were identified with LOD scores above 2.0 on chromosomes 4, 11, 14, 16 for the fronto-central location and one on chromosome 2 for the centro-parietal-occipital location. Conclusion These results corroborate the importance of regions on chromosome 4 and 6 highlighted in prior segregation studies in this and other populations for alcohol dependence-related phenotypes, as well as other areas that overlap with other substance dependence phenotypes identified in previous linkage studies in other populations. These studies additionally support the construct that EEG alpha recorded from fronto-central scalp areas may
Topdar, N; Kundu, A; Sinha, M K; Sarkar, D; Das, M; Banerjee, S; Kar, C S; Satya, P; Balyan, H S; Mahapatra, B S; Gupta, P K
2013-01-01
We report the first complete microsatellite genetic map of jute (Corchorus olitorius L.; 2n = 2x = 14) using an F6 recombinant inbred population. Of the 403 microsatellite markers screened, 82 were mapped on the seven linkage groups (LGs) that covered a total genetic distance of 799.9 cM, with an average marker interval of 10.7 cM. LG5 had the longest and LG7 the shortest genetic lengths, whereas LG1 had the maximum and LG7 the minimum number of markers. Segregation distortion of microsatellite loci was high (61%), with the majority of them (76%) skewed towards the female parent. Genomewide non-parametric single-marker analysis in combination with multiple quantitative trait loci (QTL)-models (MQM) mapping detected 26 definitive QTLs for bast fibre quality, yield and yield-related traits. These were unevenly distributed on six LGs, as colocalized clusters, at genomic sectors marked by 15 microsatellite loci. LG1 was the QTL-richest map sector, with the densest colocalized clusters of QTLs governing fibre yield, yield-related traits and tensile strength. Expectedly, favorable QTLs were derived from the desirable parents, except for nearly all of those of fibre fineness, which might be due to the creation of new gene combinations. Our results will be a good starting point for further genome analyses in jute.
Linkage studies of bipolar disorder with chromosome 18 markers.
Bowen, T; Kirov, G; Gill, M; Spurlock, G; Vallada, H P; Murray, R M; McGuffin, P; Collier, D A; Owen, M J; Craddock, N
1999-10-15
Evidence consistent with the existence of genetic linkage between bipolar disorder and three regions on chromosome 18, the pericentromeric region, 18q21, and 18q22-q23 have been reported. Some analyses indicated greater evidence for linkage in pedigrees in which paternal transmission of disease occurs. We have undertaken linkage analyses using 12 highly polymorphic markers spanning these three regions of interest in a sample of 48 U.K. bipolar pedigrees. The sample comprises predominantly nuclear families and includes 118 subjects with Diagnostic and Statistical Manual of Mental Disorders (DSM IV) bipolar I disorder and 147 subjects with broadly defined phenotype. Our data do not provide support for linkage using either parametric or nonparametric analyses. Evidence for linkage was not significantly increased by analyses that allowed for heterogeneity nor by analysing the subset of pedigrees consistent with paternal transmission.
Nonparametric statistical methods using R
Kloke, John
2014-01-01
A Practical Guide to Implementing Nonparametric and Rank-Based ProceduresNonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R, including the packages Rfit and npsm.The book first gives an overview of the R language and basic statistical c
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.
Buhler, J; Owerbach, D; Schäffer, A A; Kimmel, M; Gabbay, K H
1997-01-01
We have developed software and statistical tools for linkage analysis of polygenic diseases. We use type I diabetes mellitus (insulin-dependent diabetes mellitus, IDDM) as our model system. Two susceptibility loci (IDDM1 on 6p21 and IDDM2 on 11p15) are well established, and recent genome searches suggest the existence of other susceptibility loci. We have implemented CASPAR, a software tool that makes it possible to test for linkage quickly and efficiently using multiple polymorphic DNA markers simultaneously in nuclear families consisting of two unaffected parents and a pair of affected siblings (ASP). We use a simulation-based method to determine whether lod scores from a collection of ASP tests are significant. We test our new software and statistical tools to assess linkage of IDDM5 and IDDM7 conditioned on analyses with 1 or 2 other unlinked type I diabetes susceptibility loci. The results from the CASPAR analysis suggest that conditioning of IDDM5 on IDDM1 and IDDM4, and of IDDM7 on IDDM1 and IDDM2 provides significant benefits for the genetic analysis of polygenic loci.
Yang, Jackie (Jiekun); Li, Ming D.
2016-01-01
Experimental approaches to genetic studies of complex traits evolve with technological advances. How do discoveries using different approaches advance our knowledge of the genetic architecture underlying complex diseases/traits? Do most of the findings of newer techniques, such as genome-wide association study (GWAS), provide more information than older ones, e.g., genome-wide linkage study? In this review, we address these issues by developing a nicotine dependence (ND) genetic susceptibility map based on the results obtained by the approaches commonly used in recent years, namely, genome-wide linkage, candidate gene association, GWAS, and targeted sequencing. Converging and diverging results from these empirical approaches have elucidated a preliminary genetic architecture of this intractable psychiatric disorder and yielded new hypotheses on ND aetiology. The insights we obtained by putting together results from diverse approaches can be applied to other complex diseases/traits. In sum, developing a genetic susceptibility map and keeping it updated are effective ways to keep track of what we know about a disease/trait and what the next steps might be with new approaches. PMID:27166759
Institute of Scientific and Technical Information of China (English)
Yong Yong SHI; Lin HE
2005-01-01
In multiloci-based genetic association studies of complex diseases, a powerful and high efficient tool for analyses of linkage disequilibrium (LD) between markers, haplotype distributions and many chi-square/p values with a large number of samples has been sought for long. In order to achieve the goal of obtaining meaningful results directly from raw data,we developed a robust and user-friendly software platform with a series of tools for analysis in association study with high efficiency. The platform has been well evaluated by several sets of real data.
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
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
BACKGROUND: Linkage studies have implicated the 2q33, 9p21, 11q13 and 20q13 regions in the regulation of allergic disease. The aim of this study was to test genetic variants in candidate genes from these regions for association with specific asthma traits. METHODS: Ninety-five single nucleotide...... 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...... after accounting for the number of tests performed. CONCLUSIONS: The individual variants genotyped in the 2q33, 9p21 and 20q13 regions do not explain a large fraction of the variation in the quantitative traits tested or have a major impact on asthma or atopy risk. Our results are consistent with a weak...
Directory of Open Access Journals (Sweden)
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.
Quero-García, José; Guzmán, Alejandra; Mansur, Levi; Gratacós, Eduardo; Silva, Herman; Rosyara, Umesh R.; Iezzoni, Amy; Meisel, Lee A.; Dirlewanger, Elisabeth
2013-01-01
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 F1 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. PMID:23382953
Directory of Open Access Journals (Sweden)
Hong Liu
Full Text Available BACKGROUND: 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. METHODOLOGY: 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. RESULTS: 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. CONCLUSION: Our study has advanced our understanding of DUH pathogenesis and revealed the shared pathological mechanism between pigmentary DUH and ocular coloboma.
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...
Quantal Response: Nonparametric Modeling
2017-01-01
spline N−spline Fig. 3 Logistic regression 7 Approved for public release; distribution is unlimited. 5. Nonparametric QR Models Nonparametric linear ...stimulus and probability of response. The Generalized Linear Model approach does not make use of the limit distribution but allows arbitrary functional...7. Conclusions and Recommendations 18 8. References 19 Appendix A. The Linear Model 21 Appendix B. The Generalized Linear Model 33 Appendix C. B
Li, Changwei; Bazzano, Lydia A.L.; Rao, Dabeeru C.; Hixson, James E.; He, Jiang; Gu, Dongfeng; Gu, Charles C.; Shimmin, Lawrence C.; Jaquish, Cashell E.; Schwander, Karen; Liu, De-Pei; Huang, Jianfeng; Lu, Fanghong; Cao, Jie; Chong, Shen; Lu, Xiangfeng; Kelly, Tanika N.
2016-01-01
We conducted a genome-wide linkage scan and positional association study to identify genes and variants influencing blood lipid levels among participants of the Genetic Epidemiology Network of Salt-Sensitivity (GenSalt) study. The GenSalt study was conducted among 1906 participants from 633 Han Chinese families. Lipids were measured from overnight fasting blood samples using standard methods. Multipoint quantitative trait genome-wide linkage scans were performed on the high-density lipoprotein, low-density lipoprotein, and log-transformed triglyceride phenotypes. Using dense panels of single nucleotide polymorphisms (SNPs), single-marker and gene-based association analyses were conducted to follow-up on promising linkage signals. Additive associations between each SNP and lipid phenotypes were tested using mixed linear regression models. Gene-based analyses were performed by combining P-values from single-marker analyses within each gene using the truncated product method (TPM). Significant associations were assessed for replication among 777 Asian participants of the Multi-ethnic Study of Atherosclerosis (MESA). Bonferroni correction was used to adjust for multiple testing. In the GenSalt study, suggestive linkage signals were identified at 2p11.2–2q12.1 [maximum multipoint LOD score (MML) = 2.18 at 2q11.2] and 11q24.3–11q25 (MML = 2.29 at 11q25) for the log-transformed triglyceride phenotype. Follow-up analyses of these two regions revealed gene-based associations of charged multivesicular body protein 3 (CHMP3), ring finger protein 103 (RNF103), AF4/FMR2 family, member 3 (AFF3), and neurotrimin (NTM ) with triglycerides (P = 4 × 10−4, 1.00 × 10−5, 2.00 × 10−5, and 1.00 × 10−7, respectively). Both the AFF3 and NTM triglyceride associations were replicated among MESA study participants (P = 1.00 × 10−7 and 8.00 × 10−5, respectively). Furthermore, NTM explained the linkage signal on chromosome 11. In conclusion, we identified novel genes
Nonparametric statistical methods
Hollander, Myles; Chicken, Eric
2013-01-01
Praise for the Second Edition"This book should be an essential part of the personal library of every practicing statistician."-Technometrics Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given sit
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.
Directory of Open Access Journals (Sweden)
Day Ian NM
2007-11-01
Full Text Available Abstract Background The frequency of a haplotype comprising one allele at each of two loci can be expressed as a cubic equation (the 'Hill equation', the solution of which gives that frequency. Most haplotype and linkage disequilibrium analysis programs use iteration-based algorithms which substitute an estimate of haplotype frequency into the equation, producing a new estimate which is repeatedly fed back into the equation until the values converge to a maximum likelihood estimate (expectation-maximisation. Results We present a program, "CubeX", which calculates the biologically possible exact solution(s and provides estimated haplotype frequencies, D', r2 and χ2 values for each. CubeX provides a "complete" analysis of haplotype frequencies and linkage disequilibrium for a pair of biallelic markers under situations where sampling variation and genotyping errors distort sample Hardy-Weinberg equilibrium, potentially causing more than one biologically possible solution. We also present an analysis of simulations and real data using the algebraically exact solution, which indicates that under perfect sample Hardy-Weinberg equilibrium there is only one biologically possible solution, but that under other conditions there may be more. Conclusion Our analyses demonstrate that lower allele frequencies, lower sample numbers, population stratification and a possible |D'| value of 1 are particularly susceptible to distortion of sample Hardy-Weinberg equilibrium, which has significant implications for calculation of linkage disequilibrium in small sample sizes (eg HapMap and rarer alleles (eg paucimorphisms, q
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...
A Censored Nonparametric Software Reliability Model
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
This paper analyses the effct of censoring on the estimation of failure rate, and presents a framework of a censored nonparametric software reliability model. The model is based on nonparametric testing of failure rate monotonically decreasing and weighted kernel failure rate estimation under the constraint of failure rate monotonically decreasing. Not only does the model have the advantages of little assumptions and weak constraints, but also the residual defects number of the software system can be estimated. The numerical experiment and real data analysis show that the model performs well with censored data.
Nonparametric Predictive Regression
Ioannis Kasparis; Elena Andreou; Phillips, Peter C.B.
2012-01-01
A unifying framework for inference is developed in predictive regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric F-tests are proposed. The test statistics are related to those of Kasparis and Phillips (2012) and are obtained by kernel regression. The limit distribution of these predictive tests holds for a wide range of predictors including stationary as well as non-stationary fractional and near unit...
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.
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.
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...... used. Premature infants and mothers of premature infants were defined as affected cases in independent analyses.Results:Analyses with the infant as the case identified two genes with evidence of linkage: CRHR1 (P = 0.0012) and CYP2E1 (P = 0.0011). Analyses with the mother as the case identified four...... through the infant and/or the mother in the etiology of PTB....
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.
Non-parametric approach to the study of phenotypic stability.
Ferreira, D F; Fernandes, S B; Bruzi, A T; Ramalho, M A P
2016-02-19
The aim of this study was to undertake the theoretical derivations of non-parametric methods, which use linear regressions based on rank order, for stability analyses. These methods were extension different parametric methods used for stability analyses and the result was compared with a standard non-parametric method. Intensive computational methods (e.g., bootstrap and permutation) were applied, and data from the plant-breeding program of the Biology Department of UFLA (Minas Gerais, Brazil) were used to illustrate and compare the tests. The non-parametric stability methods were effective for the evaluation of phenotypic stability. In the presence of variance heterogeneity, the non-parametric methods exhibited greater power of discrimination when determining the phenotypic stability of genotypes.
CURRENT STATUS OF NONPARAMETRIC STATISTICS
Directory of Open Access Journals (Sweden)
Orlov A. I.
2015-02-01
Full Text Available Nonparametric statistics is one of the five points of growth of applied mathematical statistics. Despite the large number of publications on specific issues of nonparametric statistics, the internal structure of this research direction has remained undeveloped. The purpose of this article is to consider its division into regions based on the existing practice of scientific activity determination of nonparametric statistics and classify investigations on nonparametric statistical methods. Nonparametric statistics allows to make statistical inference, in particular, to estimate the characteristics of the distribution and testing statistical hypotheses without, as a rule, weakly proven assumptions about the distribution function of samples included in a particular parametric family. For example, the widespread belief that the statistical data are often have the normal distribution. Meanwhile, analysis of results of observations, in particular, measurement errors, always leads to the same conclusion - in most cases the actual distribution significantly different from normal. Uncritical use of the hypothesis of normality often leads to significant errors, in areas such as rejection of outlying observation results (emissions, the statistical quality control, and in other cases. Therefore, it is advisable to use nonparametric methods, in which the distribution functions of the results of observations are imposed only weak requirements. It is usually assumed only their continuity. On the basis of generalization of numerous studies it can be stated that to date, using nonparametric methods can solve almost the same number of tasks that previously used parametric methods. Certain statements in the literature are incorrect that nonparametric methods have less power, or require larger sample sizes than parametric methods. Note that in the nonparametric statistics, as in mathematical statistics in general, there remain a number of unresolved problems
Stambolian, Dwight; Ibay, Grace; Reider, Lauren; Dana, Debra; Moy, Chris; Schlifka, Melissa; Holmes, Taura; Ciner, Elise; Bailey-Wilson, Joan E
2004-09-01
Mild/moderate (common) myopia is a very common disorder, with both genetic and environmental influences. The environmental factors are related to near work and can be measured. There are no known genetic loci for common myopia. Our goal is to find evidence for a myopia susceptibility gene causing common myopia. Cycloplegic and manifest refraction were performed on 44 large American families of Ashkenazi Jewish descent, each with at least two affected siblings. Individuals with at least -1.00 diopter or lower in each meridian of both eyes were classified as myopic. Microsatellite genotyping with 387 markers was performed by the Center for Inherited Disease Research. Linkage analyses were conducted with parametric and nonparametric methods by use of 12 different penetrance models. The family-based association test was used for an association scan. A maximum multipoint parametric heterogeneity LOD (HLOD) score of 3.54 was observed at marker D22S685, and nonparametric linkage analyses gave consistent results, with a P value of.0002 at this marker. The parametric multipoint HLOD scores exceeded 3.0 for a 4-cM interval, and significant evidence of genetic heterogeneity was observed. This genomewide scan is the first step toward identifying a gene on chromosome 22 with an influence on common myopia. At present, we are following up our linkage results on chromosome 22 with a dense map of >1,500 single-nucleotide-polymorphism markers for fine mapping and association analyses. Identification of a susceptibility locus in this region may eventually lead to a better understanding of gene-environment interactions in the causation of this complex trait.
Asset Market Linkages in Crisis Periods
P. Hartmann; S. Straetmans; C.G. de Vries (Casper)
2001-01-01
textabstractWe characterize asset return linkages during periods of stress by an extremal dependence measure. Contrary to correlation analysis, this non-parametric measure is not predisposed towards the normal distribution and can account for non-linear relationships. Our estimates for the G-5 count
Asset Market Linkages in Crisis Periods
P. Hartmann; S. Straetmans; C.G. de Vries (Casper)
2001-01-01
textabstractWe characterize asset return linkages during periods of stress by an extremal dependence measure. Contrary to correlation analysis, this non-parametric measure is not predisposed towards the normal distribution and can account for non-linear relationships. Our estimates for the G-5
Pauls, David L.; Ott, Jurg; Paul, Steven M.; Allen, Cleona R.; Fann, Cathy S. J.; Carulli, John P.; Falls, Kathleen M.; Bouthillier, Christine A.; Gravius, Thomas C.; Keith, Tim P.; Egeland, Janice A.; Ginns, Edward I.
1995-01-01
Previously reported linkage of bipolar affective disorder to DNA markers in the pericentromeric region of chromosome 18 was reexamined in a larger homogeneous sample of Old Order Amish families. Four markers (D18S21, D18S53, D18S44, and D18S40) were examined in three kindreds containing 31 bipolar I (BP I) individuals. Although linkage findings were replicated in the one previously studied Amish pedigree containing four BP I individuals, linkage to this region was excluded in the larger sample. If a susceptibility locus for bipolar disorder is located in this region of chromosome 18, it is of minor significance in this population. PMID:7668292
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...
Linkage analysis of quantitative refraction and refractive errors in the Beaver Dam Eye Study.
Klein, Alison P; Duggal, Priya; Lee, Kristine E; Cheng, Ching-Yu; Klein, Ronald; Bailey-Wilson, Joan E; Klein, Barbara E K
2011-07-13
Refraction, as measured by spherical equivalent, is the need for an external lens to focus images on the retina. While genetic factors play an important role in the development of refractive errors, few susceptibility genes have been identified. However, several regions of linkage have been reported for myopia (2q, 4q, 7q, 12q, 17q, 18p, 22q, and Xq) and for quantitative refraction (1p, 3q, 4q, 7p, 8p, and 11p). To replicate previously identified linkage peaks and to identify novel loci that influence quantitative refraction and refractive errors, linkage analysis of spherical equivalent, myopia, and hyperopia in the Beaver Dam Eye Study was performed. Nonparametric, sibling-pair, genome-wide linkage analyses of refraction (spherical equivalent adjusted for age, education, and nuclear sclerosis), myopia and hyperopia in 834 sibling pairs within 486 extended pedigrees were performed. Suggestive evidence of linkage was found for hyperopia on chromosome 3, region q26 (empiric P = 5.34 × 10(-4)), a region that had shown significant genome-wide evidence of linkage to refraction and some evidence of linkage to hyperopia. In addition, the analysis replicated previously reported genome-wide significant linkages to 22q11 of adjusted refraction and myopia (empiric P = 4.43 × 10(-3) and 1.48 × 10(-3), respectively) and to 7p15 of refraction (empiric P = 9.43 × 10(-4)). Evidence was also found of linkage to refraction on 7q36 (empiric P = 2.32 × 10(-3)), a region previously linked to high myopia. The findings provide further evidence that genes controlling refractive errors are located on 3q26, 7p15, 7p36, and 22q11.
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......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...... rejects both the Cobb-Douglas and the Translog functional form, while a recently developed nonparametric kernel regression method with a fully nonparametric panel data specification delivers plausible results. On average, the nonparametric regression results are similar to results that are obtained from...
Semi- and Nonparametric ARCH Processes
Directory of Open Access Journals (Sweden)
Oliver B. Linton
2011-01-01
Full Text Available ARCH/GARCH modelling has been successfully applied in empirical finance for many years. This paper surveys the semiparametric and nonparametric methods in univariate and multivariate ARCH/GARCH models. First, we introduce some specific semiparametric models and investigate the semiparametric and nonparametrics estimation techniques applied to: the error density, the functional form of the volatility function, the relationship between mean and variance, long memory processes, locally stationary processes, continuous time processes and multivariate models. The second part of the paper is about the general properties of such processes, including stationary conditions, ergodic conditions and mixing conditions. The last part is on the estimation methods in ARCH/GARCH processes.
Wijsman, Ellen M; Rothstein, Joseph H; Igo, Robert P; Brunzell, John D; Motulsky, Arno G; Jarvik, Gail P
2010-06-01
Familial combined hyperlipidemia (FCHL) is a complex trait leading to cardiovascular disease (CVD) risk. Elevated levels and size of apolipoprotein B (apoB) and low-density lipoprotein (LDL) are associated with FCHL, which is genetically heterogeneous and is likely caused by rare variants. We carried out a linkage-based genome scan of four large FCHL pedigrees for apoB level that is independent of LDL: apoB level that is adjusted for LDL level and size. Follow-up included SNP genotyping in the region with the strongest evidence of linkage. Several regions with the evidence of linkage in individual pedigrees support the rare variant model. Evidence of linkage was strongest on chromosome 4q, with multipoint analysis in one pedigree giving LOD = 3.1 with a parametric model, and a log Bayes Factor = 1.5 from a Bayesian oligogenic approach. Of the 293 SNPs spanning the implicated region on 4q, rs6829588 completely explained the evidence of linkage. This SNP accounted for 39% of the apoB phenotypic variance, with heterozygotes for this SNP having a trait value that was approximately 30% higher than that of the high-frequency homozygote, thus identifying and considerably refining a strong candidate region. These results illustrate the advantage of using large pedigrees in the search for rare variants: reduced genetic heterogeneity within single pedigrees coupled with the large number of individuals segregating otherwise-rare single variants leads to high power to implicate such variants.
Linkage to chromosome 2q32.2-q33.3 in familial serrated neoplasia (Jass syndrome).
Roberts, Aedan; Nancarrow, Derek; Clendenning, Mark; Buchanan, Daniel D; Jenkins, Mark A; Duggan, David; Taverna, Darin; McKeone, Diane; Walters, Rhiannon; Walsh, Michael D; Young, Bruce W; Jass, Jeremy R; Rosty, Christophe; Gattas, Michael; Pelzer, Elise; Hopper, John L; Goldblatt, Jack; George, Jill; Suthers, Graeme K; Phillips, Kerry; Parry, Susan; Woodall, Sonja; Arnold, Julie; Tucker, Kathy; Muir, Amanda; Drini, Musa; Macrae, Finlay; Newcomb, Polly; Potter, John D; Pavluk, Erika; Lindblom, Annika; Young, Joanne P
2011-06-01
Causative genetic variants have to date been identified for only a small proportion of familial colorectal cancer (CRC). While conditions such as Familial Adenomatous Polyposis and Lynch syndrome have well defined genetic causes, the search for variants underlying the remainder of familial CRC is plagued by genetic heterogeneity. The recent identification of families with a heritable predisposition to malignancies arising through the serrated pathway (familial serrated neoplasia or Jass syndrome) provides an opportunity to study a subset of familial CRC in which heterogeneity may be greatly reduced. A genome-wide linkage screen was performed on a large family displaying a dominantly-inherited predisposition to serrated neoplasia genotyped using the Affymetrix GeneChip Human Mapping 10 K SNP Array. Parametric and nonparametric analyses were performed and resulting regions of interest, as well as previously reported CRC susceptibility loci at 3q22, 7q31 and 9q22, were followed up by finemapping in 10 serrated neoplasia families. Genome-wide linkage analysis revealed regions of interest at 2p25.2-p25.1, 2q24.3-q37.1 and 8p21.2-q12.1. Finemapping linkage and haplotype analyses identified 2q32.2-q33.3 as the region most likely to harbour linkage, with heterogeneity logarithm of the odds (HLOD) 2.09 and nonparametric linkage (NPL) score 2.36 (P = 0.004). Five primary candidate genes (CFLAR, CASP10, CASP8, FZD7 and BMPR2) were sequenced and no segregating variants identified. There was no evidence of linkage to previously reported loci on chromosomes 3, 7 and 9.
Nonparametric estimation of ultrasound pulses
DEFF Research Database (Denmark)
Jensen, Jørgen Arendt; Leeman, Sidney
1994-01-01
An algorithm for nonparametric estimation of 1D ultrasound pulses in echo sequences from human tissues is derived. The technique is a variation of the homomorphic filtering technique using the real cepstrum, and the underlying basis of the method is explained. The algorithm exploits a priori...
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
Masuzaki, S.; Miyazaki, T.; McCallum, J.; Heusden, van A.W.; Kik, C.; Yamashita, K.; Tashiro, Y.
2008-01-01
Integration of previously developed Allium cepa linkage maps requires the availability of anchor markers for each of the eight chromosomes of shallot (A. cepa L. common group Aggregatum). To this end, eight RAPD markers originating from our previous research were converted into SCAR markers via clon
Non-Parametric Inference in Astrophysics
Wasserman, L H; Nichol, R C; Genovese, C; Jang, W; Connolly, A J; Moore, A W; Schneider, J; Wasserman, Larry; Miller, Christopher J.; Nichol, Robert C.; Genovese, Chris; Jang, Woncheol; Connolly, Andrew J.; Moore, Andrew W.; Schneider, Jeff; group, the PICA
2001-01-01
We discuss non-parametric density estimation and regression for astrophysics problems. In particular, we show how to compute non-parametric confidence intervals for the location and size of peaks of a function. We illustrate these ideas with recent data on the Cosmic Microwave Background. We also briefly discuss non-parametric Bayesian inference.
Linkage analysis in alcohol dependence.
Windemuth, C; Hahn, A; Strauch, K; Baur, M P; Wienker, T F
1999-01-01
Alcohol dependence often is a familial disorder and has a genetic component. Research in causative factors of alcoholism is coordinated by a multi-center program, COGA [The Collaborative Study on the Genetics of Alcoholism, Begleiter et al., 1995]. We analyzed a subset of the COGA family sample, 84 pedigrees of Caucasian ancestry comprising 745 persons, 339 of whom are affected according to DSM-III-R and Feighner criteria. Using parametric and nonparametric methods, evidence for linkage was found on chromosome 1 (near markers D1S532, D1S1588, and D1S534), as well as on chromosome 15 (near marker D15S642). Other regions of the genome showed suggestive evidence for contributing loci. Related findings are discussed in recent publications investigating linkage in humans [Reich et al., 1998] and mice [Melo et al., 1996].
A non-parametric model for the cosmic velocity field
Branchini, E; Teodoro, L; Frenk, CS; Schmoldt, [No Value; Efstathiou, G; White, SDM; Saunders, W; Sutherland, W; Rowan-Robinson, M; Keeble, O; Tadros, H; Maddox, S; Oliver, S
1999-01-01
We present a self-consistent non-parametric model of the local cosmic velocity field derived from the distribution of IRAS galaxies in the PSCz redshift survey. The survey has been analysed using two independent methods, both based on the assumptions of gravitational instability and linear biasing.
Gao, Hanxiang; Li, Lin; Rao, Shaoqi; Shen, Gongqing; Xi, Quansheng; Chen, Shenghan; Zhang, Zheng; Wang, Kai; Ellis, Stephen G; Chen, Qiuyun; Topol, Eric J; Wang, Qing K
2014-01-01
Coronary artery disease (CAD) is the leading cause of death worldwide. Recent genome-wide association studies (GWAS) identified >50 common variants associated with CAD or its complication myocardial infarction (MI), but collectively they account for missing heritability". Rare variants with large effects may account for a large portion of missing heritability. Genome-wide linkage studies of large families and follow-up fine mapping and deep sequencing are particularly effective in identifying rare variants with large effects. Here we show results from a genome-wide linkage scan for CAD in multiplex GeneQuest families with early onset CAD and MI. Whole genome genotyping was carried out with 408 markers that span the human genome by every 10 cM and linkage analyses were performed using the affected relative pair analysis implemented in GENEHUNTER. Affected only nonparametric linkage (NPL) analysis identified two novel CAD loci with highly significant evidence of linkage on chromosome 3p25.1 (peak NPL = 5.49) and 3q29 (NPL = 6.84). We also identified four loci with suggestive linkage on 9q22.33, 9q34.11, 17p12, and 21q22.3 (NPL = 3.18-4.07). These results identify novel loci for CAD and provide a framework for fine mapping and deep sequencing to identify new susceptibility genes and novel variants associated with risk of CAD.
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.
Nonparametric Econometrics: The np Package
Directory of Open Access Journals (Sweden)
Tristen Hayﬁeld
2008-07-01
Full Text Available We describe the R np package via a series of applications that may be of interest to applied econometricians. The np package implements a variety of nonparametric and semiparametric kernel-based estimators that are popular among econometricians. There are also procedures for nonparametric tests of signiﬁcance and consistent model speciﬁcation tests for parametric mean regression models and parametric quantile regression models, among others. The np package focuses on kernel methods appropriate for the mix of continuous, discrete, and categorical data often found in applied settings. Data-driven methods of bandwidth selection are emphasized throughout, though we caution the user that data-driven bandwidth selection methods can be computationally demanding.
Astronomical Methods for Nonparametric Regression
Steinhardt, Charles L.; Jermyn, Adam
2017-01-01
I will discuss commonly used techniques for nonparametric regression in astronomy. We find that several of them, particularly running averages and running medians, are generically biased, asymmetric between dependent and independent variables, and perform poorly in recovering the underlying function, even when errors are present only in one variable. We then examine less-commonly used techniques such as Multivariate Adaptive Regressive Splines and Boosted Trees and find them superior in bias, asymmetry, and variance both theoretically and in practice under a wide range of numerical benchmarks. In this context the chief advantage of the common techniques is runtime, which even for large datasets is now measured in microseconds compared with milliseconds for the more statistically robust techniques. This points to a tradeoff between bias, variance, and computational resources which in recent years has shifted heavily in favor of the more advanced methods, primarily driven by Moore's Law. Along these lines, we also propose a new algorithm which has better overall statistical properties than all techniques examined thus far, at the cost of significantly worse runtime, in addition to providing guidance on choosing the nonparametric regression technique most suitable to any specific problem. We then examine the more general problem of errors in both variables and provide a new algorithm which performs well in most cases and lacks the clear asymmetry of existing non-parametric methods, which fail to account for errors in both variables.
Directory of Open Access Journals (Sweden)
2000-12-01
Full Text Available In order to carry out demographic analyses at individual and group levels, a manual method of linking individual event records from parish registers was developed in the late 1950s. In order to save time and to work with larger areas than small parishes, systems for automatic record linkage were developed a couple of decades later. A third method, an interactive record linkage, named Demolink, has been developed even more recently. The main new feature of the method is the possibility of linking from more than two historical sources simultaneously. This improves the process of sorting out which events belong to which individual life courses. This paper discusses how Demolink was used for record linkage in a large Norwegian parish for the period 1801-1878.
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.
Directory of Open Access Journals (Sweden)
Bailey-Wilson Joan E
2012-06-01
Full Text Available Abstract Background Genetic variants are likely to contribute to a portion of prostate cancer risk. Full elucidation of the genetic etiology of prostate cancer is difficult because of incomplete penetrance and genetic and phenotypic heterogeneity. Current evidence suggests that genetic linkage to prostate cancer has been found on several chromosomes including the X; however, identification of causative genes has been elusive. Methods Parametric and non-parametric linkage analyses were performed using 26 microsatellite markers in each of 11 groups of multiple-case prostate cancer families from the International Consortium for Prostate Cancer Genetics (ICPCG. Meta-analyses of the resultant family-specific linkage statistics across the entire 1,323 families and in several predefined subsets were then performed. Results Meta-analyses of linkage statistics resulted in a maximum parametric heterogeneity lod score (HLOD of 1.28, and an allele-sharing lod score (LOD of 2.0 in favor of linkage to Xq27-q28 at 138 cM. In subset analyses, families with average age at onset less than 65 years exhibited a maximum HLOD of 1.8 (at 138 cM versus a maximum regional HLOD of only 0.32 in families with average age at onset of 65 years or older. Surprisingly, the subset of families with only 2–3 affected men and some evidence of male-to-male transmission of prostate cancer gave the strongest evidence of linkage to the region (HLOD = 3.24, 134 cM. For this subset, the HLOD was slightly increased (HLOD = 3.47 at 134 cM when families used in the original published report of linkage to Xq27-28 were excluded. Conclusions Although there was not strong support for linkage to the Xq27-28 region in the complete set of families, the subset of families with earlier age at onset exhibited more evidence of linkage than families with later onset of disease. A subset of families with 2–3 affected individuals and with some evidence of male to male disease transmission
Nonparametric regression with filtered data
Linton, Oliver; Nielsen, Jens Perch; Van Keilegom, Ingrid; 10.3150/10-BEJ260
2011-01-01
We present a general principle for estimating a regression function nonparametrically, allowing for a wide variety of data filtering, for example, repeated left truncation and right censoring. Both the mean and the median regression cases are considered. The method works by first estimating the conditional hazard function or conditional survivor function and then integrating. We also investigate improved methods that take account of model structure such as independent errors and show that such methods can improve performance when the model structure is true. We establish the pointwise asymptotic normality of our estimators.
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.
Institute of Scientific and Technical Information of China (English)
刘晓倩; 周勇
2011-01-01
Expected shortfall (ES) model developed recently is a powerful mathematical tool to measure and control financial risk. In this paper, two-step kernel smoothed processes are used to develop a two-step nonparametric estimator of ES. Comparisons between the proposed two-step kernel smoothed ES estimator to the existing fully empirical ES estimator and one-step kernel smoothing ES estimator were made by calculating expectation and variance of them. It is of great interest that the proposed two-step kernel smoothed ES estimator has been shown to increases the variance, totally different from the existing result that the kernel smoothed VaR estimator can produces reduction in both the variance and the mean square error. In addition, the simulation results conform to the theoretical analysis. In the related empirical analysis, the close-ended funds in Shanghai and Shenzhen stock markets were explored to compute the empirical ES estimates and kernel smoothing ES estimates for risk analysis. And the RAROC of the funds sample based on weekly return and ES were computed to make the performance evaluation of the funds.The empirical results show that, with weekly return, the method based on ES is higher reliability than those based on VaR.%预期不足(ES)是近几年发展起来的用于测量和控制金融风险的量化工具.在金融时间序列中,将两步核估计应用于两步ES非参数估计之中,得到了ES模型的两步核光滑估计.通过计算其期望和方差,比较了两步核光滑ES估计与ES完全经验估计及一步核光滑估计的优劣,得到了有趣的结论:与VaR模型不同,两步光滑化并不能减小ES估计的方差,反而会增大其方差,并通过计算机模拟证实了理论获得的结论.对国内沪深两市中的封闭式基金进行了实证分析,计算了样本基金的ES完全经验估计、一步核光滑估计和两步核光滑估计,并计算了样本基金基于周收益率和ES的两步核光滑
Multiatlas segmentation as nonparametric regression.
Awate, Suyash P; Whitaker, Ross T
2014-09-01
This paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation. We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator's convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator. We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems.
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.
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...
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 Regression with Common Shocks
Directory of Open Access Journals (Sweden)
Eduardo A. Souza-Rodrigues
2016-09-01
Full Text Available This paper considers a nonparametric regression model for cross-sectional data in the presence of common shocks. Common shocks are allowed to be very general in nature; they do not need to be finite dimensional with a known (small number of factors. I investigate the properties of the Nadaraya-Watson kernel estimator and determine how general the common shocks can be while still obtaining meaningful kernel estimates. Restrictions on the common shocks are necessary because kernel estimators typically manipulate conditional densities, and conditional densities do not necessarily exist in the present case. By appealing to disintegration theory, I provide sufficient conditions for the existence of such conditional densities and show that the estimator converges in probability to the Kolmogorov conditional expectation given the sigma-field generated by the common shocks. I also establish the rate of convergence and the asymptotic distribution of the kernel estimator.
Nonparametric Bayesian Modeling of Complex Networks
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Mørup, Morten
2013-01-01
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...... for complex networks can be derived and point out relevant literature....
An asymptotically optimal nonparametric adaptive controller
Institute of Scientific and Technical Information of China (English)
郭雷; 谢亮亮
2000-01-01
For discrete-time nonlinear stochastic systems with unknown nonparametric structure, a kernel estimation-based nonparametric adaptive controller is constructed based on truncated certainty equivalence principle. Global stability and asymptotic optimality of the closed-loop systems are established without resorting to any external excitations.
Directory of Open Access Journals (Sweden)
Wang Jie
2008-02-01
Full Text Available Abstract Background The HapMap project aimed to catalog millions of common single nucleotide polymorphisms (SNPs in the human genome in four major populations, in order to facilitate association studies of complex diseases. To examine the transferability of Han Chinese in Beijing HapMap data to the Southern Han Chinese in Shanghai, we performed comparative analyses between genotypes from over 4,500 SNPs in a 21 Mb region on chromosome 1q21-q25 in 80 unrelated Shanghai Chinese and 45 HapMap Chinese data. Results Three thousand and forty-two SNPs were analyzed after removal of SNPs that failed quality control and those not in the HapMap panel. We compared the allele frequency distributions, linkage disequilibrium patterns, haplotype frequency distributions and tagging SNP sets transferability between the HapMap population and Shanghai Chinese population. Among the four HapMap populations, Beijing Chinese showed the best correlation with Shanghai population on allele frequencies, linkage disequilibrium and haplotype frequencies. Tagging SNP sets selected from four HapMap populations at different thresholds were evaluated in the Shanghai sample. Under the threshold of r2 equal to 0.8 or 0.5, both HapMap Chinese and Japanese data showed better coverage and tagging efficiency than Caucasian and African data. Conclusion Our study supported the applicability of HapMap Beijing Chinese SNP data to the study of complex diseases among southern Chinese population.
Gonzalez, Suzanne; Camarillo, Cynthia; Rodriguez, Marco; Ramirez, Mercedes; Zavala, Juan; Armas, Regina; Contreras, Salvador A; Contreras, Javier; Dassori, Albana; Almasy, Laura; Flores, Deborah; Jerez, Alvaro; Raventós, Henriette; Ontiveros, Alfonso; Nicolini, Humberto; Escamilla, Michael
2014-09-01
A genome-wide nonparametric linkage screen was performed to localize Bipolar Disorder (BP) susceptibility loci in a sample of 3757 individuals of Latino ancestry. The sample included 963 individuals with BP phenotype (704 relative pairs) from 686 families recruited from the US, Mexico, Costa Rica, and Guatemala. Non-parametric analyses were performed over a 5 cM grid with an average genetic coverage of 0.67 cM. Multipoint analyses were conducted across the genome using non-parametric Kong & Cox LOD scores along with Sall statistics for all relative pairs. Suggestive and significant genome-wide thresholds were calculated based on 1000 simulations. Single-marker association tests in the presence of linkage were performed assuming a multiplicative model with a population prevalence of 2%. We identified two genome-wide significant susceptibly loci for BP at 8q24 and 14q32, and a third suggestive locus at 2q13-q14. Within these three linkage regions, the top associated single marker (rs1847694, P = 2.40 × 10(-5)) is located 195 Kb upstream of DPP10 in Chromosome 2. DPP10 is prominently expressed in brain neuronal populations, where it has been shown to bind and regulate Kv4-mediated A-type potassium channels. Taken together, these results provide additional evidence that 8q24, 14q32, and 2q13-q14 are susceptibly loci for BP and these regions may be involved in the pathogenesis of BP in the Latino population. © 2014 Wiley Periodicals, Inc.
Parametric and Non-Parametric System Modelling
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg
1999-01-01
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....... For this purpose non-parametric methods together with additive models are suggested. Also, a new approach specifically designed to detect non-linearities is introduced. Confidence intervals are constructed by use of bootstrapping. As a link between non-parametric and parametric methods a paper dealing with neural...... 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...
Bayesian nonparametric duration model with censorship
Directory of Open Access Journals (Sweden)
Joseph Hakizamungu
2007-10-01
Full Text Available This paper is concerned with nonparametric i.i.d. durations models censored observations and we establish by a simple and unified approach the general structure of a bayesian nonparametric estimator for a survival function S. For Dirichlet prior distributions, we describe completely the structure of the posterior distribution of the survival function. These results are essentially supported by prior and posterior independence properties.
Bootstrap Estimation for Nonparametric Efficiency Estimates
1995-01-01
This paper develops a consistent bootstrap estimation procedure to obtain confidence intervals for nonparametric measures of productive efficiency. Although the methodology is illustrated in terms of technical efficiency measured by output distance functions, the technique can be easily extended to other consistent nonparametric frontier models. Variation in estimated efficiency scores is assumed to result from variation in empirical approximations to the true boundary of the production set. ...
A Bayesian nonparametric method for prediction in EST analysis
Directory of Open Access Journals (Sweden)
Prünster Igor
2007-09-01
Full Text Available Abstract Background Expressed sequence tags (ESTs analyses are a fundamental tool for gene identification in organisms. Given a preliminary EST sample from a certain library, several statistical prediction problems arise. In particular, it is of interest to estimate how many new genes can be detected in a future EST sample of given size and also to determine the gene discovery rate: these estimates represent the basis for deciding whether to proceed sequencing the library and, in case of a positive decision, a guideline for selecting the size of the new sample. Such information is also useful for establishing sequencing efficiency in experimental design and for measuring the degree of redundancy of an EST library. Results In this work we propose a Bayesian nonparametric approach for tackling statistical problems related to EST surveys. In particular, we provide estimates for: a the coverage, defined as the proportion of unique genes in the library represented in the given sample of reads; b the number of new unique genes to be observed in a future sample; c the discovery rate of new genes as a function of the future sample size. The Bayesian nonparametric model we adopt conveys, in a statistically rigorous way, the available information into prediction. Our proposal has appealing properties over frequentist nonparametric methods, which become unstable when prediction is required for large future samples. EST libraries, previously studied with frequentist methods, are analyzed in detail. Conclusion The Bayesian nonparametric approach we undertake yields valuable tools for gene capture and prediction in EST libraries. The estimators we obtain do not feature the kind of drawbacks associated with frequentist estimators and are reliable for any size of the additional sample.
Directory of Open Access Journals (Sweden)
Hanxiang Gao
Full Text Available Coronary artery disease (CAD is the leading cause of death worldwide. Recent genome-wide association studies (GWAS identified >50 common variants associated with CAD or its complication myocardial infarction (MI, but collectively they account for <20% of heritability, generating a phenomena of "missing heritability". Rare variants with large effects may account for a large portion of missing heritability. Genome-wide linkage studies of large families and follow-up fine mapping and deep sequencing are particularly effective in identifying rare variants with large effects. Here we show results from a genome-wide linkage scan for CAD in multiplex GeneQuest families with early onset CAD and MI. Whole genome genotyping was carried out with 408 markers that span the human genome by every 10 cM and linkage analyses were performed using the affected relative pair analysis implemented in GENEHUNTER. Affected only nonparametric linkage (NPL analysis identified two novel CAD loci with highly significant evidence of linkage on chromosome 3p25.1 (peak NPL = 5.49 and 3q29 (NPL = 6.84. We also identified four loci with suggestive linkage on 9q22.33, 9q34.11, 17p12, and 21q22.3 (NPL = 3.18-4.07. These results identify novel loci for CAD and provide a framework for fine mapping and deep sequencing to identify new susceptibility genes and novel variants associated with risk of CAD.
Why preferring parametric forecasting to nonparametric methods?
Jabot, Franck
2015-05-07
A recent series of papers by Charles T. Perretti and collaborators have shown that nonparametric forecasting methods can outperform parametric methods in noisy nonlinear systems. Such a situation can arise because of two main reasons: the instability of parametric inference procedures in chaotic systems which can lead to biased parameter estimates, and the discrepancy between the real system dynamics and the modeled one, a problem that Perretti and collaborators call "the true model myth". Should ecologists go on using the demanding parametric machinery when trying to forecast the dynamics of complex ecosystems? Or should they rely on the elegant nonparametric approach that appears so promising? It will be here argued that ecological forecasting based on parametric models presents two key comparative advantages over nonparametric approaches. First, the likelihood of parametric forecasting failure can be diagnosed thanks to simple Bayesian model checking procedures. Second, when parametric forecasting is diagnosed to be reliable, forecasting uncertainty can be estimated on virtual data generated with the fitted to data parametric model. In contrast, nonparametric techniques provide forecasts with unknown reliability. This argumentation is illustrated with the simple theta-logistic model that was previously used by Perretti and collaborators to make their point. It should convince ecologists to stick to standard parametric approaches, until methods have been developed to assess the reliability of nonparametric forecasting. Copyright © 2015 Elsevier Ltd. All rights reserved.
Institute of Scientific and Technical Information of China (English)
LIU Yong-jian; DUAN Chuan; TIAN Meng-liang; HU Er-liang; HUANG Yu-bi
2010-01-01
Analysis of multi-environment trials (METs) of crops for the evaluation and recommendation of varieties is an important issue in plant breeding research. Evaluating on the both stability of performance and high yield is essential in MET analyses. The objective of the present investigation was to compare 11 nonparametric stability statistics and apply nonparametric tests for genotype-by-environment interaction (GEI) to 14 maize (Zea mays L.) genotypes grown at 25 locations in southwestern China during 2005. Results of nonparametric tests of GEI and a combined ANOVA across locations showed that both crossover and noncrossover GEI, and genotypes varied highly significantly for yield. The results of principal component analysis, correlation analysis of nonparametric statistics, and yield indicated the nonparametric statistics grouped as four distinct classes that corresponded to different agronomic and biological concepts of stability.Furthermore, high values of TOP and low values of rank-sum were associated with high mean yield, but the other nonparametric statistics were not positively correlated with mean yield. Therefore, only rank-sum and TOP methods would be useful for simultaneously selection for high yield and stability. These two statistics recommended JY686 and HX 168 as desirable and ND 108, CM 12, CN36, and NK6661 as undesirable genotypes.
Nonparametric statistical structuring of knowledge systems using binary feature matches
DEFF Research Database (Denmark)
Mørup, Morten; Glückstad, Fumiko Kano; Herlau, Tue
2014-01-01
statistical support and how this approach generalizes to the structuring and alignment of knowledge systems. We propose a non-parametric Bayesian generative model for structuring binary feature data that does not depend on a specific choice of similarity measure. We jointly model all combinations of binary......Structuring knowledge systems with binary features is often based on imposing a similarity measure and clustering objects according to this similarity. Unfortunately, such analyses can be heavily influenced by the choice of similarity measure. Furthermore, it is unclear at which level clusters have...
Model-free linkage analysis of a binary trait.
Xu, Wei; Bull, Shelley B; Mirea, Lucia; Greenwood, Celia M T
2012-01-01
Genetic linkage analysis aims to detect chromosomal regions containing genes that influence risk of specific inherited diseases. The presence of linkage is indicated when a disease or trait cosegregates through the families with genetic markers at a particular region of the genome. Two main types of genetic linkage analysis are in common use, namely model-based linkage analysis and model-free linkage analysis. In this chapter, we focus solely on the latter type and specifically on binary traits or phenotypes, such as the presence or absence of a specific disease. Model-free linkage analysis is based on allele-sharing, where patterns of genetic similarity among affected relatives are compared to chance expectations. Because the model-free methods do not require the specification of the inheritance parameters of a genetic model, they are preferred by many researchers at early stages in the study of a complex disease. We introduce the history of model-free linkage analysis in Subheading 1. Table 1 describes a standard model-free linkage analysis workflow. We describe three popular model-free linkage analysis methods, the nonparametric linkage (NPL) statistic, the affected sib-pair (ASP) likelihood ratio test, and a likelihood approach for pedigrees. The theory behind each linkage test is described in this section, together with a simple example of the relevant calculations. Table 4 provides a summary of popular genetic analysis software packages that implement model-free linkage models. In Subheading 2, we work through the methods on a rich example providing sample software code and output. Subheading 3 contains notes with additional details on various topics that may need further consideration during analysis.
Nonparametric correlation models for portfolio allocation
DEFF Research Database (Denmark)
Aslanidis, Nektarios; Casas, Isabel
2013-01-01
breaks in correlations. Only when correlations are constant does the parametric DCC model deliver the best outcome. The methodologies are illustrated by evaluating two interesting portfolios. The first portfolio consists of the equity sector SPDRs and the S&P 500, while the second one contains major......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....
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
Correlated Non-Parametric Latent Feature Models
Doshi-Velez, Finale
2012-01-01
We are often interested in explaining data through a set of hidden factors or features. When the number of hidden features is unknown, the Indian Buffet Process (IBP) is a nonparametric latent feature model that does not bound the number of active features in dataset. However, the IBP assumes that all latent features are uncorrelated, making it inadequate for many realworld problems. We introduce a framework for correlated nonparametric feature models, generalising the IBP. We use this framework to generate several specific models and demonstrate applications on realworld datasets.
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...... breaks in correlations. Only when correlations are constant does the parametric DCC model deliver the best outcome. The methodologies are illustrated by evaluating two interesting portfolios. The first portfolio consists of the equity sector SPDRs and the S&P 500, while the second one contains major...
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...
Thirty years of nonparametric item response theory
Molenaar, W.
2001-01-01
Relationships between a mathematical measurement model and its real-world applications are discussed. A distinction is made between large data matrices commonly found in educational measurement and smaller matrices found in attitude and personality measurement. Nonparametric methods are evaluated fo
A Bayesian Nonparametric Approach to Test Equating
Karabatsos, George; Walker, Stephen G.
2009-01-01
A Bayesian nonparametric model is introduced for score equating. It is applicable to all major equating designs, and has advantages over previous equating models. Unlike the previous models, the Bayesian model accounts for positive dependence between distributions of scores from two tests. The Bayesian model and the previous equating models are…
How Are Teachers Teaching? A Nonparametric Approach
De Witte, Kristof; Van Klaveren, Chris
2014-01-01
This paper examines which configuration of teaching activities maximizes student performance. For this purpose a nonparametric efficiency model is formulated that accounts for (1) self-selection of students and teachers in better schools and (2) complementary teaching activities. The analysis distinguishes both individual teaching (i.e., a…
Nonparametric confidence intervals for monotone functions
Groeneboom, P.; Jongbloed, G.
2015-01-01
We study nonparametric isotonic confidence intervals for monotone functions. In [Ann. Statist. 29 (2001) 1699–1731], pointwise confidence intervals, based on likelihood ratio tests using the restricted and unrestricted MLE in the current status model, are introduced. We extend the method to the trea
Decompounding random sums: A nonparametric approach
DEFF Research Database (Denmark)
Hansen, Martin Bøgsted; Pitts, Susan M.
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...
Nonparametric confidence intervals for monotone functions
Groeneboom, P.; Jongbloed, G.
2015-01-01
We study nonparametric isotonic confidence intervals for monotone functions. In [Ann. Statist. 29 (2001) 1699–1731], pointwise confidence intervals, based on likelihood ratio tests using the restricted and unrestricted MLE in the current status model, are introduced. We extend the method to the
A Nonparametric Analogy of Analysis of Covariance
Burnett, Thomas D.; Barr, Donald R.
1977-01-01
A nonparametric test of the hypothesis of no treatment effect is suggested for a situation where measures of the severity of the condition treated can be obtained and ranked both pre- and post-treatment. The test allows the pre-treatment rank to be used as a concomitant variable. (Author/JKS)
How Are Teachers Teaching? A Nonparametric Approach
De Witte, Kristof; Van Klaveren, Chris
2014-01-01
This paper examines which configuration of teaching activities maximizes student performance. For this purpose a nonparametric efficiency model is formulated that accounts for (1) self-selection of students and teachers in better schools and (2) complementary teaching activities. The analysis distinguishes both individual teaching (i.e., a…
DEFF Research Database (Denmark)
Ansari-Mahyari, S; Berg, P; Lund, M S
2009-01-01
In fine mapping of a large-scale experimental population where collection of phenotypes are very expensive, difficult to record or time-demanding, selective phenotyping could be used to phenotype the most informative individuals. Linkage analyses based sampling criteria (LAC) and linkage...... 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...
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
Measuring the Influence of Networks on Transaction Costs Using a Nonparametric Regression Technique
DEFF Research Database (Denmark)
Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H.C.A.
. We empirically analyse the effect of networks on productivity using a cross-validated local linear non-parametric regression technique and a data set of 384 farms in Poland. Our empirical study generally supports our hypothesis that networks affect productivity. Large and dense trading networks...
Measuring the influence of networks on transaction costs using a non-parametric regression technique
DEFF Research Database (Denmark)
Henningsen, Géraldine; Henningsen, Arne; Henning, Christian H.C.A.
. We empirically analyse the effect of networks on productivity using a cross-validated local linear non-parametric regression technique and a data set of 384 farms in Poland. Our empirical study generally supports our hypothesis that networks affect productivity. Large and dense trading networks...
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
Linkage and association analysis of CACNG3 in childhood absence epilepsy
DEFF Research Database (Denmark)
Everett, Kate V; Chioza, Barry; Aicardi, Jean
2007-01-01
and association analysis. Assuming locus heterogeneity, a significant HLOD score (HLOD = 3.54, alpha = 0.62) was obtained for markers encompassing CACNG3 in 65 nuclear families with a proband with CAE. The maximum non-parametric linkage score was 2.87 (P ... and the 65 nuclear pedigrees. Evidence for transmission disequilibrium (P
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.
2007-01-01
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
Nonparametric tests for pathwise properties of semimartingales
Cont, Rama; 10.3150/10-BEJ293
2011-01-01
We propose two nonparametric tests for investigating the pathwise properties of a signal modeled as the sum of a L\\'{e}vy process and a Brownian semimartingale. Using a nonparametric threshold estimator for the continuous component of the quadratic variation, we design a test for the presence of a continuous martingale component in the process and a test for establishing whether the jumps have finite or infinite variation, based on observations on a discrete-time grid. We evaluate the performance of our tests using simulations of various stochastic models and use the tests to investigate the fine structure of the DM/USD exchange rate fluctuations and SPX futures prices. In both cases, our tests reveal the presence of a non-zero Brownian component and a finite variation jump component.
Nonparametric Transient Classification using Adaptive Wavelets
Varughese, Melvin M; Stephanou, Michael; Bassett, Bruce A
2015-01-01
Classifying transients based on multi band light curves is a challenging but crucial problem in the era of GAIA and LSST since the sheer volume of transients will make spectroscopic classification unfeasible. Here we present a nonparametric classifier that uses the transient's light curve measurements to predict its class given training data. It implements two novel components: the first is the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients. The second novelty is the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The ranked classifier is simple and quick to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant, hence they do not need the light curves to be aligned to extract features. Further, BAGIDIS is nonparametric so it can be used for blind ...
A Bayesian nonparametric meta-analysis model.
Karabatsos, George; Talbott, Elizabeth; Walker, Stephen G
2015-03-01
In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the underlying population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size population distribution, conditionally on parameters and covariates. For estimating the mean overall effect size, such models may be adequate, but for prediction, they surely are not if the effect-size distribution exhibits non-normal behavior. To address this issue, we propose a Bayesian nonparametric meta-analysis model, which can describe a wider range of effect-size distributions, including unimodal symmetric distributions, as well as skewed and more multimodal distributions. We demonstrate our model through the analysis of real meta-analytic data arising from behavioral-genetic research. We compare the predictive performance of the Bayesian nonparametric model against various conventional and more modern normal fixed-effects and random-effects models.
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.
Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study
Directory of Open Access Journals (Sweden)
Anestis Antoniadis
2001-06-01
Full Text Available Wavelet analysis has been found to be a powerful tool for the nonparametric estimation of spatially-variable objects. We discuss in detail wavelet methods in nonparametric regression, where the data are modelled as observations of a signal contaminated with additive Gaussian noise, and provide an extensive review of the vast literature of wavelet shrinkage and wavelet thresholding estimators developed to denoise such data. These estimators arise from a wide range of classical and empirical Bayes methods treating either individual or blocks of wavelet coefficients. We compare various estimators in an extensive simulation study on a variety of sample sizes, test functions, signal-to-noise ratios and wavelet filters. Because there is no single criterion that can adequately summarise the behaviour of an estimator, we use various criteria to measure performance in finite sample situations. Insight into the performance of these estimators is obtained from graphical outputs and numerical tables. In order to provide some hints of how these estimators should be used to analyse real data sets, a detailed practical step-by-step illustration of a wavelet denoising analysis on electrical consumption is provided. Matlab codes are provided so that all figures and tables in this paper can be reproduced.
Nonparametric Bayes analysis of social science data
Kunihama, Tsuyoshi
Social science data often contain complex characteristics that standard statistical methods fail to capture. Social surveys assign many questions to respondents, which often consist of mixed-scale variables. Each of the variables can follow a complex distribution outside parametric families and associations among variables may have more complicated structures than standard linear dependence. Therefore, it is not straightforward to develop a statistical model which can approximate structures well in the social science data. In addition, many social surveys have collected data over time and therefore we need to incorporate dynamic dependence into the models. Also, it is standard to observe massive number of missing values in the social science data. To address these challenging problems, this thesis develops flexible nonparametric Bayesian methods for the analysis of social science data. Chapter 1 briefly explains backgrounds and motivations of the projects in the following chapters. Chapter 2 develops a nonparametric Bayesian modeling of temporal dependence in large sparse contingency tables, relying on a probabilistic factorization of the joint pmf. Chapter 3 proposes nonparametric Bayes inference on conditional independence with conditional mutual information used as a measure of the strength of conditional dependence. Chapter 4 proposes a novel Bayesian density estimation method in social surveys with complex designs where there is a gap between sample and population. We correct for the bias by adjusting mixture weights in Bayesian mixture models. Chapter 5 develops a nonparametric model for mixed-scale longitudinal surveys, in which various types of variables can be induced through latent continuous variables and dynamic latent factors lead to flexibly time-varying associations among variables.
Bayesian nonparametric estimation for Quantum Homodyne Tomography
Naulet, Zacharie; Barat, Eric
2016-01-01
We estimate the quantum state of a light beam from results of quantum homodyne tomography noisy measurements performed on identically prepared quantum systems. We propose two Bayesian nonparametric approaches. The first approach is based on mixture models and is illustrated through simulation examples. The second approach is based on random basis expansions. We study the theoretical performance of the second approach by quantifying the rate of contraction of the posterior distribution around ...
NONPARAMETRIC ESTIMATION OF CHARACTERISTICS OF PROBABILITY DISTRIBUTIONS
Directory of Open Access Journals (Sweden)
Orlov A. I.
2015-10-01
Full Text Available The article is devoted to the nonparametric point and interval estimation of the characteristics of the probabilistic distribution (the expectation, median, variance, standard deviation, variation coefficient of the sample results. Sample values are regarded as the implementation of independent and identically distributed random variables with an arbitrary distribution function having the desired number of moments. Nonparametric analysis procedures are compared with the parametric procedures, based on the assumption that the sample values have a normal distribution. Point estimators are constructed in the obvious way - using sample analogs of the theoretical characteristics. Interval estimators are based on asymptotic normality of sample moments and functions from them. Nonparametric asymptotic confidence intervals are obtained through the use of special output technology of the asymptotic relations of Applied Statistics. In the first step this technology uses the multidimensional central limit theorem, applied to the sums of vectors whose coordinates are the degrees of initial random variables. The second step is the conversion limit multivariate normal vector to obtain the interest of researcher vector. At the same considerations we have used linearization and discarded infinitesimal quantities. The third step - a rigorous justification of the results on the asymptotic standard for mathematical and statistical reasoning level. It is usually necessary to use the necessary and sufficient conditions for the inheritance of convergence. This article contains 10 numerical examples. Initial data - information about an operating time of 50 cutting tools to the limit state. Using the methods developed on the assumption of normal distribution, it can lead to noticeably distorted conclusions in a situation where the normality hypothesis failed. Practical recommendations are: for the analysis of real data we should use nonparametric confidence limits
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.
DEFF Research Database (Denmark)
Shete, Sanjay; Lau, Ching C; Houlston, Richard S;
2011-01-01
-fold increased risk of glioma, the search for susceptibility loci in familial forms of the disease has been challenging because the disease is relatively rare, fatal, and heterogeneous, making it difficult to collect sufficient biosamples from families for statistical power. To address this challenge...... nonparametric (model-free) methods. After removal of high linkage disequilibrium single-nucleotide polymorphism, we obtained a maximum nonparametric linkage score (NPL) of 3.39 (P = 0.0005) at 17q12-21.32 and the Z-score of 4.20 (P = 0.000007). To replicate our findings, we genotyped 29 independent U...
A high-density screen for linkage in multiple sclerosis.
Sawcer, Stephen; Ban, Maria; Maranian, Mel; Yeo, Tai Wai; Compston, Alastair; Kirby, Andrew; Daly, Mark J; De Jager, Philip L; Walsh, Emily; Lander, Eric S; Rioux, John D; Hafler, David A; Ivinson, Adrian; Rimmler, Jacqueline; Gregory, Simon G; Schmidt, Silke; Pericak-Vance, Margaret A; Akesson, Eva; Hillert, Jan; Datta, Pameli; Oturai, Annette; Ryder, Lars P; Harbo, Hanne F; Spurkland, Anne; Myhr, Kjell-Morten; Laaksonen, Mikko; Booth, David; Heard, Robert; Stewart, Graeme; Lincoln, Robin; Barcellos, Lisa F; Hauser, Stephen L; Oksenberg, Jorge R; Kenealy, Shannon J; Haines, Jonathan L
2005-09-01
To provide a definitive linkage map for multiple sclerosis, we have genotyped the Illumina BeadArray linkage mapping panel (version 4) in a data set of 730 multiplex families of Northern European descent. After the application of stringent quality thresholds, data from 4,506 markers in 2,692 individuals were included in the analysis. Multipoint nonparametric linkage analysis revealed highly significant linkage in the major histocompatibility complex (MHC) on chromosome 6p21 (maximum LOD score [MLS] 11.66) and suggestive linkage on chromosomes 17q23 (MLS 2.45) and 5q33 (MLS 2.18). This set of markers achieved a mean information extraction of 79.3% across the genome, with a Mendelian inconsistency rate of only 0.002%. Stratification based on carriage of the multiple sclerosis-associated DRB1*1501 allele failed to identify any other region of linkage with genomewide significance. However, ordered-subset analysis suggested that there may be an additional locus on chromosome 19p13 that acts independent of the main MHC locus. These data illustrate the substantial increase in power that can be achieved with use of the latest tools emerging from the Human Genome Project and indicate that future attempts to systematically identify susceptibility genes for multiple sclerosis will have to involve large sample sizes and an association-based methodology.
Schraagen, Marijn Paul
2014-01-01
This thesis is an exploration of the subject of historical record linkage. The general goal of historical record linkage is to discover relations between historical entities in a database, for any specific definition of relation, entity and database. Although this task originates from historical
DEFF Research Database (Denmark)
Andersson, Ulf; Perri, Alessandra; Nell, Phillip C.
2012-01-01
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...
Of River Linkage and Issue Linkage
Warner, Jeroen Frank
2016-01-01
It is a truism in mainstream International Relations that issue linkage promotes regime formation and integration. The present article applies this idea to the transboundary lower river Meuse and finds its history of integration to be a tortuous one. Contextual political factors have at times
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.
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.
A nonparametric and diversified portfolio model
Shirazi, Yasaman Izadparast; Sabiruzzaman, Md.; Hamzah, Nor Aishah
2014-07-01
Traditional portfolio models, like mean-variance (MV) suffer from estimation error and lack of diversity. Alternatives, like mean-entropy (ME) or mean-variance-entropy (MVE) portfolio models focus independently on the issue of either a proper risk measure or the diversity. In this paper, we propose an asset allocation model that compromise between risk of historical data and future uncertainty. In the new model, entropy is presented as a nonparametric risk measure as well as an index of diversity. Our empirical evaluation with a variety of performance measures shows that this model has better out-of-sample performances and lower portfolio turnover than its competitors.
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 pointed...... out, and methods to prevent bias are presented. The techniques are evaluated by comparing their speed and accuracy on the simple case of estimating auto-correlation functions for the response of a single degree-of-freedom system loaded with white noise....
Lottery spending: a non-parametric analysis.
Garibaldi, Skip; Frisoli, Kayla; Ke, Li; Lim, Melody
2015-01-01
We analyze the spending of individuals in the United States on lottery tickets in an average month, as reported in surveys. We view these surveys as sampling from an unknown distribution, and we use non-parametric methods to compare properties of this distribution for various demographic groups, as well as claims that some properties of this distribution are constant across surveys. We find that the observed higher spending by Hispanic lottery players can be attributed to differences in education levels, and we dispute previous claims that the top 10% of lottery players consistently account for 50% of lottery sales.
Lottery spending: a non-parametric analysis.
Directory of Open Access Journals (Sweden)
Skip Garibaldi
Full Text Available We analyze the spending of individuals in the United States on lottery tickets in an average month, as reported in surveys. We view these surveys as sampling from an unknown distribution, and we use non-parametric methods to compare properties of this distribution for various demographic groups, as well as claims that some properties of this distribution are constant across surveys. We find that the observed higher spending by Hispanic lottery players can be attributed to differences in education levels, and we dispute previous claims that the top 10% of lottery players consistently account for 50% of lottery sales.
Nonparametric inferences for kurtosis and conditional kurtosis
Institute of Scientific and Technical Information of China (English)
XIE Xiao-heng; HE You-hua
2009-01-01
Under the assumption of strictly stationary process, this paper proposes a nonparametric model to test the kurtosis and conditional kurtosis for risk time series. We apply this method to the daily returns of S&P500 index and the Shanghai Composite Index, and simulate GARCH data for verifying the efficiency of the presented model. Our results indicate that the risk series distribution is heavily tailed, but the historical information can make its future distribution light-tailed. However the far future distribution's tails are little affected by the historical data.
Parametric versus non-parametric simulation
Dupeux, Bérénice; Buysse, Jeroen
2014-01-01
Most of ex-ante impact assessment policy models have been based on a parametric approach. We develop a novel non-parametric approach, called Inverse DEA. We use non parametric efficiency analysis for determining the farm’s technology and behaviour. Then, we compare the parametric approach and the Inverse DEA models to a known data generating process. We use a bio-economic model as a data generating process reflecting a real world situation where often non-linear relationships exist. Results s...
Preliminary results on nonparametric facial occlusion detection
Directory of Open Access Journals (Sweden)
Daniel LÓPEZ SÁNCHEZ
2016-10-01
Full Text Available The problem of face recognition has been extensively studied in the available literature, however, some aspects of this field require further research. The design and implementation of face recognition systems that can efficiently handle unconstrained conditions (e.g. pose variations, illumination, partial occlusion... is still an area under active research. This work focuses on the design of a new nonparametric occlusion detection technique. In addition, we present some preliminary results that indicate that the proposed technique might be useful to face recognition systems, allowing them to dynamically discard occluded face parts.
Application of a nonparametric approach to analyze delta-pCO2 data from the Southern Ocean
CSIR Research Space (South Africa)
Pretorius, WB
2011-11-01
Full Text Available In this paper the authors discuss the application of a classical nonparametric inference approach to analyse delta-pCO2 measurements from the Southern Ocean, which is a novel method to analysing data in this area, as well as comparing results...
An estimating function approach to linkage heterogeneity
Indian Academy of Sciences (India)
He Gao; Ying Zhou; Weijun Ma; Haidong Liu; Linan Zhao
2013-12-01
Testing linkage heterogeneity between two loci is an important issue in genetics. Currently, there are four methods (K-test, A-test, B-test and D-test) for testing linkage heterogeneity in linkage analysis, which are based on the likelihood-ratio test. Among them, the commonly used methods are the K-test and A-test. In this paper, we present a novel test method which is different from the above four tests, called G-test. The new test statistic is based on estimating function, possessing a theoretic asymptotic distribution, and therefore demonstrates its own advantages. The proposed test is applied to analyse a real pedigree dataset. Our simulation results also indicate that the G-test performs well in terms of power of testing linkage heterogeneity and outperforms the current methods to some degree.
Directory of Open Access Journals (Sweden)
Yin-Ju Lien
Full Text Available BACKGROUND: As schizophrenia is genetically and phenotypically heterogeneous, targeting genetically informative phenotypes may help identify greater linkage signals. The aim of the study is to evaluate the genetic linkage evidence for schizophrenia in subsets of families with earlier age at onset or greater neurocognitive deficits. METHODS: Patients with schizophrenia (n = 1,207 and their first-degree relatives (n = 1,035 from 557 families with schizophrenia were recruited from six data collection field research centers throughout Taiwan. Subjects completed a face-to-face semi-structured interview, the Continuous Performance Test (CPT, the Wisconsin Card Sorting Test, and were genotyped with 386 microsatellite markers across the genome. RESULTS: A maximum nonparametric logarithm of odds (LOD score of 4.17 at 2q22.1 was found in 295 families ranked by increasing age at onset, which had significant increases in the maximum LOD score compared with those obtained in initial linkage analyses using all available families. Based on this subset, a further subsetting by false alarm rate on the undegraded and degraded CPT obtained further increase in the nested subset-based LOD on 2q22.1, with a score of 7.36 in 228 families and 7.71 in 243 families, respectively. CONCLUSION: We found possible evidence of linkage on chromosome 2q22.1 in families of schizophrenia patients with more CPT false alarm rates nested within the families with younger age at onset. These results highlight the importance of incorporating genetically informative phenotypes in unraveling the complex genetics of schizophrenia.
DEFF Research Database (Denmark)
Perri, Alessandra; Andersson, Ulf; Nell, Phillip C.;
This paper investigates local vertical linkages of foreign subsidiaries and the dual role of such linkages as conduits for learning as well as potential channels for spillovers to competitors. On the basis of data from 97 subsidiaries, we analyze the quality of such linkages under varying levels...... of competition and subsidiary capabilities. Our theoretical development and the results from the analysis document a far more complex and dynamic relationship between levels of competition and MNCs’ local participation in knowledge intensive activities, i.e. learning and spillovers, than previous studies do. We...
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.
Linkage analysis of chromosome 14 and essential hypertension in Chinese population
Institute of Scientific and Technical Information of China (English)
ZHAO Wei-yan; HUANG Jian-feng; GE Dong-liang; SU Shao-yong; LI Biao; GU Dong-feng
2005-01-01
Background Hypertension is a complex biological trait that influenced by multiple factors. The encouraging results for hypertension research showed that the linkage analysis can be used to replicate other studies and discover new genetic risk factors. Previous studies linked human chromosome 14 to essential hypertension or blood pressure traits. With a Chinese population, we tried to replicate these findings. Methods A linkage scan was performed on chromosome 14 with 14-microsatellite markers with a density of about 10 centi Morgen (cM) in 147 Chinese hypertensive nuclear families. Multipoint non-parametric linkage analysis and exclusion mapping were performed with the GENEHUNTER software, whereas quantitative analysis was performed with the variance component method integrated in the SOLAR package. Results In the qualitative analysis, the highest non-parametric linkage score is 1.0 (P=0.14) at D14S261 in the single point analysis, and no loci achieved non-parametric linkage score more than 1.0 in the multipoint analysis. Maximum-likelihood mapping showed no significant results, either. Subsequently the traditional exclusion criteria of the log-of-the-odds score-2 were adopted, and the chromosome 14 with λs≥2.4 was excluded. In the quantitative analysis of blood pressure with the SOLAR software, two-point analysis and multipoint analysis suggested no evidence for linkage occurred on chromosome 14 for systolic and diastolic blood pressure. Conclusion There was no substantial evidence to support the linkage of chromosome 14 and essential hypertension or blood pressure trait in Chinese hypertensive subjects in this study.
Nonparametric dark energy reconstruction from supernova data.
Holsclaw, Tracy; Alam, Ujjaini; Sansó, Bruno; Lee, Herbert; Heitmann, Katrin; Habib, Salman; Higdon, David
2010-12-10
Understanding the origin of the accelerated expansion of the Universe poses one of the greatest challenges in physics today. Lacking a compelling fundamental theory to test, observational efforts are targeted at a better characterization of the underlying cause. If a new form of mass-energy, dark energy, is driving the acceleration, the redshift evolution of the equation of state parameter w(z) will hold essential clues as to its origin. To best exploit data from observations it is necessary to develop a robust and accurate reconstruction approach, with controlled errors, for w(z). We introduce a new, nonparametric method for solving the associated statistical inverse problem based on Gaussian process modeling and Markov chain Monte Carlo sampling. Applying this method to recent supernova measurements, we reconstruct the continuous history of w out to redshift z=1.5.
Local Component Analysis for Nonparametric Bayes Classifier
Khademi, Mahmoud; safayani, Meharn
2010-01-01
The decision boundaries of Bayes classifier are optimal because they lead to maximum probability of correct decision. It means if we knew the prior probabilities and the class-conditional densities, we could design a classifier which gives the lowest probability of error. However, in classification based on nonparametric density estimation methods such as Parzen windows, the decision regions depend on the choice of parameters such as window width. Moreover, these methods suffer from curse of dimensionality of the feature space and small sample size problem which severely restricts their practical applications. In this paper, we address these problems by introducing a novel dimension reduction and classification method based on local component analysis. In this method, by adopting an iterative cross-validation algorithm, we simultaneously estimate the optimal transformation matrices (for dimension reduction) and classifier parameters based on local information. The proposed method can classify the data with co...
Nonparametric k-nearest-neighbor entropy estimator.
Lombardi, Damiano; Pant, Sanjay
2016-01-01
A nonparametric k-nearest-neighbor-based entropy estimator is proposed. It improves on the classical Kozachenko-Leonenko estimator by considering nonuniform probability densities in the region of k-nearest neighbors around each sample point. It aims to improve the classical estimators in three situations: first, when the dimensionality of the random variable is large; second, when near-functional relationships leading to high correlation between components of the random variable are present; and third, when the marginal variances of random variable components vary significantly with respect to each other. Heuristics on the error of the proposed and classical estimators are presented. Finally, the proposed estimator is tested for a variety of distributions in successively increasing dimensions and in the presence of a near-functional relationship. Its performance is compared with a classical estimator, and a significant improvement is demonstrated.
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 Maximum Entropy Estimation on Information Diagrams
Martin, Elliot A; Meinke, Alexander; Děchtěrenko, Filip; Davidsen, Jörn
2016-01-01
Maximum entropy estimation is of broad interest for inferring properties of systems across many different disciplines. In this work, we significantly extend a technique we previously introduced for estimating the maximum entropy of a set of random discrete variables when conditioning on bivariate mutual informations and univariate entropies. Specifically, we show how to apply the concept to continuous random variables and vastly expand the types of information-theoretic quantities one can condition on. This allows us to establish a number of significant advantages of our approach over existing ones. Not only does our method perform favorably in the undersampled regime, where existing methods fail, but it also can be dramatically less computationally expensive as the cardinality of the variables increases. In addition, we propose a nonparametric formulation of connected informations and give an illustrative example showing how this agrees with the existing parametric formulation in cases of interest. We furthe...
Nonparametric estimation of employee stock options
Institute of Scientific and Technical Information of China (English)
FU Qiang; LIU Li-an; LIU Qian
2006-01-01
We proposed a new model to price employee stock options (ESOs). The model is based on nonparametric statistical methods with market data. It incorporates the kernel estimator and employs a three-step method to modify BlackScholes formula. The model overcomes the limits of Black-Scholes formula in handling option prices with varied volatility. It disposes the effects of ESOs self-characteristics such as non-tradability, the longer term for expiration, the early exercise feature, the restriction on shorting selling and the employee's risk aversion on risk neutral pricing condition, and can be applied to ESOs valuation with the explanatory variable in no matter the certainty case or random case.
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 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
A nonparametric dynamic additive regression model for longitudinal data
DEFF Research Database (Denmark)
Martinussen, Torben; Scheike, Thomas 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...
Nonparametric Bayesian inference for multidimensional compound Poisson processes
S. Gugushvili; F. van der Meulen; P. Spreij
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, whic
Asymptotic theory of nonparametric regression estimates with censored data
Institute of Scientific and Technical Information of China (English)
施沛德; 王海燕; 张利华
2000-01-01
For regression analysis, some useful Information may have been lost when the responses are right censored. To estimate nonparametric functions, several estimates based on censored data have been proposed and their consistency and convergence rates have been studied in literat黵e, but the optimal rates of global convergence have not been obtained yet. Because of the possible Information loss, one may think that it is impossible for an estimate based on censored data to achieve the optimal rates of global convergence for nonparametric regression, which were established by Stone based on complete data. This paper constructs a regression spline estimate of a general nonparametric regression f unction based on right-censored response data, and proves, under some regularity condi-tions, that this estimate achieves the optimal rates of global convergence for nonparametric regression. Since the parameters for the nonparametric regression estimate have to be chosen based on a data driven criterion, we also obtai
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...
Genome-wide linkage analysis for human longevity
DEFF Research Database (Denmark)
Beekman, Marian; Blanché, Hélène; Perola, Markus
2013-01-01
sibling pairs that have been enrolled in 15 study centers of 11 European countries as part of the Genetics of Healthy Aging (GEHA) project. In the joint linkage analyses, we observed four regions that show linkage with longevity; chromosome 14q11.2 (LOD = 3.47), chromosome 17q12-q22 (LOD = 2...
Directory of Open Access Journals (Sweden)
Farook Thameem
Full Text Available OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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
A Linkage Learning Genetic Algorithm with Linkage Matrix
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
The goal of linkage learning, or building block identification, is the creation of a more effective Genetic Algorithm (GA). This paper proposes a new Linkage Learning Genetic Algorithms, named m-LLGA. With the linkage learning module and the linkage-based genetic operation, m-LLGA is not only able to learn and record the linkage information among genes without any prior knowledge of the function being optimized. It also can use the linkage information stored in the linkage matrix to guide the selection of crossover point. The preliminary experiments on two kinds of bounded difficulty problems and a TSP problem validated the performance of m-LLGA. The m-LLGA learns the linkage of different building blocks parallel and therefore solves these problems effectively; it can also reasonably reduce the probability of building blocks being disrupted by crossover at the same time give attention to getting away from local minimum.
Cosmopolitan linkage disequilibrium maps
Directory of Open Access Journals (Sweden)
Gibson Jane
2005-03-01
Full Text Available Abstract Linkage maps have been invaluable for the positional cloning of many genes involved in severe human diseases. Standard genetic linkage maps have been constructed for this purpose from the Centre d'Etude du Polymorphisme Humain and other panels, and have been widely used. Now that attention has shifted towards identifying genes predisposing to common disorders using linkage disequilibrium (LD and maps of single nucleotide polymorphisms (SNPs, it is of interest to consider a standard LD map which is somewhat analogous to the corresponding map for linkage. We have constructed and evaluated a cosmopolitan LD map by combining samples from a small number of populations using published data from a 10-megabase region on chromosome 20. In support of a pilot study, which examined a number of small genomic regions with a lower density of markers, we have found that a cosmopolitan map, which serves all populations when appropriately scaled, recovers 91 to 95 per cent of the information within population-specific maps. Recombination hot spots appear to have a dominant role in shaping patterns of LD. The success of the cosmopolitan map might be attributed to the co-localisation of hot spots in all populations. Although there must be finer scale differences between populations due to other processes (mutation, drift, selection, the results suggest that a whole-genome standard LD map would indeed be a useful resource for disease gene mapping.
A Comparison of Parametric and Non-Parametric Methods Applied to a Likert Scale.
Mircioiu, Constantin; Atkinson, Jeffrey
2017-05-10
A trenchant and passionate dispute over the use of parametric versus non-parametric methods for the analysis of Likert scale ordinal data has raged for the past eight decades. The answer is not a simple "yes" or "no" but is related to hypotheses, objectives, risks, and paradigms. In this paper, we took a pragmatic approach. We applied both types of methods to the analysis of actual Likert data on responses from different professional subgroups of European pharmacists regarding competencies for practice. Results obtained show that with "large" (>15) numbers of responses and similar (but clearly not normal) distributions from different subgroups, parametric and non-parametric analyses give in almost all cases the same significant or non-significant results for inter-subgroup comparisons. Parametric methods were more discriminant in the cases of non-similar conclusions. Considering that the largest differences in opinions occurred in the upper part of the 4-point Likert scale (ranks 3 "very important" and 4 "essential"), a "score analysis" based on this part of the data was undertaken. This transformation of the ordinal Likert data into binary scores produced a graphical representation that was visually easier to understand as differences were accentuated. In conclusion, in this case of Likert ordinal data with high response rates, restraining the analysis to non-parametric methods leads to a loss of information. The addition of parametric methods, graphical analysis, analysis of subsets, and transformation of data leads to more in-depth analyses.
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.
Nonparametric methods in actigraphy: An update
Gonçalves, Bruno S.B.; Cavalcanti, Paula R.A.; Tavares, Gracilene R.; Campos, Tania F.; Araujo, John F.
2014-01-01
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. PMID:26483921
Nonparametric Detection of Geometric Structures Over Networks
Zou, Shaofeng; Liang, Yingbin; Poor, H. Vincent
2017-10-01
Nonparametric detection of existence of an anomalous structure over a network is investigated. Nodes corresponding to the anomalous structure (if one exists) receive samples generated by a distribution q, which is different from a distribution p generating samples for other nodes. If an anomalous structure does not exist, all nodes receive samples generated by p. It is assumed that the distributions p and q are arbitrary and unknown. The goal is to design statistically consistent tests with probability of errors converging to zero as the network size becomes asymptotically large. Kernel-based tests are proposed based on maximum mean discrepancy that measures the distance between mean embeddings of distributions into a reproducing kernel Hilbert space. Detection of an anomalous interval over a line network is first studied. Sufficient conditions on minimum and maximum sizes of candidate anomalous intervals are characterized in order to guarantee the proposed test to be consistent. It is also shown that certain necessary conditions must hold to guarantee any test to be universally consistent. Comparison of sufficient and necessary conditions yields that the proposed test is order-level optimal and nearly optimal respectively in terms of minimum and maximum sizes of candidate anomalous intervals. Generalization of the results to other networks is further developed. Numerical results are provided to demonstrate the performance of the proposed tests.
The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard
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...... to avoid this problem. The main objective is to investigate the applicability of the nonparametric kernel regression method in applied production analysis. The focus of the empirical analyses included in this thesis is the agricultural sector in Poland. Data on Polish farms are used to investigate...... practically and politically relevant problems and to illustrate how nonparametric regression methods can be used in applied microeconomic production analysis both in panel data and cross-section data settings. The thesis consists of four papers. The first paper addresses problems of parametric...
The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard
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...... function. However, the a priori specification of a functional form involves the risk of choosing one that is not similar to the “true” but unknown relationship between the regressors and the dependent variable. This problem, known as parametric misspecification, can result in biased parameter estimates...... 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...
Asymptotic estimation theory of multipoint linkage analysis under perfect marker information
Hössjer, Ola
2003-01-01
We consider estimation of a disease susceptibility locus $\\tau$ at a chromosome. With perfect marker data available, the estimator $\\htau_N$ of $\\tau$ based on $N$-pedigrees has a rate of convergence $N^{-1}$ under mild regularity conditions. The limiting distribution is the arg max of a certain compound Poisson process. Our approach is conditional on observed phenotypes, and therefore treats parametric and nonparametric linkage, as well as quantitative trait loci methods within a unified fra...
Nonparametric Bayes modeling for case control studies with many predictors.
Zhou, Jing; Herring, Amy H; Bhattacharya, Anirban; Olshan, Andrew F; Dunson, David B
2016-03-01
It is common in biomedical research to run case-control studies involving high-dimensional predictors, with the main goal being detection of the sparse subset of predictors having a significant association with disease. Usual analyses rely on independent screening, considering each predictor one at a time, or in some cases on logistic regression assuming no interactions. We propose a fundamentally different approach based on a nonparametric Bayesian low rank tensor factorization model for the retrospective likelihood. Our model allows a very flexible structure in characterizing the distribution of multivariate variables as unknown and without any linear assumptions as in logistic regression. Predictors are excluded only if they have no impact on disease risk, either directly or through interactions with other predictors. Hence, we obtain an omnibus approach for screening for important predictors. Computation relies on an efficient Gibbs sampler. The methods are shown to have high power and low false discovery rates in simulation studies, and we consider an application to an epidemiology study of birth defects.
Nonparametric Bayesian drift estimation for multidimensional stochastic differential equations
Gugushvili, S.; Spreij, P.
2014-01-01
We consider nonparametric Bayesian estimation of the drift coefficient of a multidimensional stochastic differential equation from discrete-time observations on the solution of this equation. Under suitable regularity conditions, we establish posterior consistency in this context.
Homothetic Efficiency and Test Power: A Non-Parametric Approach
J. Heufer (Jan); P. Hjertstrand (Per)
2015-01-01
markdownabstract__Abstract__ We provide a nonparametric revealed preference approach to demand analysis based on homothetic efficiency. Homotheticity is a useful restriction but data rarely satisfies testable conditions. To overcome this we provide a way to estimate homothetic efficiency of
A non-parametric approach to investigating fish population dynamics
National Research Council Canada - National Science Library
Cook, R.M; Fryer, R.J
2001-01-01
.... Using a non-parametric model for the stock-recruitment relationship it is possible to avoid defining specific functions relating recruitment to stock size while also providing a natural framework to model process error...
Genome-wide linkage analysis of inguinal hernia in pigs using affected sib pairs
Directory of Open Access Journals (Sweden)
Taubert Helge
2006-05-01
Full Text Available Abstract Background Inguinal and scrotal hernias are of great concern to pig producers, and lead to poor animal welfare and severe economic loss. Selection against these conditions is highly preferable, but at this time no gene, Quantitative Trait Loci (QTL, or mode of inheritance has been identified in pigs or in any other species. Therefore, a complete genome scan was performed in order to identify genomic regions affecting inguinal and scrotal hernias in pigs. Records from seedstock breeding farms were collected. No clinical examinations were executed on the pigs and there was therefore no distinction between inguinal and scrotal hernias. The genome scan utilised affected sib pairs (ASP, and the data was analysed using both an ASP test based on Non-parametric Linkage (NPL analysis, and a Transmission Disequilibrium Test (TDT. Results Significant QTLs (p Conclusion For the first time in any species, a genome scan has revealed suggestive QTLs for inguinal and scrotal hernias. While this study permitted the detection of chromosomal regions only, it is interesting to note that several promising candidate genes, including INSL3, MIS, and CGRP, are located within the highly significant QTL regions. Further studies are required in order to narrow down the suggestive QTL regions, investigate the candidate genes, and to confirm the suggestive QTLs in other populations. The haplotype associated with inguinal and scrotal hernias may help in achieving selection against the disorder.
From Enclave to Linkage Economies?
DEFF Research Database (Denmark)
Hansen, Michael W.
and local enterprises by themselves will indeed produce linkages, the scope, depth and development impacts of linkages eventually depend on government intervention. Resource-rich African countries’ governments are aware of this and linkage promotion is increasingly becoming a key element...
Nonparametric Bayesian Modeling for Automated Database Schema Matching
Energy Technology Data Exchange (ETDEWEB)
Ferragut, Erik M [ORNL; Laska, Jason A [ORNL
2015-01-01
The problem of merging databases arises in many government and commercial applications. Schema matching, a common first step, identifies equivalent fields between databases. We introduce a schema matching framework that builds nonparametric Bayesian models for each field and compares them by computing the probability that a single model could have generated both fields. Our experiments show that our method is more accurate and faster than the existing instance-based matching algorithms in part because of the use of nonparametric Bayesian models.
PV power forecast using a nonparametric PV model
Almeida, Marcelo Pinho; Perpiñan Lamigueiro, Oscar; Narvarte Fernández, Luis
2015-01-01
Forecasting the AC power output of a PV plant accurately is important both for plant owners and electric system operators. Two main categories of PV modeling are available: the parametric and the nonparametric. In this paper, a methodology using a nonparametric PV model is proposed, using as inputs several forecasts of meteorological variables from a Numerical Weather Forecast model, and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quant...
CHIN LEE; M. Azali
2010-01-01
The purpose of this study is to examine the potential linkages among ASEAN-5 currencies, in particular the possibility of a Singapore dollar bloc during the pre- and post-crisis periods by using the Johansen multivariate cointegration test and the Granger causality test. Significant nonstationarity and the presence of unit roots were documented for each currency under both study periods. Using ASEAN-4 exchange rates against the Singapore dollar, the Johansen cointegration test showed that the...
Directory of Open Access Journals (Sweden)
Joseph H Lee
2014-01-01
Full Text Available Leukocyte telomere length is believed to measure cellular aging in humans, and short leukocyte telomere length is associated with increased risks of late onset diseases, including cardiovascular disease, dementia, etc. Many studies have shown that leukocyte telomere length is a heritable trait, and several candidate genes have been identified, including TERT, TERC, OBFC1, and CTC1. Unlike most studies that have focused on genetic causes of chronic diseases such as heart disease and diabetes in relation to leukocyte telomere length, the present study examined the genome to identify variants that may contribute to variation in leukocyte telomere length among families with exceptional longevity. From the genome wide association analysis in 4,289 LLFS participants, we identified a novel intergenic SNP rs7680468 located near PAPSS1 and DKK2 on 4q25 (p=4.7E-8. From our linkage analysis, we identified two additional loci with HLOD scores exceeding three, including 4.77 for 17q23.2 and 4.36 for 10q11.21. These two loci harbor a number of novel candidate genes with SNPs, and our gene-wise association analysis identified multiple genes, including DCAF7, POLG2, CEP95, and SMURF2 at 17q23.2; and RASGEF1A, HNRNPF, ANF487, CSTF2T, and PRKG1 at 10q11.21. Among these genes, multiple SNPs were associated with leukocyte telomere length, but the strongest association was observed with one contiguous haplotype in CEP95 and SMURF2. We also show that three previously reported genes – TERC, MYNN, and OBFC1 – were significantly associated with leukocyte telomere length at pempirical smaller than 0.05.
The phenotypic difference discards sib-pair QTL linkage information
Energy Technology Data Exchange (ETDEWEB)
Wright, F.A. [Univ. of California, San Diego, CA (United States)]|[Univ. of Texas, El Paso, TX (United States)
1997-03-01
Kruglyak and Lander provide an important synthesis of methods for (IBD) sib-pair linkage mapping, with an emphasis on the use of complete multipoint inheritance information for each sib pair. These procedures are implemented in the computer program MAPMAKER/SIBS, which performs interval mapping for dichotomous and quantitative traits. The authors present three methods for mapping quantitative trait loci (QTLs): a variant of the commonly used Haseman-Elston regression approach, a maximum-likelihood procedure involving variance components, and a rank-based nonparametric procedure. These approaches and related work use the magnitude of the difference in the sibling phenotype values for each sib pair as the observation for analysis. Linkage is detected if siblings sharing more alleles IBD have similar phenotypes (i.e., a small difference in the phenotype values), while siblings sharing fewer alleles IBD have less similar phenotypes. Such techniques have been used to detect linkage for a number of quantitative traits. However, the exclusive reliance on the phenotypic differences may be due in large part to historical inertia. A likelihood argument is presented here to show that, under certain classical assumptions, the phenotypic differences do not contain the full likelihood information for QTL mapping. Furthermore, considerable gains in power to detect linkage can be achieved with an expanded likelihood model. The development here is related to previous work, which incorporates the full set of phenotypic data using likelihood and robust quasi-likelihood methods. The purpose of this letter is not to endorse a particular approach but to spur research in alternative and perhaps more powerful linkage tests. 17 refs.
Asymptotic theory of nonparametric regression estimates with censored data
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
For regression analysis, some useful information may have been lost when the responses are right censored. To estimate nonparametric functions, several estimates based on censored data have been proposed and their consistency and convergence rates have been studied in literature, but the optimal rates of global convergence have not been obtained yet. Because of the possible information loss, one may think that it is impossible for an estimate based on censored data to achieve the optimal rates of global convergence for nonparametric regression, which were established by Stone based on complete data. This paper constructs a regression spline estimate of a general nonparametric regression function based on right_censored response data, and proves, under some regularity conditions, that this estimate achieves the optimal rates of global convergence for nonparametric regression. Since the parameters for the nonparametric regression estimate have to be chosen based on a data driven criterion, we also obtain the asymptotic optimality of AIC, AICC, GCV, Cp and FPE criteria in the process of selecting the parameters.
Rediscovery of Good-Turing estimators via Bayesian nonparametrics.
Favaro, Stefano; Nipoti, Bernardo; Teh, Yee Whye
2016-03-01
The problem of estimating discovery probabilities originated in the context of statistical ecology, and in recent years it has become popular due to its frequent appearance in challenging applications arising in genetics, bioinformatics, linguistics, designs of experiments, machine learning, etc. A full range of statistical approaches, parametric and nonparametric as well as frequentist and Bayesian, has been proposed for estimating discovery probabilities. In this article, we investigate the relationships between the celebrated Good-Turing approach, which is a frequentist nonparametric approach developed in the 1940s, and a Bayesian nonparametric approach recently introduced in the literature. Specifically, under the assumption of a two parameter Poisson-Dirichlet prior, we show that Bayesian nonparametric estimators of discovery probabilities are asymptotically equivalent, for a large sample size, to suitably smoothed Good-Turing estimators. As a by-product of this result, we introduce and investigate a methodology for deriving exact and asymptotic credible intervals to be associated with the Bayesian nonparametric estimators of discovery probabilities. The proposed methodology is illustrated through a comprehensive simulation study and the analysis of Expressed Sequence Tags data generated by sequencing a benchmark complementary DNA library.
A generalization of Kempe's linkages
Institute of Scientific and Technical Information of China (English)
MAO De-can; LUO Yao-zhi; YOU Zhong
2007-01-01
A new, general type of planar linkages is presented, which extends the classical linkages developed by Kempe consisting of two single-looped kinematic chains of linkages, interconnected by revolute hinges. Together with a locking device, these new linkages have only one degree of freedom (DOF), which makes them ideal for serving as deployable structures for different purposes. Here, we start with a fresh matrix method of analysis for double-loop planar linkages, using 2D transformation matrices and a new symbolic notation. Further inspection for one case of Kempe's linkages is provided. Basing on the inspection, by means of some novel algebraic and geometric techniques, one particularly fascinating solution was found. Physical models were built to show that the derivation in this paper is valid and the new mechanisms are correct.
Non-parametric combination and related permutation tests for neuroimaging.
Winkler, Anderson M; Webster, Matthew A; Brooks, Jonathan C; Tracey, Irene; Smith, Stephen M; Nichols, Thomas E
2016-04-01
In this work, we show how permutation methods can be applied to combination analyses such as those that include multiple imaging modalities, multiple data acquisitions of the same modality, or simply multiple hypotheses on the same data. Using the well-known definition of union-intersection tests and closed testing procedures, we use synchronized permutations to correct for such multiplicity of tests, allowing flexibility to integrate imaging data with different spatial resolutions, surface and/or volume-based representations of the brain, including non-imaging data. For the problem of joint inference, we propose and evaluate a modification of the recently introduced non-parametric combination (NPC) methodology, such that instead of a two-phase algorithm and large data storage requirements, the inference can be performed in a single phase, with reasonable computational demands. The method compares favorably to classical multivariate tests (such as MANCOVA), even when the latter is assessed using permutations. We also evaluate, in the context of permutation tests, various combining methods that have been proposed in the past decades, and identify those that provide the best control over error rate and power across a range of situations. We show that one of these, the method of Tippett, provides a link between correction for the multiplicity of tests and their combination. Finally, we discuss how the correction can solve certain problems of multiple comparisons in one-way ANOVA designs, and how the combination is distinguished from conjunctions, even though both can be assessed using permutation tests. We also provide a common algorithm that accommodates combination and correction.
Zhang, Yanli; Liao, Zetao; Wei, Qiujing; Pan, Yunfeng; Wang, Xinwei; Cao, Shuangyan; Guo, Zishi; Wu, Yuqiong; Rong, Ju; Jin, Ou; Xu, Manlong; Gu, Jieruo
2016-01-01
Objectives To screen susceptibility loci for ankylosing spondylitis (AS) using an affected-only linkage analysis based on high-density single nucleotide polymorphisms (SNPs) in a genome-wide manner. Patients and Methods AS patients from ten families with Cantonese origin of China were enrolled in the study. Blood samples were genotyped using genomic DNA derived from peripheral blood leukocytes by Illumina HumanHap 610-Quad SNP Chip. Genotype data were generated using the Illumina BeadStudio 3.2 software. PLINK package was used to remove non-autosomal SNPs and to further eliminate markers of typing errors. An affected-only linkage analysis was carried out using both non-parametric and parametric linkage analyses, as implemented in MERLIN. Result Seventy-eight AS patients (48 males and 30 females, mean age: 39±16 years) were enrolled in the study. The mean age of onset was 23±10 years and mean duration of disease was 16.7±12.2 years. Iritis (2/76, 2.86%), dactylitis (5/78, 6.41%), hip joint involvement (9/78, 11.54%), peripheral arthritis (22/78, 28.21%), inflammatory back pain (IBP) (69/78, 88.46%) and HLA-B27 positivity (70/78, 89.74%) were observed in these patients. Using non-parameter linkage analysis, we found one susceptibility locus for AS, IBP and HLA-B27 in 6p21 respectively, spanning about 13.5Mb, 20.9Mb and 21.2Mb, respectively No significant results were found in the other clinical trait groups including dactylitis, hip involved and arthritis. The identical susceptibility locus region spanning above 9.44Mb was detected in AS IBP and HLA-B27 by the parametric linkage analysis. Conclusion Our genome-wide SNP linkage analysis in ten families with ankylosing spondylitis suggests a susceptibility locus on 6p21 in AS, which is a risk locus for IBP in AS patients. PMID:27973620
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.
A genome-wide linkage study of bipolar disorder and co-morbid migraine
DEFF Research Database (Denmark)
Oedegaard, K. J.; Greenwood, T. A.; Lunde, Asger
2010-01-01
on chromosome 4q24 for migraine (but not BPAD) with a peak LOD of 2.26. This region has previously been implicated in two independent migraine linkage studies. In additionwe identified a locus on chromosome 20p11 with overlapping elevated LOD scores for both migraine (LOD=1.95) and BPAD (LOD=1.67) phenotypes...... Genetics Initiative wave 4 data set. In this analysis we selected only those families in which at least two members were diagnosed with migraine by a doctor according to patients' reports. Nonparametric linkage analysis performed on 31 families segregating both BPAD and migraine identified a linkage signal...... osome 4 (not co-segregating with BPAD) in a sample of BPAD families with comorbid migraine, and suggest a susceptibility locus on chromosome 20, harboring a gene for the migraine/BPAD phenotype. Together these data suggest that some genes may predispose to both bipolar disorder and migraine....
2008-01-01
Abstract Background The HapMap project aimed to catalog millions of common single nucleotide polymorphisms (SNPs) in the human genome in four major populations, in order to facilitate association studies of complex diseases. To examine the transferability of Han Chinese in Beijing HapMap data to the Southern Han Chinese in Shanghai, we performed comparative analyses between genotypes from over 4,500 SNPs in a 21 Mb region on chromosome 1q21-q25 in 80 unrelated Shanghai Chinese and 45 HapMap C...
Nonparametric estimation of a convex bathtub-shaped hazard function.
Jankowski, Hanna K; Wellner, Jon A
2009-11-01
In this paper, we study the nonparametric maximum likelihood estimator (MLE) of a convex hazard function. We show that the MLE is consistent and converges at a local rate of n(2/5) at points x(0) where the true hazard function is positive and strictly convex. Moreover, we establish the pointwise asymptotic distribution theory of our estimator under these same assumptions. One notable feature of the nonparametric MLE studied here is that no arbitrary choice of tuning parameter (or complicated data-adaptive selection of the tuning parameter) is required.
Non-parametric partitioning of SAR images
Delyon, G.; Galland, F.; Réfrégier, Ph.
2006-09-01
We describe and analyse a generalization of a parametric segmentation technique adapted to Gamma distributed SAR images to a simple non parametric noise model. The partition is obtained by minimizing the stochastic complexity of a quantized version on Q levels of the SAR image and lead to a criterion without parameters to be tuned by the user. We analyse the reliability of the proposed approach on synthetic images. The quality of the obtained partition will be studied for different possible strategies. In particular, one will discuss the reliability of the proposed optimization procedure. Finally, we will precisely study the performance of the proposed approach in comparison with the statistical parametric technique adapted to Gamma noise. These studies will be led by analyzing the number of misclassified pixels, the standard Hausdorff distance and the number of estimated regions.
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 ...
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...
Clément, K; Dina, C; Basdevant, A; Chastang, N; Pelloux, V; Lahlou, N; Berlan, M; Langin, D; Guy-Grand, B; Froguel, P
1999-02-01
As part of an ongoing search for susceptibility genes in obese families, we performed linkage analyses in 101 French families between qualitative and quantitative traits related to morbid obesity and polymorphisms located in or near 15 candidate genes whose products are involved in body weight regulation. These included cholecystokinin A and B receptors (CCK-AR and CCK-BR), glucagon-like peptide 1 receptor (GLP-1R), the LIM/homeodomain islet-1 gene (Isl-1), the caudal-type homeodomain 3 (CDX-3), the uncoupling protein 1 (UCP-1), the beta3-adrenoceptor (beta3-AR), the fatty acid-binding protein 2 (FABP-2), the hormone-sensitive lipase (HSL), the lipoprotein lipase (LPL), the apoprotein-C2 (apo-C2), the insulin receptor substrate-1 (IRS-1), the peroxisome proliferator-activated receptor-gamma (PPAR-gamma), tumor necrosis factor-alpha (TNF-alpha), and the liver carnitine palmitoyltransferase-1 (CPT-1). Phenotypes related to obesity such as BMI, adult life body weight gain, fasting leptin, insulin, fasting glycerol, and free fatty acids were used for nonparametric sib-pair analyses. A weak indication for linkage was obtained between the Isl-1 locus and obesity status defined by a z score over one SD of BMI (n = 226 sib pairs, pi = 0.54 +/- 0.02, P = 0.03). Moreover, a suggestive indication for linkage was found between the Isl-1 locus and BMI and leptin values (P = 0.001 and 0.0003, respectively) and leptin adjusted for BMI (P = 0.0001). Multipoint analyses for leptin trait with Isl-1 and two flanking markers (D5S418 and D5S407) showed that the logarithm of odds (LOD) score is 1.73, coinciding with the Isl-1 locus. Although marginally positive indications for linkage in subgroups of families were found with IRS-1, CPT-1, and HSL loci, our data suggested that these genes are not major contributors to obesity. Whether an obesity susceptibility gene (Isl-1 itself or another nearby gene) lies on chromosome 5q should be determined by further analyses.
Nonparametric Cointegration Analysis of Fractional Systems With Unknown Integration Orders
DEFF Research Database (Denmark)
Nielsen, Morten Ørregaard
2009-01-01
In this paper a nonparametric variance ratio testing approach is proposed for determining the number of cointegrating relations in fractionally integrated systems. The test statistic is easily calculated without prior knowledge of the integration order of the data, the strength of the cointegrating...
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...... but that this dependence vanishes after 2-3 years....
Influence of test and person characteristics on nonparametric appropriateness measurement
Meijer, Rob R.; Molenaar, Ivo W.; Sijtsma, Klaas
1994-01-01
Appropriateness measurement in nonparametric item response theory modeling is affected by the reliability of the items, the test length, the type of aberrant response behavior, and the percentage of aberrant persons in the group. The percentage of simulees defined a priori as aberrant responders tha
Influence of Test and Person Characteristics on Nonparametric Appropriateness Measurement
Meijer, Rob R; Molenaar, Ivo W; Sijtsma, Klaas
1994-01-01
Appropriateness measurement in nonparametric item response theory modeling is affected by the reliability of the items, the test length, the type of aberrant response behavior, and the percentage of aberrant persons in the group. The percentage of simulees defined a priori as aberrant responders tha
Estimation of Spatial Dynamic Nonparametric Durbin Models with Fixed Effects
Qian, Minghui; Hu, Ridong; Chen, Jianwei
2016-01-01
Spatial panel data models have been widely studied and applied in both scientific and social science disciplines, especially in the analysis of spatial influence. In this paper, we consider the spatial dynamic nonparametric Durbin model (SDNDM) with fixed effects, which takes the nonlinear factors into account base on the spatial dynamic panel…
Uniform Consistency for Nonparametric Estimators in Null Recurrent Time Series
DEFF Research Database (Denmark)
Gao, Jiti; Kanaya, Shin; Li, Degui
2015-01-01
This paper establishes uniform consistency results for nonparametric kernel density and regression estimators when time series regressors concerned are nonstationary null recurrent Markov chains. Under suitable regularity conditions, we derive uniform convergence rates of the estimators. Our...... results can be viewed as a nonstationary extension of some well-known uniform consistency results for stationary time series....
Non-parametric Bayesian inference for inhomogeneous Markov point processes
DEFF Research Database (Denmark)
Berthelsen, Kasper Klitgaard; Møller, Jesper
With reference to a specific data set, we consider how to perform a flexible non-parametric Bayesian analysis of an inhomogeneous point pattern modelled by a Markov point process, with a location dependent first order term and pairwise interaction only. A priori we assume that the first order term...
Investigating the cultural patterns of corruption: A nonparametric analysis
Halkos, George; Tzeremes, Nickolaos
2011-01-01
By using a sample of 77 countries our analysis applies several nonparametric techniques in order to reveal the link between national culture and corruption. Based on Hofstede’s cultural dimensions and the corruption perception index, the results reveal that countries with higher levels of corruption tend to have higher power distance and collectivism values in their society.
Coverage Accuracy of Confidence Intervals in Nonparametric Regression
Institute of Scientific and Technical Information of China (English)
Song-xi Chen; Yong-song Qin
2003-01-01
Point-wise confidence intervals for a nonparametric regression function with random design points are considered. The confidence intervals are those based on the traditional normal approximation and the empirical likelihood. Their coverage accuracy is assessed by developing the Edgeworth expansions for the coverage probabilities. It is shown that the empirical likelihood confidence intervals are Bartlett correctable.
Homothetic Efficiency and Test Power: A Non-Parametric Approach
J. Heufer (Jan); P. Hjertstrand (Per)
2015-01-01
markdownabstract__Abstract__ We provide a nonparametric revealed preference approach to demand analysis based on homothetic efficiency. Homotheticity is a useful restriction but data rarely satisfies testable conditions. To overcome this we provide a way to estimate homothetic efficiency of consump
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...
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.
Directory of Open Access Journals (Sweden)
Akhtar R. Siddique
2000-03-01
Full Text Available This paper develops a filtering-based framework of non-parametric estimation of parameters of a diffusion process from the conditional moments of discrete observations of the process. This method is implemented for interest rate data in the Eurodollar and long term bond markets. The resulting estimates are then used to form non-parametric univariate and bivariate interest rate models and compute prices for the short term Eurodollar interest rate futures options and long term discount bonds. The bivariate model produces prices substantially closer to the market prices. This paper develops a filtering-based framework of non-parametric estimation of parameters of a diffusion process from the conditional moments of discrete observations of the process. This method is implemented for interest rate data in the Eurodollar and long term bond markets. The resulting estimates are then used to form non-parametric univariate and bivariate interest rate models and compute prices for the short term Eurodollar interest rate futures options and long term discount bonds. The bivariate model produces prices substantially closer to the market prices.
Gorla, Elisa; Nagel, Uwe
2010-01-01
In this paper, we give a sufficient condition for a set $\\mathal G$ of polynomials to be a Gr\\"obner basis with respect to a given term-order for the ideal $I$ that it generates. Our criterion depends on the linkage pattern of the ideal $I$ and of the ideal generated by the initial terms of the elements of $\\mathcal G$. We then apply this criterion to ideals generated by minors and pfaffians. More precisely, we consider large families of ideals generated by minors or pfaffians in a matrix or a ladder, where the size of the minors or pfaffians is allowed to vary in different regions of the matrix or the ladder. We use the sufficient condition that we established to prove that the minors or pfaffians form a reduced Gr\\"obner basis for the ideal that they generate, with respect to any diagonal or anti-diagonal term-order. We also show that the corresponding initial ideal is Cohen-Macaulay. Our proof relies on known results in liaison theory, combined with a simple Hilbert function computation. In particular, our...
Comparison of Rank Analysis of Covariance and Nonparametric Randomized Blocks Analysis.
Porter, Andrew C.; McSweeney, Maryellen
The relative power of three possible experimental designs under the condition that data is to be analyzed by nonparametric techniques; the comparison of the power of each nonparametric technique to its parametric analogue; and the comparison of relative powers using nonparametric and parametric techniques are discussed. The three nonparametric…
JLIN: A java based linkage disequilibrium plotter
Directory of Open Access Journals (Sweden)
McCaskie Pamela A
2006-02-01
Full Text Available Abstract Background A great deal of effort and expense are being expended internationally in attempts to detect genetic polymorphisms contributing to susceptibility to complex human disease. Techniques such as Linkage Disequilibrium mapping are being increasingly used to examine and compare markers across increasingly large datasets. Visualisation techniques are becoming essential to analyse the ever-growing volume of data and results available with any given analysis. Results JLIN (Java LINkage disequilibrium plotter is a software package designed for customisable, intuitive visualisation of Linkage Disequilibrium (LD across all common computing platforms. Customisation allows the user to choose particular visualisations, statistical measures and measurement ranges. JLIN also allows the user to export images of the LD visualisation in several common document formats. Conclusion JLIN allows the user to visually compare and contrast the results of a range of statistical measures on the input dataset(s. These measures include the commonly used D' and r2 statistics and empirical p-values. JLIN has a number of unique and novel features that improve on existing LD visualisation tools.
Emergency Linkage Mode of Power Enterprise
Directory of Open Access Journals (Sweden)
Feng Jie
2016-01-01
Full Text Available Power emergency disposal needs take full advantage of the power enterprise within the external emergency power and resources. Based on analyzing and summarizing the relevant experience of domestic and foreign emergency linkage, this paper draws the Emergency Linkage subjects, Emergency Linkage contents, Emergency Linkage level, which are three key elements if power enterprise Emergency Linkage. Emergency Linkage subjects are divided into the two types of inner subjects and the external body; Emergency Linkage contents are in accordance with four phases of prevention, preparedness, response and recovery; Emergency Linkage level is divided into three levels of enterprise headquarter, provincial enterprise and incident unite. Binding power enterprise emergency management practice, this paper studies the internal Emergency Linkage modes (including horizontal mode and vertical mode, external Emergency Linkage mode and comprehensive Emergency Linkage Mode of power enterprise based on Fishbone Diagram and Process Management Technology.
STAKEHOLDER LINKAGES FOR SUSTAINABLE LAND ...
African Journals Online (AJOL)
Osondu
stakeholder interactions for SLM in the study areas. Key words: Stakeholders; farmer-expert linkages; resource management; Ethiopia ... management practices in many parts of Africa. Farmers .... chosen with consideration of distance to the.
LINKAGES FOR QUADRUPED BIO-ROBOT WALKING
Directory of Open Access Journals (Sweden)
Ovidiu ANTONESCU
2015-12-01
Full Text Available This paper analyses the Jansen mechanism. It then presents a few pictures of a mobile quadruped robot, which will help to describe how the robot moves. We take into consideration the kinematic scheme of the spatial mechanism with bars (spatial linkage, which is used for each of the four robot legs. Each leg mechanism is driven by two rotate brushless actuators that include a spur gear low-ratio transmission. By means of analyzing the kinematic scheme, the spatial mechanism mobility that operates in both horizontal and vertical plane is calculated
Directory of Open Access Journals (Sweden)
Kühnast, Corinna
2008-04-01
Full Text Available Background: Although non-normal data are widespread in biomedical research, parametric tests unnecessarily predominate in statistical analyses. Methods: We surveyed five biomedical journals and – for all studies which contain at least the unpaired t-test or the non-parametric Wilcoxon-Mann-Whitney test – investigated the relationship between the choice of a statistical test and other variables such as type of journal, sample size, randomization, sponsoring etc. Results: The non-parametric Wilcoxon-Mann-Whitney was used in 30% of the studies. In a multivariable logistic regression the type of journal, the test object, the scale of measurement and the statistical software were significant. The non-parametric test was more common in case of non-continuous data, in high-impact journals, in studies in humans, and when the statistical software is specified, in particular when SPSS was used.
Nonparametric inference procedures for multistate life table analysis.
Dow, M M
1985-01-01
Recent generalizations of the classical single state life table procedures to the multistate case provide the means to analyze simultaneously the mobility and mortality experience of 1 or more cohorts. This paper examines fairly general nonparametric combinatorial matrix procedures, known as quadratic assignment, as an analysis technic of various transitional patterns commonly generated by cohorts over the life cycle course. To some degree, the output from a multistate life table analysis suggests inference procedures. In his discussion of multstate life table construction features, the author focuses on the matrix formulation of the problem. He then presents several examples of the proposed nonparametric procedures. Data for the mobility and life expectancies at birth matrices come from the 458 member Cayo Santiago rhesus monkey colony. The author's matrix combinatorial approach to hypotheses testing may prove to be a useful inferential strategy in several multidimensional demographic areas.
Non-parametric estimation of Fisher information from real data
Shemesh, Omri Har; Miñano, Borja; Hoekstra, Alfons G; Sloot, Peter M A
2015-01-01
The Fisher Information matrix is a widely used measure for applications ranging from statistical inference, information geometry, experiment design, to the study of criticality in biological systems. Yet there is no commonly accepted non-parametric algorithm to estimate it from real data. In this rapid communication we show how to accurately estimate the Fisher information in a nonparametric way. We also develop a numerical procedure to minimize the errors by choosing the interval of the finite difference scheme necessary to compute the derivatives in the definition of the Fisher information. Our method uses the recently published "Density Estimation using Field Theory" algorithm to compute the probability density functions for continuous densities. We use the Fisher information of the normal distribution to validate our method and as an example we compute the temperature component of the Fisher Information Matrix in the two dimensional Ising model and show that it obeys the expected relation to the heat capa...
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.
Combined parametric-nonparametric identification of block-oriented systems
Mzyk, Grzegorz
2014-01-01
This book considers a problem of block-oriented nonlinear dynamic system identification in the presence of random disturbances. This class of systems includes various interconnections of linear dynamic blocks and static nonlinear elements, e.g., Hammerstein system, Wiener system, Wiener-Hammerstein ("sandwich") system and additive NARMAX systems with feedback. Interconnecting signals are not accessible for measurement. The combined parametric-nonparametric algorithms, proposed in the book, can be selected dependently on the prior knowledge of the system and signals. Most of them are based on the decomposition of the complex system identification task into simpler local sub-problems by using non-parametric (kernel or orthogonal) regression estimation. In the parametric stage, the generalized least squares or the instrumental variables technique is commonly applied to cope with correlated excitations. Limit properties of the algorithms have been shown analytically and illustrated in simple experiments.
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......A two-step estimation method of stochastic volatility models is proposed: In the first step, we nonparametrically estimate the (unobserved) instantaneous volatility process. In the second step, standard estimation methods for fully observed diffusion processes are employed, but with the filtered...... 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...
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.
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
-Douglas function nor the Translog function are consistent with the “true” relationship between the inputs and the output in our data set. We solve this problem by using non-parametric regression. This approach delivers reasonable results, which are on average not too different from the results of the parametric......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...
Right-Censored Nonparametric Regression: A Comparative Simulation Study
Directory of Open Access Journals (Sweden)
Dursun Aydın
2016-11-01
Full Text Available This paper introduces the operating of the selection criteria for right-censored nonparametric regression using smoothing spline. In order to transform the response variable into a variable that contains the right-censorship, we used the KaplanMeier weights proposed by [1], and [2]. The major problem in smoothing spline method is to determine a smoothing parameter to obtain nonparametric estimates of the regression function. In this study, the mentioned parameter is chosen based on censored data by means of the criteria such as improved Akaike information criterion (AICc, Bayesian (or Schwarz information criterion (BIC and generalized crossvalidation (GCV. For this purpose, a Monte-Carlo simulation study is carried out to illustrate which selection criterion gives the best estimation for censored data.
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...
Poverty and life cycle effects: A nonparametric analysis for Germany
Stich, Andreas
1996-01-01
Most empirical studies on poverty consider the extent of poverty either for the entire society or for separate groups like elderly people.However, these papers do not show what the situation looks like for persons of a certain age. In this paper poverty measures depending on age are derived using the joint density of income and age. The density is nonparametrically estimated by weighted Gaussian kernel density estimation. Applying the conditional density of income to several poverty measures ...
Nonparametric estimation of Fisher information from real data
Har-Shemesh, Omri; Quax, Rick; Miñano, Borja; Hoekstra, Alfons G.; Sloot, Peter M. A.
2016-02-01
The Fisher information matrix (FIM) is a widely used measure for applications including statistical inference, information geometry, experiment design, and the study of criticality in biological systems. The FIM is defined for a parametric family of probability distributions and its estimation from data follows one of two paths: either the distribution is assumed to be known and the parameters are estimated from the data or the parameters are known and the distribution is estimated from the data. We consider the latter case which is applicable, for example, to experiments where the parameters are controlled by the experimenter and a complicated relation exists between the input parameters and the resulting distribution of the data. Since we assume that the distribution is unknown, we use a nonparametric density estimation on the data and then compute the FIM directly from that estimate using a finite-difference approximation to estimate the derivatives in its definition. The accuracy of the estimate depends on both the method of nonparametric estimation and the difference Δ θ between the densities used in the finite-difference formula. We develop an approach for choosing the optimal parameter difference Δ θ based on large deviations theory and compare two nonparametric density estimation methods, the Gaussian kernel density estimator and a novel density estimation using field theory method. We also compare these two methods to a recently published approach that circumvents the need for density estimation by estimating a nonparametric f divergence and using it to approximate the FIM. We use the Fisher information of the normal distribution to validate our method and as a more involved example we compute the temperature component of the FIM in the two-dimensional Ising model and show that it obeys the expected relation to the heat capacity and therefore peaks at the phase transition at the correct critical temperature.
ANALYSIS OF TIED DATA: AN ALTERNATIVE NON-PARAMETRIC APPROACH
Directory of Open Access Journals (Sweden)
I. C. A. OYEKA
2012-02-01
Full Text Available This paper presents a non-parametric statistical method of analyzing two-sample data that makes provision for the possibility of ties in the data. A test statistic is developed and shown to be free of the effect of any possible ties in the data. An illustrative example is provided and the method is shown to compare favourably with its competitor; the Mann-Whitney test and is more powerful than the latter when there are ties.
Nonparametric test for detecting change in distribution with panel data
Pommeret, Denys; Ghattas, Badih
2011-01-01
This paper considers the problem of comparing two processes with panel data. A nonparametric test is proposed for detecting a monotone change in the link between the two process distributions. The test statistic is of CUSUM type, based on the empirical distribution functions. The asymptotic distribution of the proposed statistic is derived and its finite sample property is examined by bootstrap procedures through Monte Carlo simulations.
Fusion of Hard and Soft Information in Nonparametric Density Estimation
2015-06-10
estimation exploiting, in concert, hard and soft information. Although our development, theoretical and numerical, makes no distinction based on sample...Fusion of Hard and Soft Information in Nonparametric Density Estimation∗ Johannes O. Royset Roger J-B Wets Department of Operations Research...univariate density estimation in situations when the sample ( hard information) is supplemented by “soft” information about the random phenomenon. These
Nonparametric estimation for hazard rate monotonously decreasing system
Institute of Scientific and Technical Information of China (English)
Han Fengyan; Li Weisong
2005-01-01
Estimation of density and hazard rate is very important to the reliability analysis of a system. In order to estimate the density and hazard rate of a hazard rate monotonously decreasing system, a new nonparametric estimator is put forward. The estimator is based on the kernel function method and optimum algorithm. Numerical experiment shows that the method is accurate enough and can be used in many cases.
Non-parametric versus parametric methods in environmental sciences
Directory of Open Access Journals (Sweden)
Muhammad Riaz
2016-01-01
Full Text Available This current report intends to highlight the importance of considering background assumptions required for the analysis of real datasets in different disciplines. We will provide comparative discussion of parametric methods (that depends on distributional assumptions (like normality relative to non-parametric methods (that are free from many distributional assumptions. We have chosen a real dataset from environmental sciences (one of the application areas. The findings may be extended to the other disciplines following the same spirit.
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 ...... by random genetic drift. The implications for future gene mapping studies are discussed....
Linkage disequilibrium and demographic history of the isolated population of the Faroe Islands
DEFF Research Database (Denmark)
Jorgensen, T.H.; Degn, B.; Wang, A.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 ...
Lack of reproducibility of linkage results in serially measured blood pressure data
Patel, [No Value; Celedon, JC; Weiss, ST; Palmer, LJ
2003-01-01
Background: Using the longitudinal Framingham Heart Study data on blood pressure, we analyzed the reproducibility of linkage measures from serial cross-sectional surveys of a defined population by performing genome-wide model-free linkage analyses to systolic blood pressure (SBP) and history of hype
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 ...
Crainiceanu, Ciprian M; Caffo, Brian S; Di, Chong-Zhi; Punjabi, Naresh M
2009-06-01
We introduce methods for signal and associated variability estimation based on hierarchical nonparametric smoothing with application to the Sleep Heart Health Study (SHHS). SHHS is the largest electroencephalographic (EEG) collection of sleep-related data, which contains, at each visit, two quasi-continuous EEG signals for each subject. The signal features extracted from EEG data are then used in second level analyses to investigate the relation between health, behavioral, or biometric outcomes and sleep. Using subject specific signals estimated with known variability in a second level regression becomes a nonstandard measurement error problem. We propose and implement methods that take into account cross-sectional and longitudinal measurement error. The research presented here forms the basis for EEG signal processing for the SHHS.
The application of non-parametric statistical techniques to an ALARA programme.
Moon, J H; Cho, Y H; Kang, C S
2001-01-01
For the cost-effective reduction of occupational radiation dose (ORD) at nuclear power plants, it is necessary to identify what are the processes of repetitive high ORD during maintenance and repair operations. To identify the processes, the point values such as mean and median are generally used, but they sometimes lead to misjudgment since they cannot show other important characteristics such as dose distributions and frequencies of radiation jobs. As an alternative, the non-parametric analysis method is proposed, which effectively identifies the processes of repetitive high ORD. As a case study, the method is applied to ORD data of maintenance and repair processes at Kori Units 3 and 4 that are pressurised water reactors with 950 MWe capacity and have been operating since 1986 and 1987 respectively, in Korea and the method is demonstrated to be an efficient way of analysing the data.
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
a Multivariate Downscaling Model for Nonparametric Simulation of Daily Flows
Molina, J. M.; Ramirez, J. A.; Raff, D. A.
2011-12-01
A multivariate, stochastic nonparametric framework for stepwise disaggregation of seasonal runoff volumes to daily streamflow is presented. The downscaling process is conditional on volumes of spring runoff and large-scale ocean-atmosphere teleconnections and includes a two-level cascade scheme: seasonal-to-monthly disaggregation first followed by monthly-to-daily disaggregation. The non-parametric and assumption-free character of the framework allows consideration of the random nature and nonlinearities of daily flows, which parametric models are unable to account for adequately. This paper examines statistical links between decadal/interannual climatic variations in the Pacific Ocean and hydrologic variability in US northwest region, and includes a periodicity analysis of climate patterns to detect coherences of their cyclic behavior in the frequency domain. We explore the use of such relationships and selected signals (e.g., north Pacific gyre oscillation, southern oscillation, and Pacific decadal oscillation indices, NPGO, SOI and PDO, respectively) in the proposed data-driven framework by means of a combinatorial approach with the aim of simulating improved streamflow sequences when compared with disaggregated series generated from flows alone. A nearest neighbor time series bootstrapping approach is integrated with principal component analysis to resample from the empirical multivariate distribution. A volume-dependent scaling transformation is implemented to guarantee the summability condition. In addition, we present a new and simple algorithm, based on nonparametric resampling, that overcomes the common limitation of lack of preservation of historical correlation between daily flows across months. The downscaling framework presented here is parsimonious in parameters and model assumptions, does not generate negative values, and produces synthetic series that are statistically indistinguishable from the observations. We present evidence showing that both
Garcia-Gámez, E; Gutiérrez-Gil, B; Suarez-Vega, A; de la Fuente, L F; Arranz, J J
2013-09-01
In this study, 2 procedures were used to analyze a data set from a whole-genome scan, one based on linkage analysis information and the other combing linkage disequilibrium and linkage analysis (LDLA), to determine the quantitative trait loci (QTL) influencing milk production traits in sheep. A total of 1,696 animals from 16 half-sib families were genotyped using the OvineSNP50 BeadChip (Illumina Inc., San Diego, CA) and analysis was performed using a daughter design. Moreover, the same data set has been previously investigated through a genome-wide association (GWA) analysis and a comparison of results from the 3 methods has been possible. The linkage analysis and LDLA methodologies yielded different results, although some significantly associated regions were common to both procedures. The linkage analysis detected 3 overlapping genome-wise significant QTL on sheep chromosome (OAR) 2 influencing milk yield, protein yield, and fat yield, whereas 34 genome-wise significant QTL regions were detected using the LDLA approach. The most significant QTL for protein and fat percentages was detected on OAR3, which was reported in a previous GWA analysis. Both the linkage analysis and LDLA identified many other chromosome-wise significant associations across different sheep autosomes. Additional analyses were performed on OAR2 and OAR3 to determine the possible causality of the most significant polymorphisms identified for these genetic effects by the previously reported GWA analysis. For OAR3, the analyses demonstrated additional genetic proof of the causality previously suggested by our group for a single nucleotide polymorphism located in the α-lactalbumin gene (LALBA). In summary, although the results shown here suggest that in commercial dairy populations, the LDLA method exhibits a higher efficiency to map QTL than the simple linkage analysis or linkage disequilibrium methods, we believe that comparing the 3 analysis methods is the best approach to obtain a global
Panel data nonparametric estimation of production risk and risk preferences
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
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...... 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...
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
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
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
Testing for a constant coefficient of variation in nonparametric regression
Dette, Holger; Marchlewski, Mareen; Wagener, Jens
2010-01-01
In the common nonparametric regression model Y_i=m(X_i)+sigma(X_i)epsilon_i we consider the problem of testing the hypothesis that the coefficient of the scale and location function is constant. The test is based on a comparison of the observations Y_i=\\hat{sigma}(X_i) with their mean by a smoothed empirical process, where \\hat{sigma} denotes the local linear estimate of the scale function. We show weak convergence of a centered version of this process to a Gaussian process under the null ...
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...
Phylogenetically resolving epidemiologic linkage.
Romero-Severson, Ethan O; Bulla, Ingo; Leitner, Thomas
2016-03-08
Although the use of phylogenetic trees in epidemiological investigations has become commonplace, their epidemiological interpretation has not been systematically evaluated. Here, we use an HIV-1 within-host coalescent model to probabilistically evaluate transmission histories of two epidemiologically linked hosts. Previous critique of phylogenetic reconstruction has claimed that direction of transmission is difficult to infer, and that the existence of unsampled intermediary links or common sources can never be excluded. The phylogenetic relationship between the HIV populations of epidemiologically linked hosts can be classified into six types of trees, based on cladistic relationships and whether the reconstruction is consistent with the true transmission history or not. We show that the direction of transmission and whether unsampled intermediary links or common sources existed make very different predictions about expected phylogenetic relationships: (i) Direction of transmission can often be established when paraphyly exists, (ii) intermediary links can be excluded when multiple lineages were transmitted, and (iii) when the sampled individuals' HIV populations both are monophyletic a common source was likely the origin. Inconsistent results, suggesting the wrong transmission direction, were generally rare. In addition, the expected tree topology also depends on the number of transmitted lineages, the sample size, the time of the sample relative to transmission, and how fast the diversity increases after infection. Typically, 20 or more sequences per subject give robust results. We confirm our theoretical evaluations with analyses of real transmission histories and discuss how our findings should aid in interpreting phylogenetic results.
Dubin's Minimal Linkage Construct Revisited.
Rogers, Donald P.
This paper contains a theoretical analysis and empirical study that support the major premise of Robert Dubin's minimal-linkage construct-that restricting communication links increases organizational stability. The theoretical analysis shows that fewer communication links are associated with less uncertainty, more redundancy, and greater…
Constructing dense genetic linkage maps
Jansen, J.; Jong, de A.G.; Ooijen, van J.W.
2001-01-01
This paper describes a novel combination of techniques for the construction of dense genetic linkage maps. The construction of such maps is hampered by the occurrence of even small proportions of typing errors. Simulated annealing is used to obtain the best map according to the optimality criterion:
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...
Using Mathematica to build Non-parametric Statistical Tables
Directory of Open Access Journals (Sweden)
Gloria Perez Sainz de Rozas
2003-01-01
Full Text Available In this paper, I present computational procedures to obtian statistical tables. The tables of the asymptotic distribution and the exact distribution of Kolmogorov-Smirnov statistic Dn for one population, the table of the distribution of the runs R, the table of the distribution of Wilcoxon signed-rank statistic W+ and the table of the distribution of Mann-Whitney statistic Ux using Mathematica, Version 3.9 under Window98. I think that it is an interesting cuestion because many statistical packages give the asymptotic significance level in the statistical tests and with these porcedures one can easily calculate the exact significance levels and the left-tail and right-tail probabilities with non-parametric distributions. I have used mathematica to make these calculations because one can use symbolic language to solve recursion relations. It's very easy to generate the format of the tables, and it's possible to obtain any table of the mentioned non-parametric distributions with any precision, not only with the standard parameters more used in Statistics, and without transcription mistakes. Furthermore, using similar procedures, we can generate tables for the following distribution functions: Binomial, Poisson, Hypergeometric, Normal, x2 Chi-Square, T-Student, F-Snedecor, Geometric, Gamma and Beta.
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...
Non-parametric Morphologies of Mergers in the Illustris Simulation
Bignone, Lucas A; Sillero, Emanuel; Pedrosa, Susana E; Pellizza, Leonardo J; Lambas, Diego G
2016-01-01
We study non-parametric morphologies of mergers events in a cosmological context, using the Illustris project. We produce mock g-band images comparable to observational surveys from the publicly available Illustris simulation idealized mock images at $z=0$. We then measure non parametric indicators: asymmetry, Gini, $M_{20}$, clumpiness and concentration for a set of galaxies with $M_* >10^{10}$ M$_\\odot$. We correlate these automatic statistics with the recent merger history of galaxies and with the presence of close companions. Our main contribution is to assess in a cosmological framework, the empirically derived non-parametric demarcation line and average time-scales used to determine the merger rate observationally. We found that 98 per cent of galaxies above the demarcation line have a close companion or have experienced a recent merger event. On average, merger signatures obtained from the $G-M_{20}$ criteria anticorrelate clearly with the elapsing time to the last merger event. We also find that the a...
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.
Stochastic Earthquake Rupture Modeling Using Nonparametric Co-Regionalization
Lee, Kyungbook; Song, Seok Goo
2016-10-01
Accurate predictions of the intensity and variability of ground motions are essential in simulation-based seismic hazard assessment. Advanced simulation-based ground motion prediction methods have been proposed to complement the empirical approach, which suffers from the lack of observed ground motion data, especially in the near-source region for large events. It is important to quantify the variability of the earthquake rupture process for future events and to produce a number of rupture scenario models to capture the variability in simulation-based ground motion predictions. In this study, we improved the previously developed stochastic earthquake rupture modeling method by applying the nonparametric co-regionalization, which was proposed in geostatistics, to the correlation models estimated from dynamically derived earthquake rupture models. The nonparametric approach adopted in this study is computationally efficient and, therefore, enables us to simulate numerous rupture scenarios, including large events (M > 7.0). It also gives us an opportunity to check the shape of true input correlation models in stochastic modeling after being deformed for permissibility. We expect that this type of modeling will improve our ability to simulate a wide range of rupture scenario models and thereby predict ground motions and perform seismic hazard assessment more accurately.
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
2012-01-01
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-Douglas a......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...... to estimate production functions without the specification of a functional form. Therefore, they avoid possible misspecification errors due to the use of an unsuitable functional form. In this paper, we use parametric and non-parametric methods to identify the optimal size of Polish crop farms...
Bayesian nonparametric centered random effects models with variable selection.
Yang, Mingan
2013-03-01
In a linear mixed effects model, it is common practice to assume that the random effects follow a parametric distribution such as a normal distribution with mean zero. However, in the case of variable selection, substantial violation of the normality assumption can potentially impact the subset selection and result in poor interpretation and even incorrect results. In nonparametric random effects models, the random effects generally have a nonzero mean, which causes an identifiability problem for the fixed effects that are paired with the random effects. In this article, we focus on a Bayesian method for variable selection. We characterize the subject-specific random effects nonparametrically with a Dirichlet process and resolve the bias simultaneously. In particular, we propose flexible modeling of the conditional distribution of the random effects with changes across the predictor space. The approach is implemented using a stochastic search Gibbs sampler to identify subsets of fixed effects and random effects to be included in the model. Simulations are provided to evaluate and compare the performance of our approach to the existing ones. We then apply the new approach to a real data example, cross-country and interlaboratory rodent uterotrophic bioassay.
Computing Economies of Scope Using Robust Partial Frontier Nonparametric Methods
Directory of Open Access Journals (Sweden)
Pedro Carvalho
2016-03-01
Full Text Available This paper proposes a methodology to examine economies of scope using the recent order-α nonparametric method. It allows us to investigate economies of scope by comparing the efficient order-α frontiers of firms that produce two or more goods with the efficient order-α frontiers of firms that produce only one good. To accomplish this, and because the order-α frontiers are irregular, we suggest to linearize them by the DEA estimator. The proposed methodology uses partial frontier nonparametric methods that are more robust than the traditional full frontier methods. By using a sample of 67 Portuguese water utilities for the period 2002–2008 and, also, a simulated sample, we prove the usefulness of the approach adopted and show that if only the full frontier methods were used, they would lead to different results. We found evidence of economies of scope in the provision of water supply and wastewater services simultaneously by water utilities in Portugal.
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.
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...... estimator. A simulation study that serves two purposes is provided. First, it illustrates in details how to implement our proposed nonparametric estimator. Secondly, it facilitates a comparison of the nonparametric estimator to a parametric counterpart based on the estimator of Lu and Liang (2008...
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.
A genetic linkage map and comparative mapping of the prairie vole (Microtus ochrogaster genome
Directory of Open Access Journals (Sweden)
Young Larry J
2011-07-01
Full Text Available Abstract Background The prairie vole (Microtus ochrogaster is an emerging rodent model for investigating the genetics, evolution and molecular mechanisms of social behavior. Though a karyotype for the prairie vole has been reported and low-resolution comparative cytogenetic analyses have been done in this species, other basic genetic resources for this species, such as a genetic linkage map, are lacking. Results Here we report the construction of a genome-wide linkage map of the prairie vole. The linkage map consists of 406 markers that are spaced on average every 7 Mb and span an estimated ~90% of the genome. The sex average length of the linkage map is 1707 cM, which, like other Muroid rodent linkage maps, is on the lower end of the length distribution of linkage maps reported to date for placental mammals. Linkage groups were assigned to 19 out of the 26 prairie vole autosomes as well as the X chromosome. Comparative analyses of the prairie vole linkage map based on the location of 387 Type I markers identified 61 large blocks of synteny with the mouse genome. In addition, the results of the comparative analyses revealed a potential elevated rate of inversions in the prairie vole lineage compared to the laboratory mouse and rat. Conclusions A genetic linkage map of the prairie vole has been constructed and represents the fourth genome-wide high-resolution linkage map reported for Muroid rodents and the first for a member of the Arvicolinae sub-family. This resource will advance studies designed to dissect the genetic basis of a variety of social behaviors and other traits in the prairie vole as well as our understanding of genome evolution in the genus Microtus.
Robust Depth-Weighted Wavelet for Nonparametric Regression Models
Institute of Scientific and Technical Information of China (English)
Lu LIN
2005-01-01
In the nonpaxametric regression models, the original regression estimators including kernel estimator, Fourier series estimator and wavelet estimator are always constructed by the weighted sum of data, and the weights depend only on the distance between the design points and estimation points. As a result these estimators are not robust to the perturbations in data. In order to avoid this problem, a new nonparametric regression model, called the depth-weighted regression model, is introduced and then the depth-weighted wavelet estimation is defined. The new estimation is robust to the perturbations in data, which attains very high breakdown value close to 1/2. On the other hand, some asymptotic behaviours such as asymptotic normality are obtained. Some simulations illustrate that the proposed wavelet estimator is more robust than the original wavelet estimator and, as a price to pay for the robustness, the new method is slightly less efficient than the original method.
Nonparametric Bayesian inference of the microcanonical stochastic block model
Peixoto, Tiago P
2016-01-01
A principled approach to characterize the hidden modular structure of networks is to formulate generative models, and then infer their parameters from data. When the desired structure is composed of modules or "communities", a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the structure is imposed via hard constraints. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: 1. Deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, that not only remove limitations that seriously degrade the inference on large networks, but also reveal s...
A Non-Parametric Spatial Independence Test Using Symbolic Entropy
Directory of Open Access Journals (Sweden)
López Hernández, Fernando
2008-01-01
Full Text Available In the present paper, we construct a new, simple, consistent and powerful test forspatial independence, called the SG test, by using symbolic dynamics and symbolic entropyas a measure of spatial dependence. We also give a standard asymptotic distribution of anaffine transformation of the symbolic entropy under the null hypothesis of independencein the spatial process. The test statistic and its standard limit distribution, with theproposed symbolization, are invariant to any monotonuous transformation of the data.The test applies to discrete or continuous distributions. Given that the test is based onentropy measures, it avoids smoothed nonparametric estimation. We include a MonteCarlo study of our test, together with the well-known Moran’s I, the SBDS (de Graaffet al, 2001 and (Brett and Pinkse, 1997 non parametric test, in order to illustrate ourapproach.
Analyzing single-molecule time series via nonparametric Bayesian inference.
Hines, Keegan E; Bankston, John R; Aldrich, Richard W
2015-02-03
The ability to measure the properties of proteins at the single-molecule level offers an unparalleled glimpse into biological systems at the molecular scale. The interpretation of single-molecule time series has often been rooted in statistical mechanics and the theory of Markov processes. While existing analysis methods have been useful, they are not without significant limitations including problems of model selection and parameter nonidentifiability. To address these challenges, we introduce the use of nonparametric Bayesian inference for the analysis of single-molecule time series. These methods provide a flexible way to extract structure from data instead of assuming models beforehand. We demonstrate these methods with applications to several diverse settings in single-molecule biophysics. This approach provides a well-constrained and rigorously grounded method for determining the number of biophysical states underlying single-molecule data. Copyright © 2015 Biophysical Society. Published by Elsevier Inc. All rights reserved.
Analyzing multiple spike trains with nonparametric Granger causality.
Nedungadi, Aatira G; Rangarajan, Govindan; Jain, Neeraj; Ding, Mingzhou
2009-08-01
Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons simultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.
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...
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......-Douglas or the Translog production function is used. However, the specification of a functional form for the production function involves the risk of specifying a functional form that is not similar to the “true” relationship between the inputs and the output. This misspecification might result in biased estimation...... 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...
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.
Curve registration by nonparametric goodness-of-fit testing
Dalalyan, Arnak
2011-01-01
The problem of curve registration appears in many different areas of applications ranging from neuroscience to road traffic modeling. In the present work, we propose a nonparametric testing framework in which we develop a generalized likelihood ratio test to perform curve registration. We first prove that, under the null hypothesis, the resulting test statistic is asymptotically distributed as a chi-squared random variable. This result, often referred to as Wilks' phenomenon, provides a natural threshold for the test of a prescribed asymptotic significance level and a natural measure of lack-of-fit in terms of the p-value of the chi squared test. We also prove that the proposed test is consistent, i.e., its power is asymptotically equal to 1. Some numerical experiments on synthetic datasets are reported as well.
Nonparametric forecasting of low-dimensional dynamical systems.
Berry, Tyrus; Giannakis, Dimitrios; Harlim, John
2015-03-01
This paper presents a nonparametric modeling approach for forecasting stochastic dynamical systems on low-dimensional manifolds. The key idea is to represent the discrete shift maps on a smooth basis which can be obtained by the diffusion maps algorithm. In the limit of large data, this approach converges to a Galerkin projection of the semigroup solution to the underlying dynamics on a basis adapted to the invariant measure. This approach allows one to quantify uncertainties (in fact, evolve the probability distribution) for nontrivial dynamical systems with equation-free modeling. We verify our approach on various examples, ranging from an inhomogeneous anisotropic stochastic differential equation on a torus, the chaotic Lorenz three-dimensional model, and the Niño-3.4 data set which is used as a proxy of the El Niño Southern Oscillation.
Nonparametric Model of Smooth Muscle Force Production During Electrical Stimulation.
Cole, Marc; Eikenberry, Steffen; Kato, Takahide; Sandler, Roman A; Yamashiro, Stanley M; Marmarelis, Vasilis Z
2017-03-01
A nonparametric model of smooth muscle tension response to electrical stimulation was estimated using the Laguerre expansion technique of nonlinear system kernel estimation. The experimental data consisted of force responses of smooth muscle to energy-matched alternating single pulse and burst current stimuli. The burst stimuli led to at least a 10-fold increase in peak force in smooth muscle from Mytilus edulis, despite the constant energy constraint. A linear model did not fit the data. However, a second-order model fit the data accurately, so the higher-order models were not required to fit the data. Results showed that smooth muscle force response is not linearly related to the stimulation power.
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.
Indoor Positioning Using Nonparametric Belief Propagation Based on Spanning Trees
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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.
Revealing components of the galaxy population through nonparametric techniques
Bamford, Steven P; Nichol, Robert C; Miller, Christopher J; Wasserman, Larry; Genovese, Christopher R; Freeman, Peter E
2008-01-01
The distributions of galaxy properties vary with environment, and are often multimodal, suggesting that the galaxy population may be a combination of multiple components. The behaviour of these components versus environment holds details about the processes of galaxy development. To release this information we apply a novel, nonparametric statistical technique, identifying four components present in the distribution of galaxy H$\\alpha$ emission-line equivalent-widths. We interpret these components as passive, star-forming, and two varieties of active galactic nuclei. Independent of this interpretation, the properties of each component are remarkably constant as a function of environment. Only their relative proportions display substantial variation. The galaxy population thus appears to comprise distinct components which are individually independent of environment, with galaxies rapidly transitioning between components as they move into denser environments.
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...... 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...... of stochasticity associated with Lithuanian family farm performance. The former technique showed that the farms differed in terms of the mean values and variance of the efficiency scores over time with some clear patterns prevailing throughout the whole research period. The fuzzy Free Disposal Hull showed...
Binary Classifier Calibration Using a Bayesian Non-Parametric Approach.
Naeini, Mahdi Pakdaman; Cooper, Gregory F; Hauskrecht, Milos
Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in Data mining. This paper presents two new non-parametric methods for calibrating outputs of binary classification models: a method based on the Bayes optimal selection and a method based on the Bayesian model averaging. The advantage of these methods is that they are independent of the algorithm used to learn a predictive model, and they can be applied in a post-processing step, after the model is learned. This makes them applicable to a wide variety of machine learning models and methods. These calibration methods, as well as other methods, are tested on a variety of datasets in terms of both discrimination and calibration performance. The results show the methods either outperform or are comparable in performance to the state-of-the-art calibration methods.
Parametric or nonparametric? A parametricness index for model selection
Liu, Wei; 10.1214/11-AOS899
2012-01-01
In model selection literature, two classes of criteria perform well asymptotically in different situations: Bayesian information criterion (BIC) (as a representative) is consistent in selection when the true model is finite dimensional (parametric scenario); Akaike's information criterion (AIC) performs well in an asymptotic efficiency when the true model is infinite dimensional (nonparametric scenario). But there is little work that addresses if it is possible and how to detect the situation that a specific model selection problem is in. In this work, we differentiate the two scenarios theoretically under some conditions. We develop a measure, parametricness index (PI), to assess whether a model selected by a potentially consistent procedure can be practically treated as the true model, which also hints on AIC or BIC is better suited for the data for the goal of estimating the regression function. A consequence is that by switching between AIC and BIC based on the PI, the resulting regression estimator is si...
Nonparametric reconstruction of the Om diagnostic to test LCDM
Escamilla-Rivera, Celia
2015-01-01
Cosmic acceleration is usually related with the unknown dark energy, which equation of state, w(z), is constrained and numerically confronted with independent astrophysical data. In order to make a diagnostic of w(z), the introduction of a null test of dark energy can be done using a diagnostic function of redshift, Om. In this work we present a nonparametric reconstruction of this diagnostic using the so-called Loess-Simex factory to test the concordance model with the advantage that this approach offers an alternative way to relax the use of priors and find a possible 'w' that reliably describe the data with no previous knowledge of a cosmological model. Our results demonstrate that the method applied to the dynamical Om diagnostic finds a preference for a dark energy model with equation of state w =-2/3, which correspond to a static domain wall network.
Evaluation of Nonparametric Probabilistic Forecasts of Wind Power
DEFF Research Database (Denmark)
Pinson, Pierre; Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg, orlov 31.07.2008;
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...... of quantile forecasts. The required and desirable properties of such probabilistic forecasts are defined and a framework for their evaluation is proposed. This framework is applied for evaluating the quality of two statistical methods producing full predictive distributions from point predictions of wind......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...
Equity and efficiency in private and public education: a nonparametric comparison
L. Cherchye; K. de Witte; E. Ooghe; I. Nicaise
2007-01-01
We present a nonparametric approach for the equity and efficiency evaluation of (private and public) primary schools in Flanders. First, we use a nonparametric (Data Envelopment Analysis) model that is specially tailored to assess educational efficiency at the pupil level. The model accounts for the
Song, Dong; Wang, Zhuo; Marmarelis, Vasilis Z; Berger, Theodore W
2009-02-01
This paper presents a synergistic parametric and non-parametric modeling study of short-term plasticity (STP) in the Schaffer collateral to hippocampal CA1 pyramidal neuron (SC) synapse. Parametric models in the form of sets of differential and algebraic equations have been proposed on the basis of the current understanding of biological mechanisms active within the system. Non-parametric Poisson-Volterra models are obtained herein from broadband experimental input-output data. The non-parametric model is shown to provide better prediction of the experimental output than a parametric model with a single set of facilitation/depression (FD) process. The parametric model is then validated in terms of its input-output transformational properties using the non-parametric model since the latter constitutes a canonical and more complete representation of the synaptic nonlinear dynamics. Furthermore, discrepancies between the experimentally-derived non-parametric model and the equivalent non-parametric model of the parametric model suggest the presence of multiple FD processes in the SC synapses. Inclusion of an additional set of FD process in the parametric model makes it replicate better the characteristics of the experimentally-derived non-parametric model. This improved parametric model in turn provides the requisite biological interpretability that the non-parametric model lacks.
Out-of-Sample Extensions for Non-Parametric Kernel Methods.
Pan, Binbin; Chen, Wen-Sheng; Chen, Bo; Xu, Chen; Lai, Jianhuang
2017-02-01
Choosing suitable kernels plays an important role in the performance of kernel methods. Recently, a number of studies were devoted to developing nonparametric kernels. Without assuming any parametric form of the target kernel, nonparametric kernel learning offers a flexible scheme to utilize the information of the data, which may potentially characterize the data similarity better. The kernel methods using nonparametric kernels are referred to as nonparametric kernel methods. However, many nonparametric kernel methods are restricted to transductive learning, where the prediction function is defined only over the data points given beforehand. They have no straightforward extension for the out-of-sample data points, and thus cannot be applied to inductive learning. In this paper, we show how to make the nonparametric kernel methods applicable to inductive learning. The key problem of out-of-sample extension is how to extend the nonparametric kernel matrix to the corresponding kernel function. A regression approach in the hyper reproducing kernel Hilbert space is proposed to solve this problem. Empirical results indicate that the out-of-sample performance is comparable to the in-sample performance in most cases. Experiments on face recognition demonstrate the superiority of our nonparametric kernel method over the state-of-the-art parametric kernel methods.
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
Equity and efficiency in private and public education: a nonparametric comparison
Cherchye, L.; de Witte, K.; Ooghe, E.; Nicaise, I.
2007-01-01
We present a nonparametric approach for the equity and efficiency evaluation of (private and public) primary schools in Flanders. First, we use a nonparametric (Data Envelopment Analysis) model that is specially tailored to assess educational efficiency at the pupil level. The model accounts for the
Semi-parametric regression: Efficiency gains from modeling the nonparametric part
Yu, Kyusang; Park, Byeong U; 10.3150/10-BEJ296
2011-01-01
It is widely admitted that structured nonparametric modeling that circumvents the curse of dimensionality is important in nonparametric estimation. In this paper we show that the same holds for semi-parametric estimation. We argue that estimation of the parametric component of a semi-parametric model can be improved essentially when more structure is put into the nonparametric part of the model. We illustrate this for the partially linear model, and investigate efficiency gains when the nonparametric part of the model has an additive structure. We present the semi-parametric Fisher information bound for estimating the parametric part of the partially linear additive model and provide semi-parametric efficient estimators for which we use a smooth backfitting technique to deal with the additive nonparametric part. We also present the finite sample performances of the proposed estimators and analyze Boston housing data as an illustration.
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.
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:
Directory of Open Access Journals (Sweden)
Weiß, Verena
2015-10-01
Full Text Available Introduction: For survival data the coefficient of determination cannot be used to describe how good a model fits to the data. Therefore, several measures of explained variation for survival data have been proposed in recent years.Methods: We analyse an existing measure of explained variation with regard to minimisation aspects and demonstrate that these are not fulfilled for the measure.Results: In analogy to the least squares method from linear regression analysis we develop a novel measure for categorical covariates which is based only on the Kaplan-Meier estimator. Hence, the novel measure is a completely nonparametric measure with an easy graphical interpretation. For the novel measure different weighting possibilities are available and a statistical test of significance can be performed. Eventually, we apply the novel measure and further measures of explained variation to a dataset comprising persons with a histopathological papillary thyroid carcinoma.Conclusion: We propose a novel measure of explained variation with a comprehensible derivation as well as a graphical interpretation, which may be used in further analyses with survival data.
Phasukkijwatana, Nopasak; Kunhapan, Bussaraporn; Stankovich, Jim; Chuenkongkaew, Wanicha L; Thomson, Russell; Thornton, Timothy; Bahlo, Melanie; Mushiroda, Taisei; Nakamura, Yusuke; Mahasirimongkol, Surakameth; Tun, Aung Win; Srisawat, Chatchawan; Limwongse, Chanin; Peerapittayamongkol, Chayanon; Sura, Thanyachai; Suthammarak, Wichit; Lertrit, Patcharee
2010-07-01
Leber hereditary optic neuropathy (LHON) is the most common mitochondrially inherited disease causing blindness, preferentially in young adult males. Most of the patients carry the G11778A mitochondrial DNA (mtDNA) mutation. However, the marked incomplete penetrance and the gender bias indicate some additional genetic and/or environmental factors to disease expression. Herein, we first conducted a genome-wide linkage scan with 400 microsatellite markers in 9 large Thai LHON G11778A pedigrees. Using an affecteds-only nonparametric linkage analysis, 4 regions on chromosomes 3, 12, 13 and 18 showed Zlr scores greater than 2 (P 2 in 10 of 16 allele sharing models tested) was then expanded to include the region 3q26.2-3q28 covering SLC7A14 (3q26.2), MFN1 (3q26.32), MRPL47 (3q26.33), MCCC1 (3q27.1), PARL (3q27.1) and OPA1 (3q28-q29). All of these candidate genes were selected from the Maestro database and had known to be localized in mitochondria. Sixty tag SNPs were genotyped in 86 cases, 211 of their relatives and 32 unrelated Thai controls, by multiplex-PCR-based Invader assay. Analyses using a powerful association testing tool that adjusts for relatedness (the M(QLS) statistic) showed the most evidence of association between two SNPs, rs3749446 and rs1402000 (located in PARL presenilins-associated rhomboid-like) and LHON expression (both P = 8.8 x 10(-5)). The mitochondrial PARL protease has been recently known to play a role with a dynamin-related OPA1 protein in preventing apoptotic events by slowing down the release of cytochrome c out of mitochondrial cristae junctions. Moreover, PARL is required to activate the intramembranous proteolyses resulting in the degradation of an accumulated pro-apoptotic protein in the outer mitochondrial membrane. Under these circumstances, variants of PARL are suggested to influence cell death by apoptosis which has long been believed to intrigue the neurodegeneration of LHON.
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.
Linkage mechanics and power amplification of the mantis shrimp's strike.
Patek, S N; Nowroozi, B N; Baio, J E; Caldwell, R L; Summers, A P
2007-10-01
Mantis shrimp (Stomatopoda) generate extremely rapid and forceful predatory strikes through a suite of structural modifications of their raptorial appendages. Here we examine the key morphological and kinematic components of the raptorial strike that amplify the power output of the underlying muscle contractions. Morphological analyses of joint mechanics are integrated with CT scans of mineralization patterns and kinematic analyses toward the goal of understanding the mechanical basis of linkage dynamics and strike performance. We test whether a four-bar linkage mechanism amplifies rotation in this system and find that the rotational amplification is approximately two times the input rotation, thereby amplifying the velocity and acceleration of the strike. The four-bar model is generally supported, although the observed kinematic transmission is lower than predicted by the four-bar model. The results of the morphological, kinematic and mechanical analyses suggest a multi-faceted mechanical system that integrates latches, linkages and lever arms and is powered by multiple sites of cuticular energy storage. Through reorganization of joint architecture and asymmetric distribution of mineralized cuticle, the mantis shrimp's raptorial appendage offers a remarkable example of how structural and mechanical modifications can yield power amplification sufficient to produce speeds and forces at the outer known limits of biological systems.
Directed Graphs, Decompositions, and Spatial Linkages
Shai, Offer; Whiteley, Walter
2010-01-01
The decomposition of a system of constraints into small basic components is an important tool of design and analysis. Specifically, the decomposition of a linkage into minimal components is a central tool of analysis and synthesis of linkages. In this paper we prove that every pinned 3-isostatic (minimally rigid) graph (grounded linkage) has a unique decomposition into minimal strongly connected components (in the sense of directed graphs) which we call 3-Assur graphs. This analysis extends the Assur decompositions of plane linkages previously studied in the mathematical and the mechanical engineering literature. These 3-Assur graphs are the central building blocks for all kinematic linkages in 3-space. They share a number of key combinatorial and geometric properties with the 2-Assur graphs, including an associated lower block-triangular decomposition of the pinned rigidity matrix which provides a format for extending the motion induced by inserting one driver in a bottom Assur linkage to the joints of the e...
Directory of Open Access Journals (Sweden)
Goddard Mike E
2004-05-01
Full Text Available Abstract A multi-locus QTL mapping method is presented, which combines linkage and linkage disequilibrium (LD information and uses multitrait data. The method assumed a putative QTL at the midpoint of each marker bracket. Whether the putative QTL had an effect or not was sampled using Markov chain Monte Carlo (MCMC methods. The method was tested in dairy cattle data on chromosome 14 where the DGAT1 gene was known to be segregating. The DGAT1 gene was mapped to a region of 0.04 cM, and the effects of the gene were accurately estimated. The fitting of multiple QTL gave a much sharper indication of the QTL position than a single QTL model using multitrait data, probably because the multi-locus QTL mapping reduced the carry over effect of the large DGAT1 gene to adjacent putative QTL positions. This suggests that the method could detect secondary QTL that would, in single point analyses, remain hidden under the broad peak of the dominant QTL. However, no indications for a second QTL affecting dairy traits were found on chromosome 14.
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.
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.
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
2017-01-18
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.
The Utility of Nonparametric Transformations for Imputation of Survey Data
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Robbins Michael W.
2014-12-01
Full Text Available Missing values present a prevalent problem in the analysis of establishment survey data. Multivariate imputation algorithms (which are used to fill in missing observations tend to have the common limitation that imputations for continuous variables are sampled from Gaussian distributions. This limitation is addressed here through the use of robust marginal transformations. Specifically, kernel-density and empirical distribution-type transformations are discussed and are shown to have favorable properties when used for imputation of complex survey data. Although such techniques have wide applicability (i.e., they may be easily applied in conjunction with a wide array of imputation techniques, the proposed methodology is applied here with an algorithm for imputation in the USDA’s Agricultural Resource Management Survey. Data analysis and simulation results are used to illustrate the specific advantages of the robust methods when compared to the fully parametric techniques and to other relevant techniques such as predictive mean matching. To summarize, transformations based upon parametric densities are shown to distort several data characteristics in circumstances where the parametric model is ill fit; however, no circumstances are found in which the transformations based upon parametric models outperform the nonparametric transformations. As a result, the transformation based upon the empirical distribution (which is the most computationally efficient is recommended over the other transformation procedures in practice.
Nonparametric identification of structural modifications in Laplace domain
Suwała, G.; Jankowski, Ł.
2017-02-01
This paper proposes and experimentally verifies a Laplace-domain method for identification of structural modifications, which (1) unlike time-domain formulations, allows the identification to be focused on these parts of the frequency spectrum that have a high signal-to-noise ratio, and (2) unlike frequency-domain formulations, decreases the influence of numerical artifacts related to the particular choice of the FFT exponential window decay. In comparison to the time-domain approach proposed earlier, advantages of the proposed method are smaller computational cost and higher accuracy, which leads to reliable performance in more difficult identification cases. Analytical formulas for the first- and second-order sensitivity analysis are derived. The approach is based on a reduced nonparametric model, which has the form of a set of selected structural impulse responses. Such a model can be collected purely experimentally, which obviates the need for design and laborious updating of a parametric model, such as a finite element model. The approach is verified experimentally using a 26-node lab 3D truss structure and 30 identification cases of a single mass modification or two concurrent mass modifications.
A New Non-Parametric Approach to Galaxy Morphological Classification
Lotz, J M; Madau, P; Lotz, Jennifer M.; Primack, Joel; Madau, Piero
2003-01-01
We present two new non-parametric methods for quantifying galaxy morphology: the relative distribution of the galaxy pixel flux values (the Gini coefficient or G) and the second-order moment of the brightest 20% of the galaxy's flux (M20). We test the robustness of G and M20 to decreasing signal-to-noise and spatial resolution, and find that both measures are reliable to within 10% at average signal-to-noise per pixel greater than 3 and resolutions better than 1000 pc and 500 pc, respectively. We have measured G and M20, as well as concentration (C), asymmetry (A), and clumpiness (S) in the rest-frame near-ultraviolet/optical wavelengths for 150 bright local "normal" Hubble type galaxies (E-Sd) galaxies and 104 0.05 < z < 0.25 ultra-luminous infrared galaxies (ULIRGs).We find that most local galaxies follow a tight sequence in G-M20-C, where early-types have high G and C and low M20 and late-type spirals have lower G and C and higher M20. The majority of ULIRGs lie above the normal galaxy G-M20 sequence...
Biological parametric mapping with robust and non-parametric statistics.
Yang, Xue; Beason-Held, Lori; Resnick, Susan M; Landman, Bennett A
2011-07-15
Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, regions of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrices. Recently, biological parametric mapping has extended the widely popular statistical parametric mapping approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and non-parametric regression in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provide a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities. Copyright © 2011 Elsevier Inc. All rights reserved.
Adaptive Neural Network Nonparametric Identifier With Normalized Learning Laws.
Chairez, Isaac
2016-04-05
This paper addresses the design of a normalized convergent learning law for neural networks (NNs) with continuous dynamics. The NN is used here to obtain a nonparametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties is the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on normalized algorithms was used to adjust the weights of the NN. The adaptive algorithm was derived by means of a nonstandard logarithmic Lyapunov function (LLF). Two identifiers were designed using two variations of LLFs leading to a normalized learning law for the first identifier and a variable gain normalized learning law. In the case of the second identifier, the inclusion of normalized learning laws yields to reduce the size of the convergence region obtained as solution of the practical stability analysis. On the other hand, the velocity of convergence for the learning laws depends on the norm of errors in inverse form. This fact avoids the peaking transient behavior in the time evolution of weights that accelerates the convergence of identification error. A numerical example demonstrates the improvements achieved by the algorithm introduced in this paper compared with classical schemes with no-normalized continuous learning methods. A comparison of the identification performance achieved by the no-normalized identifier and the ones developed in this paper shows the benefits of the learning law proposed in this paper.
Nonparametric estimation of quantum states, processes and measurements
Lougovski, Pavel; Bennink, Ryan
Quantum state, process, and measurement estimation methods traditionally use parametric models, in which the number and role of relevant parameters is assumed to be known. When such an assumption cannot be justified, a common approach in many disciplines is to fit the experimental data to multiple models with different sets of parameters and utilize an information criterion to select the best fitting model. However, it is not always possible to assume a model with a finite (countable) number of parameters. This typically happens when there are unobserved variables that stem from hidden correlations that can only be unveiled after collecting experimental data. How does one perform quantum characterization in this situation? We present a novel nonparametric method of experimental quantum system characterization based on the Dirichlet Process (DP) that addresses this problem. Using DP as a prior in conjunction with Bayesian estimation methods allows us to increase model complexity (number of parameters) adaptively as the number of experimental observations grows. We illustrate our approach for the one-qubit case and show how a probability density function for an unknown quantum process can be estimated.
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.
Non-parametric and least squares Langley plot methods
Directory of Open Access Journals (Sweden)
P. W. Kiedron
2015-04-01
Full Text Available Langley plots are used to calibrate sun radiometers primarily for the measurement of the aerosol component of the atmosphere that attenuates (scatters and absorbs incoming direct solar radiation. In principle, the calibration of a sun radiometer is a straightforward application of the Bouguer–Lambert–Beer law V=V>/i>0e−τ ·m, where a plot of ln (V voltage vs. m air mass yields a straight line with intercept ln (V0. This ln (V0 subsequently can be used to solve for τ for any measurement of V and calculation of m. This calibration works well on some high mountain sites, but the application of the Langley plot calibration technique is more complicated at other, more interesting, locales. This paper is concerned with ferreting out calibrations at difficult sites and examining and comparing a number of conventional and non-conventional methods for obtaining successful Langley plots. The eleven techniques discussed indicate that both least squares and various non-parametric techniques produce satisfactory calibrations with no significant differences among them when the time series of ln (V0's are smoothed and interpolated with median and mean moving window filters.
Pivotal Estimation of Nonparametric Functions via Square-root Lasso
Belloni, Alexandre; Wang, Lie
2011-01-01
In a nonparametric linear regression model we study a variant of LASSO, called square-root LASSO, which does not require the knowledge of the scaling parameter $\\sigma$ of the noise or bounds for it. This work derives new finite sample upper bounds for prediction norm rate of convergence, $\\ell_1$-rate of converge, $\\ell_\\infty$-rate of convergence, and sparsity of the square-root LASSO estimator. A lower bound for the prediction norm rate of convergence is also established. In many non-Gaussian noise cases, we rely on moderate deviation theory for self-normalized sums and on new data-dependent empirical process inequalities to achieve Gaussian-like results provided log p = o(n^{1/3}) improving upon results derived in the parametric case that required log p = O(log n). In addition, we derive finite sample bounds on the performance of ordinary least square (OLS) applied tom the model selected by square-root LASSO accounting for possible misspecification of the selected model. In particular, we provide mild con...
NParCov3: A SAS/IML Macro for Nonparametric Randomization-Based Analysis of Covariance
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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.
Linkages between NAMA - LEDS - MRV
DEFF Research Database (Denmark)
Agyemang-Bonsu, William; Benioff, Ron; Cox, Sadie
Low Emission Development Strategies (LEDS), Nationally Appropriate Mitigation Actions (NAMAs) and Monitoring, Reporting and Verification (MRV) are three of the key conceptual components emerging as part of the global architecture for a new climate agreement by 2015. The three components are devel......Low Emission Development Strategies (LEDS), Nationally Appropriate Mitigation Actions (NAMAs) and Monitoring, Reporting and Verification (MRV) are three of the key conceptual components emerging as part of the global architecture for a new climate agreement by 2015. The three components...... how the three components are conceptually interlinked. Identifying the linkages can inform the work on each component and strengthen coordination of work in the context of the three big partnerships; the International Partnership on Mitigation and MRV, the LEDS Global Partnership and the NAMA...
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.
Institute of Scientific and Technical Information of China (English)
LINGNeng-xiang; DUXue-qiao
2005-01-01
In this paper, we study the strong consistency for partitioning estimation of regression function under samples that axe φ-mixing sequences with identically distribution.Key words: nonparametric regression function; partitioning estimation; strong convergence;φ-mixing sequences.
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...
Bayesian nonparametric estimation and consistency of mixed multinomial logit choice models
De Blasi, Pierpaolo; Lau, John W; 10.3150/09-BEJ233
2011-01-01
This paper develops nonparametric estimation for discrete choice models based on the mixed multinomial logit (MMNL) model. It has been shown that MMNL models encompass all discrete choice models derived under the assumption of random utility maximization, subject to the identification of an unknown distribution $G$. Noting the mixture model description of the MMNL, we employ a Bayesian nonparametric approach, using nonparametric priors on the unknown mixing distribution $G$, to estimate choice probabilities. We provide an important theoretical support for the use of the proposed methodology by investigating consistency of the posterior distribution for a general nonparametric prior on the mixing distribution. Consistency is defined according to an $L_1$-type distance on the space of choice probabilities and is achieved by extending to a regression model framework a recent approach to strong consistency based on the summability of square roots of prior probabilities. Moving to estimation, slightly different te...
Nonparametric Monitoring for Geotechnical Structures Subject to Long-Term Environmental Change
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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.
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Additive Models
Fan, Jianqing; Song, Rui
2011-01-01
A variable screening procedure via correlation learning was proposed Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonparametric learning. Our nonparametric independence screening is called NIS, a specific member of the sure independence screening. Several closely related variable screening procedures are proposed. Under the nonparametric additive models, it is shown that under some mild technical conditions, the proposed independence screening methods enjoy a sure screening property. The extent to which the dimensionality can be reduced by independence screening is also explicitly quantified. As a methodological extension, an iterative nonparametric independence screening (INIS) is also proposed to enhance the finite sample performance for fitting sparse additive models. The simulation results and a real data a...
Nonparametric TOA estimators for low-resolution IR-UWB digital receiver
Institute of Scientific and Technical Information of China (English)
Yanlong Zhang; Weidong Chen
2015-01-01
Nonparametric time-of-arrival (TOA) estimators for im-pulse radio ultra-wideband (IR-UWB) signals are proposed. Non-parametric detection is obviously useful in situations where de-tailed information about the statistics of the noise is unavailable or not accurate. Such TOA estimators are obtained based on condi-tional statistical tests with only a symmetry distribution assumption on the noise probability density function. The nonparametric es-timators are attractive choices for low-resolution IR-UWB digital receivers which can be implemented by fast comparators or high sampling rate low resolution analog-to-digital converters (ADCs), in place of high sampling rate high resolution ADCs which may not be available in practice. Simulation results demonstrate that nonparametric TOA estimators provide more effective and robust performance than typical energy detection (ED) based estimators.
Nonparametric statistical tests for the continuous data: the basic concept and the practical use.
Nahm, Francis Sahngun
2016-02-01
Conventional statistical tests are usually called parametric tests. Parametric tests are used more frequently than nonparametric tests in many medical articles, because most of the medical researchers are familiar with and the statistical software packages strongly support parametric tests. Parametric tests require important assumption; assumption of normality which means that distribution of sample means is normally distributed. However, parametric test can be misleading when this assumption is not satisfied. In this circumstance, nonparametric tests are the alternative methods available, because they do not required the normality assumption. Nonparametric tests are the statistical methods based on signs and ranks. In this article, we will discuss about the basic concepts and practical use of nonparametric tests for the guide to the proper use.
Extent of linkage disequilibrium in chicken
Aerts, J.; Megens, H.J.W.C.; Veenendaal, T.; Ovcharenko, I.; Crooijmans, R.P.M.A.; Gordon, L.; Stubbs, L.; Groenen, M.A.M.; Rodoinov, A.; Gaginskaya, E.
2007-01-01
Many of the economically important traits in chicken are multifactorial and governed by multiple genes located at different quantitative trait loci (QTLs). The optimal marker density to identify these QTLs in linkage and association studies is largely determined by the extent of linkage
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.
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.
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.
Nonparametric Bayesian inference of the microcanonical stochastic block model
Peixoto, Tiago P.
2017-01-01
A principled approach to characterize the hidden modular structure of networks is to formulate generative models and then infer their parameters from data. When the desired structure is composed of modules or "communities," a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the structure is imposed via hard constraints, i.e., the generated networks are not allowed to violate the patterns imposed by the model. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: (1) deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, which not only remove limitations that seriously degrade the inference on large networks but also reveal structures at multiple scales; (2) a very efficient inference algorithm that scales well not only for networks with a large number of nodes and edges but also with an unlimited number of modules. We show also how this approach can be used to sample modular hierarchies from the posterior distribution, as well as to perform model selection. We discuss and analyze the differences between sampling from the posterior and simply finding the single parameter estimate that maximizes it. Furthermore, we expose a direct equivalence between our microcanonical approach and alternative derivations based on the canonical SBM.
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 novel nonparametric confidence interval for differences of proportions for correlated binary data.
Duan, Chongyang; Cao, Yingshu; Zhou, Lizhi; Tan, Ming T; Chen, Pingyan
2016-11-16
Various confidence interval estimators have been developed for differences in proportions resulted from correlated binary data. However, the width of the mostly recommended Tango's score confidence interval tends to be wide, and the computing burden of exact methods recommended for small-sample data is intensive. The recently proposed rank-based nonparametric method by treating proportion as special areas under receiver operating characteristic provided a new way to construct the confidence interval for proportion difference on paired data, while the complex computation limits its application in practice. In this article, we develop a new nonparametric method utilizing the U-statistics approach for comparing two or more correlated areas under receiver operating characteristics. The new confidence interval has a simple analytic form with a new estimate of the degrees of freedom of n - 1. It demonstrates good coverage properties and has shorter confidence interval widths than that of Tango. This new confidence interval with the new estimate of degrees of freedom also leads to coverage probabilities that are an improvement on the rank-based nonparametric confidence interval. Comparing with the approximate exact unconditional method, the nonparametric confidence interval demonstrates good coverage properties even in small samples, and yet they are very easy to implement computationally. This nonparametric procedure is evaluated using simulation studies and illustrated with three real examples. The simplified nonparametric confidence interval is an appealing choice in practice for its ease of use and good performance. © The Author(s) 2016.
Identifying marker typing incompatibilities in linkage analysis
Energy Technology Data Exchange (ETDEWEB)
Stringham, H.M.; Boehnke, M. [Univ. of Michigan, Ann Arbor, MI (United States)
1996-10-01
A common problem encountered in linkage analyses is that execution of the computer program is halted because of genotypes in the data that are inconsistent with Mendelian inheritance. Such inconsistencies may arise because of pedigree errors or errors in typing. In some cases, the source of the inconsistencies is easily identified by examining the pedigree. In others, the error is not obvious, and substantial time and effort are required to identify the responsible genotypes. We have developed two methods for automatically identifying those individuals whose genotypes are most likely the cause of the inconsistencies. First, we calculate the posterior probability of genotyping error for each member of the pedigree, given the marker data on all pedigree members and allowing anyone in the pedigree to have an error. Second, we identify those individuals whose genotypes could be solely responsible for the inconsistency in the pedigree. We illustrate these methods with two examples: one a pedigree error, the second a genotyping error. These methods have been implemented as a module of the pedigree analysis program package MENDEL. 9 refs., 2 figs., 2 tabs.
Brant, Steven R; Shugart, Yin Yao
2004-05-01
Observed inflammatory bowel disease (IBD) familial clustering and increased monozygotic twin concordance has led to the hypothesis that genetic loci containing IBD susceptibility genes can be identified by whole genome linkage mapping approaches. Methodology including collecting carefully phenotyped multiplex pedigrees, genotyping using highly informative microsatellite markers and linkage analysis by non-parametric allele sharing methods has been established. Eleven published genome wide screens (GWS) have studied more than 1,200 multiplex IBD pedigrees. Two-thirds of affected relative pairs were Crohn's disease (CD), 20% ulcerative colitis (UC) and the remaining were mixed. Seven loci (IBDI-7) on chromosomes 16q, 12, 6p, 14q, 5q, 19, and 1p have been identified with genome wide significant and independently replicated linkage. Risk alleles/haplotypes have been defined for the IBD1 (CARD15/NOD2), IBD3 (HLA) and IBD5 (5q cytokine cluster) loci. There has been evidence for a second chromosome 16 locus (IBD8) independent of NOD2 that overlaps IBD1 on the pericentromeric p-arm. Several other regions show great promise for containing additional IBD loci, particularly chromosome 3p with genome wide evidence in one study at 3p26 and more centromeric evidence in several other studies, and chromosomes 2q, 3q, 4q, 7, 11p, and Xp each with suggestive evidence of linkage in one and additional evidence in two or more studies. Single GWSs and fine mapping studies containing very large sets of pedigrees and in particular, more UC pedigrees, and the use of creative analytic and disease stratification schemes are required to identify, establish and refine weaker IBD loci.
Guan, Weihua; Boehnke, Michael; Pluzhnikov, Anna; Cox, Nancy J; Scott, Laura J
2012-12-01
When planning resequencing studies for complex diseases, previous association and linkage studies can constrain the range of plausible genetic models for a given locus. Here, we explore the combinations of causal risk allele frequency (RAFC ) and genotype relative risk (GRRC ) consistent with no or limited evidence for affected sibling pair (ASP) linkage and strong evidence for case-control association. We find that significant evidence for case-control association combined with no or moderate evidence for ASP linkage can define a lower bound for the plausible RAFC . Using data from large type 2 diabetes (T2D) linkage and genome-wide association study meta-analyses, we find that under reasonable model assumptions, 23 of 36 autosomal T2D risk loci are unlikely to be due to causal variants with combined RAFC < 0.005, and four of the 23 are unlikely to be due to causal variants with combined RAFC < 0.05.
Energy Technology Data Exchange (ETDEWEB)
Stevens, J. G.; Nuclear Engineering Division
2009-04-24
The Reduced Enrichment Research and Test Reactor (RERTR) Program uses the REBUS-PC computer code to provide reactor physics and core design information such as neutron flux distributions in space, energy, and time, and to track isotopic changes in fuel and neutron absorbers with burnup. REBUS-PC models the complete fuel cycle including shuffling capability. REBUS-PC evolved using the neutronic capabilities of multi-group diffusion theory code DIF3D 9.0, but was extended to apply the continuous energy Monte Carlo code MCNP for one-group fluxes and cross-sections. The linkage between REBUS-PC and MCNP has recently been modernized and extended, as described in this manual. REBUS-PC now calls MCNP via a system call so that the user can apply any valid MCNP executable. The interface between REBUS-PC and MCNP requires minimal changes to an existing MCNP model, and little additional input. The REBUS-MCNP interface can also be used in conjunction with DIF3D neutronics to update an MCNP model with fuel compositions predicted using a DIF3D based depletion.
Hussey, Michael A; Koch, Gary G; Preisser, John S; Saville, Benjamin R
2016-01-01
Time-to-event or dichotomous outcomes in randomized clinical trials often have analyses using the Cox proportional hazards model or conditional logistic regression, respectively, to obtain covariate-adjusted log hazard (or odds) ratios. Nonparametric Randomization-Based Analysis of Covariance (NPANCOVA) can be applied to unadjusted log hazard (or odds) ratios estimated from a model containing treatment as the only explanatory variable. These adjusted estimates are stratified population-averaged treatment effects and only require a valid randomization to the two treatment groups and avoid key modeling assumptions (e.g., proportional hazards in the case of a Cox model) for the adjustment variables. The methodology has application in the regulatory environment where such assumptions cannot be verified a priori. Application of the methodology is illustrated through three examples on real data from two randomized trials.
Ford, Eric B; Steffen, Jason H; Carter, Joshua A; Fressin, Francois; Holman, Matthew J; Lissauer, Jack J; Moorhead, Althea V; Morehead, Robert C; Ragozzine, Darin; Rowe, Jason F; Welsh, William F; Allen, Christopher; Batalha, Natalie M; Borucki, William J; Bryson, Stephen T; Buchhave, Lars A; Burke, Christopher J; Caldwell, Douglas A; Charbonneau, David; Clarke, Bruce D; Cochran, William D; Désert, Jean-Michel; Endl, Michael; Everett, Mark E; Fischer, Debra A; Gautier, Thomas N; Gilliland, Ron L; Jenkins, Jon M; Haas, Michael R; Horch, Elliott; Howell, Steve B; Ibrahim, Khadeejah A; Isaacson, Howard; Koch, David G; Latham, David W; Li, Jie; Lucas, Philip; MacQueen, Phillip J; Marcy, Geoffrey W; McCauliff, Sean; Mullally, Fergal R; Quinn, Samuel N; Quintana, Elisa; Shporer, Avi; Still, Martin; Tenenbaum, Peter; Thompson, Susan E; Torres, Guillermo; Twicken, Joseph D; Wohler, Bill
2012-01-01
We present a new method for confirming transiting planets based on the combination of transit timingn 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:...
Danielski, C; Tinetti, G
2013-01-01
The NASA Kepler mission is delivering groundbreaking results, with an increasing number of Earth-sized and moon-sized objects been discovered. A high photometric precision can be reached only through a thorough removal of the stellar activity and the instrumental systematics. We have explored here the possibility of using non-parametric methods to analyse the Simple Aperture Photometry data observed by the Kepler mission. We focused on a sample of stellar light curves with different effective temperatures and flux modulations, and we found that Gaussian Processes-based techniques can very effectively correct the instrumental systematics along with the long-term stellar activity. Our method can disentangle astrophysical features (events), such as planetary transits, flares or general sudden variations in the intensity, from the star signal and it is very efficient as it requires only a few training iterations of the Gaussian Process model. The results obtained show the potential of our method to isolate the ma...
DEFF Research Database (Denmark)
Boolsen, Merete Watt
bogen forklarer de fundamentale trin i forskningsprocessen og applikerer dem på udvalgte kvalitative analyser: indholdsanalyse, Grounded Theory, argumentationsanalyse og diskursanalyse......bogen forklarer de fundamentale trin i forskningsprocessen og applikerer dem på udvalgte kvalitative analyser: indholdsanalyse, Grounded Theory, argumentationsanalyse og diskursanalyse...
Silverman, Edwin K; Mosley, Jonathan D; Palmer, Lyle J; Barth, Matthew; Senter, Jody M; Brown, Alison; Drazen, Jeffrey M; Kwiatkowski, David J; Chapman, Harold A; Campbell, Edward J; Province, Michael A; Rao, D C; Reilly, John J; Ginns, Leo C; Speizer, Frank E; Weiss, Scott T
2002-03-15
Familial aggregation of chronic obstructive pulmonary disease (COPD) has been demonstrated, but linkage analysis of COPD-related phenotypes has not been reported previously. An autosomal 10 cM genome-wide scan of short tandem repeat (STR) polymorphic markers was analyzed for linkage to COPD-related phenotypes in 585 members of 72 pedigrees ascertained through severe, early-onset COPD probands without severe alpha1-antitrypsin deficiency. Multipoint non-parametric linkage analysis (using the ALLEGRO program) was performed for qualitative phenotypes including moderate airflow obstruction [forced expiratory volume at one second (FEV(1)) < 60% predicted, FEV(1)/FVC < 90% predicted], mild airflow obstruction (FEV(1) < 80% predicted, FEV(1)/FVC < 90% predicted) and chronic bronchitis. The strongest evidence for linkage in all subjects was observed at chromosomes 12 (LOD = 1.70) and 19 (LOD = 1.54) for moderate airflow obstruction, chromosomes 8 (LOD = 1.36) and 19 (LOD = 1.09) for mild airflow obstruction and chromosomes 19 (LOD = 1.21) and 22 (LOD = 1.37) for chronic bronchitis. Restricting analysis to cigarette smokers only provided increased evidence for linkage of mild airflow obstruction and chronic bronchitis to several genomic regions; for mild airflow obstruction in smokers only, the maximum LOD was 1.64 at chromosome 19, whereas for chronic bronchitis in smokers only, the maximum LOD was 2.08 at chromosome 22. On chromosome 12p, 12 additional STR markers were genotyped, which provided additional support for an airflow obstruction locus in that region with a non-parametric multipoint approach for moderate airflow obstruction (LOD = 2.13) and mild airflow obstruction (LOD = 1.43). Using a dominant model with the STR markers on 12p, two point parametric linkage analysis of all subjects demonstrated a maximum LOD score of 2.09 for moderate airflow obstruction and 2.61 for mild airflow obstruction. In smokers only, the maximum two point LOD score for mild airflow
DYNAMIC DESIGN OF VARIABLE SPEED PLANAR LINKAGES
Institute of Scientific and Technical Information of China (English)
Yao Yanan; Yan Hongsen; Zou Huijun
2005-01-01
A method for improving dynamic characteristics of planar linkages by actively varying the speed function of the input link is presented. Design criteria and constraints for the dynamic design of variable speed planar linkages are developed. Both analytical and optimization approaches for determining suitable input speed functions to minimize the driving torque, the shaking moment, or both simultaneously of planar linkages, subject to various design requirements and constraints, are derived.Finally, some examples are given to illustrate the design procedure and to verify its feasibility.
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
Noor Rashidah Rashid
2012-01-01
Cluster Analysis is a multivariate method in statistics. Agglomerative Hierarchical Cluster Analysis is one of approaches in Cluster Analysis. There are two linkage methods in Agglomerative Hierarchical Cluster Analysis which are Single Linkage and Complete Linkage. The purpose of this study is to compare between Single Linkage and Complete Linkage in Agglomerative Hierarchical Cluster Analysis. The comparison of performances between these linkage methods was shown by using Kruskal-Wallis tes...
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.
[Dystroglycan linkage and muscular dystrophy].
Shimizu, Teruo
2002-11-01
Dystroglycan is a key complex between basal lamina laminin, extracellularly and membrano-cytoskeleton, intracellularly. The damage of this linkage is turned out to cause muscular dystrophies. Dystroglycan knockout is lethal. Dystroglycan-associated intracellular proteins such as dystrophin, dystrobrevin, sarcoglycans, plectin and caveolin-3 are responsible for causing severe (Duchenne type) and moderate forms (Becker, LGMDs). Laminin, dystroglycan-binding extracellular protein, is deficient in the most severe form of congenital muscular dystrophy with normal intelligence and eye. Recently, a remarkable progress is made in most severe forms of congenital muscular dystrophy with anomalies of brain and eye such as Fukuyama type (Japan) and muscle-eye-brain disease (Finland). The gene product for Fukuyama type, fukutin, belongs to a family of glycosylation enzymes in bacteria and yeast. Since alpha-dystroglycan contains 14-15 o-glycans, ser/thr-mannose 2-1 GlcNAc 4-1 Gal 3-2 Sial in the middle third mucin-domain and the sial-o-glycan is essential for laminin-binding, and since alpha-dystroglycan is defective in Fukuyama type sarcolemma with anti both sugar moiety- and peptide-antidodies, defective fukutin causes incomplete o-glycosylation of alpha-dystroglycan. In '02, it is clarified that a glycosylation enzyme, POMGnT1 which modifies GlcNAc onto ser/thr-mannose, is defective in 6 MEB patients. The loss of the enzyme activity is turned out to lose alpha-dystroglycan from sarcolemma of MEB. These data strongly suggests that o-glycosylation defect of alpha-dystroglycan causes the most severe congenital muscular dystrophy such as Fukuyama type, MEB and Walker Warburg syndrome.
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...
Connectivity and Linkages Between Isolated Habitat
California Department of Resources — Proposed areas where connectiviy and linkages between isolated habitat on the San Joaquin Valley floor and natural lands in the surrounding foothills should be...
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
Life distributions structure of nonparametric, semiparametric, and parametric families
Marshall, Albert W
2007-01-01
This book is devoted to the study of univariate distributions appropriate for the analyses of data known to be nonnegative. The book includes much material from reliability theory in engineering and survival analysis in medicine.
Unidimensional nonnegative scaling for genome-wide linkage disequilibrium maps.
Liao, Haiyong; Ng, Michael; Fung, Eric; Sham, Pak C
2008-01-01
The main aim of this paper is to propose and develop a unidimensional nonnegative scaling model to construct Linkage Disequilibrium (LD) maps. The proposed constrained scaling model can be efficiently solved by transforming it to an unconstrained model. The method is implemented in PC Clusters at Hong Kong Baptist University. The LD maps are constructed for four populations from Hapmap data sets with chromosomes of several ten thousand Single Nucleotide Polymorphisms (SNPs). The similarities and dissimilarities of the LD maps are studied and analysed. Computational results are also reported to show the effectiveness of the method using parallel computation.
A Hybrid Index for Characterizing Drought Based on a Nonparametric Kernel Estimator
Energy Technology Data Exchange (ETDEWEB)
Huang, Shengzhi; Huang, Qiang; Leng, Guoyong; Chang, Jianxia
2016-06-01
This study develops a nonparametric multivariate drought index, namely, the Nonparametric Multivariate Standardized Drought Index (NMSDI), by considering the variations of both precipitation and streamflow. Building upon previous efforts in constructing Nonparametric Multivariate Drought Index, we use the nonparametric kernel estimator to derive the joint distribution of precipitation and streamflow, thus providing additional insights in drought index development. The proposed NMSDI are applied in the Wei River Basin (WRB), based on which the drought evolution characteristics are investigated. Results indicate: (1) generally, NMSDI captures the drought onset similar to Standardized Precipitation Index (SPI) and drought termination and persistence similar to Standardized Streamflow Index (SSFI). The drought events identified by NMSDI match well with historical drought records in the WRB. The performances are also consistent with that by an existing Multivariate Standardized Drought Index (MSDI) at various timescales, confirming the validity of the newly constructed NMSDI in drought detections (2) An increasing risk of drought has been detected for the past decades, and will be persistent to a certain extent in future in most areas of the WRB; (3) the identified change points of annual NMSDI are mainly concentrated in the early 1970s and middle 1990s, coincident with extensive water use and soil reservation practices. This study highlights the nonparametric multivariable drought index, which can be used for drought detections and predictions efficiently and comprehensively.
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.
Significant Linkage of Parkinson Disease to Chromosome 2q36-37
Pankratz, Nathan; Nichols, William C.; Uniacke, Sean K.; Halter, Cheryl; Rudolph, Alice; Shults, Cliff; Conneally, P. Michael; Foroud, Tatiana
2003-01-01
Parkinson disease (PD) is the second most common neurodegenerative disorder, surpassed in frequency only by Alzheimer disease. Elsewhere we have reported linkage to chromosome 2q in a sample of sibling pairs with PD. We have now expanded our sample to include 150 families meeting our strictest diagnostic definition of verified PD. To further delineate the chromosome 2q linkage, we have performed analyses using only those pedigrees with the strongest family history of PD. Linkage analyses in this subset of 65 pedigrees generated a LOD score of 5.1, which was obtained using an autosomal dominant model of disease transmission. This result strongly suggests that variation in a gene on chromosome 2q36-37 contributes to PD susceptibility. PMID:12638082
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...... updating approach and be integrated in the reliability analysis by a third-order polynomial chaos expansion approximation. Although Classical Bayesian updating approaches are often used because of its parametric formulation, non-parametric approaches are better alternatives for multi-parametric updating...... with a non-conjugating formulation. The results in this paper show the influence on the time dependent updated reliability when non-parametric and classical Bayesian approaches are used. Further, the influence on the reliability of the number of updated parameters is illustrated....
Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures
Li, Quanbao; Wei, Fajie; Zhou, Shenghan
2017-05-01
The linear discriminant analysis (LDA) is one of popular means for linear feature extraction. It usually performs well when the global data structure is consistent with the local data structure. Other frequently-used approaches of feature extraction usually require linear, independence, or large sample condition. However, in real world applications, these assumptions are not always satisfied or cannot be tested. In this paper, we introduce an adaptive method, local kernel nonparametric discriminant analysis (LKNDA), which integrates conventional discriminant analysis with nonparametric statistics. LKNDA is adept in identifying both complex nonlinear structures and the ad hoc rule. Six simulation cases demonstrate that LKNDA have both parametric and nonparametric algorithm advantages and higher classification accuracy. Quartic unilateral kernel function may provide better robustness of prediction than other functions. LKNDA gives an alternative solution for discriminant cases of complex nonlinear feature extraction or unknown feature extraction. At last, the application of LKNDA in the complex feature extraction of financial market activities is proposed.
Non-parametric seismic hazard analysis in the presence of incomplete data
Yazdani, Azad; Mirzaei, Sajjad; Dadkhah, Koroush
2017-01-01
The distribution of earthquake magnitudes plays a crucial role in the estimation of seismic hazard parameters. Due to the complexity of earthquake magnitude distribution, non-parametric approaches are recommended over classical parametric methods. The main deficiency of the non-parametric approach is the lack of complete magnitude data in almost all cases. This study aims to introduce an imputation procedure for completing earthquake catalog data that will allow the catalog to be used for non-parametric density estimation. Using a Monte Carlo simulation, the efficiency of introduced approach is investigated. This study indicates that when a magnitude catalog is incomplete, the imputation procedure can provide an appropriate tool for seismic hazard assessment. As an illustration, the imputation procedure was applied to estimate earthquake magnitude distribution in Tehran, the capital city of Iran.
Oedegaard, K J; Greenwood, T A; Lunde, A; Fasmer, O B; Akiskal, H S; Kelsoe, J R
2010-04-01
Migraine and Bipolar Disorder (BPAD) are clinically heterogeneous disorders of the brain with a significant, but complex, genetic component. Epidemiological and clinical studies have demonstrated a high degree of co-morbidity between migraine and BPAD. Several genome-wide linkage studies in BPAD and migraine have shown overlapping regions of linkage on chromosomes, and two functionally similar voltage-dependent calcium channels CACNA1A and CACNA1C have been identified in familial hemiplegic migraine and recently implicated in two whole genome BPAD association studies, respectively. We hypothesized that using migraine co-morbidity to look at subsets of BPAD families in a genetic linkage analysis would prove useful in identifying genetic susceptibility regions in both of these disorders. We used BPAD with co-morbid migraine as an alternative phenotype definition in a re-analysis of the NIMH Bipolar Genetics Initiative wave 4 data set. In this analysis we selected only those families in which at least two members were diagnosed with migraine by a doctor according to patients' reports. Nonparametric linkage analysis performed on 31 families segregating both BPAD and migraine identified a linkage signal on chromosome 4q24 for migraine (but not BPAD) with a peak LOD of 2.26. This region has previously been implicated in two independent migraine linkage studies. In addition we identified a locus on chromosome 20p11 with overlapping elevated LOD scores for both migraine (LOD=1.95) and BPAD (LOD=1.67) phenotypes. This region has previously been implicated in two BPAD linkage studies, and, interestingly, it harbors a known potassium dependant sodium/calcium exchanger gene, SLC24A3, that plays a critical role in neuronal calcium homeostasis. Our findings replicate a previously identified migraine linkage locus on chromosome 4 (not co-segregating with BPAD) in a sample of BPAD families with co-morbid migraine, and suggest a susceptibility locus on chromosome 20, harboring a
Comparing linkage designs based on land facets to linkage designs based on focal species.
Brost, Brian M; Beier, Paul
2012-01-01
Least-cost modeling for focal species is the most widely used method for designing conservation corridors and linkages. However, these designs depend on today's land covers, which will be altered by climate change. We recently proposed an alternative approach based on land facets (recurring landscape units of relatively uniform topography and soils). The rationale is that corridors with high continuity of individual land facets will facilitate movement of species associated with each facet today and in the future. Conservation practitioners might like to know whether a linkage design based on land facets is likely to provide continuity of modeled breeding habitat for species needing connectivity today, and whether a linkage for focal species provides continuity and interspersion of land facets. To address these questions, we compared linkages designed for focal species and land facets in three landscapes in Arizona, USA. We used two variables to measure linkage utility, namely distances between patches of modeled breeding habitat for 5-16 focal species in each linkage, and resistance profiles for focal species and land facets between patches connected by the linkage. Compared to focal species designs, linkage designs based on land facets provided as much or more modeled habitat connectivity for 25 of 28 species-landscape combinations, failing only for the three species with the most narrowly distributed habitat. Compared to land facets designs, focal species linkages provided lower connectivity for about half the land facets in two landscapes. In areas where a focal species approach to linkage design is not possible, our results suggest that conservation practitioners may be able to implement a land facets approach with some confidence that the linkage design would serve most potential focal species. In areas where focal species designs are possible, we recommend using the land facet approach to complement, rather than replace, focal species approaches.
Gao, Oliver H.; Holmén, Britt A.; Niemeier, Debbie A.
The Ozone Weekend Effect (OWE) has become increasingly more frequent and widespread in southern California since the mid-1970s. Although a number of hypotheses have been suggested to explain the effect, there remains uncertainty associated with the root factors contributing to elevated weekend ozone concentrations. Targeting the time window of the 1997 Southern California Ozone Study (SCOS97), this paper examines traffic activity data for 14 vehicle classes at 27 weigh-in-motion (WIM) stations in southern California. Nonparametric factorial analyses of light-duty vehicle (LDV) and heavy-duty truck (HDT) traffic volumes indicate significant differences in daily volumes by day of week and between the weekly patterns of daily LDV and HDT volumes. Across WIM stations, the daily LDV volume was highest on Friday and decreased by 10% on weekends compared to that on midweek days. In contrast, daily HDT volumes showed dramatic weekend drops of 53% on Saturday and 64% on Sunday. As a result, LDV to HDT ratios increased by 145% on weekends. Nonparametric tests also suggest that weekly traffic patterns varied significantly between WIM stations located close to (central) and far from (peripheral) the Los Angeles Metro area. Weekend increases in LDV/HDT ratios were more pronounced at central WIM sites due to greater weekend declines of HDT relative to LDV traffic. The implications of these weekly traffic patterns for the OWE in southern California were investigated by estimating daily WIM traffic on-road running exhaust emissions of total organic gas (TOG) and oxides of nitrogen (NO x) using EMFAC2002 emission factors. The results support the California Air Resource Board's (CARB's) NO x reduction hypothesis that greater weekend NO x reductions relative to volatile organic compound (VOC) emissions, in combinations with the VOC-limited ozone system, contribute to the OWE observed in the region. The results from this study can be used to develop weekend on-road mobile emission
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
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.
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.
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.
Parametrically guided estimation in nonparametric varying coefficient models with quasi-likelihood.
Davenport, Clemontina A; Maity, Arnab; Wu, Yichao
2015-04-01
Varying coefficient models allow us to generalize standard linear regression models to incorporate complex covariate effects by modeling the regression coefficients as functions of another covariate. For nonparametric varying coefficients, we can borrow the idea of parametrically guided estimation to improve asymptotic bias. In this paper, we develop a guided estimation procedure for the nonparametric varying coefficient models. Asymptotic properties are established for the guided estimators and a method of bandwidth selection via bias-variance tradeoff is proposed. We compare the performance of the guided estimator with that of the unguided estimator via both simulation and real data examples.
Genome-wide linkage scan for loci associated with epilepsy in Belgian shepherd dogs
Directory of Open Access Journals (Sweden)
Regan Kelly R
2010-05-01
Full Text Available Abstract Background Idiopathic epilepsy in the Belgian shepherd dog is known to have a substantial genetic component. The objective of this study was to identify genomic regions associated with the expression of generalized seizures in the Belgian Tervuren and Sheepdog. Results DNA from 366 dogs, of which 74 were classified as epileptic, representing two extended families were subjected to a genome-wide linkage scan using 410 microsatellite markers yielding informative coverage averaging 5.95 ± 0.21 Mb. Though previous studies based on pedigree analyses proposed a major gene of influence, the present study demonstrated the trait to be highly polygenic. Studies of complex disorders in humans indicate that a liberal composite evaluation of genetic linkage is needed to identify underlying quantitative trait loci (QTLs. Four chromosomes yielded tentative linkage based upon LOD scores in excess of 1.0. Possible QTLs within these regions were supported also by analyses of multipoint linkage, allele frequency, TDT, and transmission of haplotype blocks. Conclusions Taken together the data tentatively indicate six QTLs, three on CFA 2, and one on each of CFA 6, 12, and 37, that support fine mapping for mutations associated with epilepsy in the Belgian shepherd. The study also underscores the complexity of genomic linkage studies for polygenic disorders.
Institute of Scientific and Technical Information of China (English)
曾丽苹; 龙志高; 戴和平; 张灼华; 夏家辉; 赵靖平; 夏昆; 胡正茂; 穆莉莉; 梅桂森; 路秀玲; 郑永军; 李培建; 张瑛雪; 潘乾
2011-01-01
目的 探讨中国人群中精神分裂症亚型与1号染色体长臂1q21-25和6号染色体短臂6p21-25易感基因位点的相关性.方法 在染色体1q21-25区域中选择5个微卫星标记和6p21-25区域中选择8个微卫星标记对36个来自中国河南省的精神分裂症家系(19个偏执型和17个未分化型)中的242个个体进行基因分型及参数和非参数连锁分析.结果 36个精神分裂症家系的1号染色体参数分析时,在显性遗传模式下,D1S484得到多点异质性对数优势记分法(heterogeneity Log of odds score method,HLOD)值为1.33 (α=0.38).非参数分析时,在D1S484得到多点非参数连锁(nonparametric linkage,NPL)值为1.89(P=0.0188);D1S2878单点NPL值为2.11(P=0.0111),多点NPL值为2.41(P=0.0053);D1S196多点NPL值为1.59(P=0.0383).提示以上3个位点存在连锁.在17个未分化型家系中,D1S484多点NPL值为1.60(P=0.0367);D1S2878单点 NPL值为1.95(P=0.0145),多点NPL值为2.39(P=0.0041); D1S196多点NPL值为 1.74(P=0.0255).这与以上36个家系提示连锁的位点相同.在19个偏执型家系中,5个微卫星标记位点均未提示连锁.36个精神分裂症家系的6号染色体分析发现,除19个偏执型精神分裂症家系参数连锁分析在隐性模式下D6S289位点单点HLOD值为1.26(α=0.40),多点HLOD值为1.12(α=0.38)和非参数连锁分析在D6S289位点单点NPL值为1.52(P=0.0402),多点NPL值为1.92(P=0.0206)之外,36个精神分裂症家系总体分析和其中17个未分化型家系分型分析的结果显示8个微卫星标记位点均未提示有连锁.结论 在染色体1q23.3 和1q24.2区域可能存在与中国河南省未分化型精神分裂症相关的易感基因;在6p23区域可能存在与偏执型精神分裂症相关的易感基因.%Objective To investigate the relationship of susceptibility loci in chromosomes 1q21-25 and 6p21-25 and schizophrenia subtypes in Chinese population. Methods A genomic scan and parametric and non-parametric analyses
A linkage study between the GABAA beta2 and GABAA gamma2 subunit genes and major psychoses.
Ambrósio, Alda M; Kennedy, James L; Macciardi, Fabio; King, Nicole; Azevedo, Maria H; Oliveira, Catarina R; Pato, Carlos N
2005-01-01
Alterations of the gamma-aminobutyric acid (GABA) system have been implicated in the pathophysiology of major psychoses. Restriction fragment length polymorphisms associated with the human gamma-aminobutyric acid type A (GABAA) beta2 and GABAA gamma2 subunit genes on chromosome 5q32-q35 were tested to determine whether they confer susceptibility to major psychoses. Thirty-two schizophrenic families and 25 bipolar families were tested for linkage. Nonparametric linkage (NPL) analysis performed by GENEHUNTER showed no significant NPL scores for both genes in schizophrenia (GABAA beta2: NPL narrow= -0.450; NPL broad= -0.808; GABAA gamma2: NPL narrow=0.177; NPL broad= -0.051) or bipolar disorder (GABAA beta2: NPL narrow=0.834; NPL broad=0.783; GABAA gamma2: NPL narrow= -0.159; NPL broad=0.070). Linkage analysis does not support the hypothesis that variants within the GABAA beta2 and GABAA gamma2 genes are significantly linked to major psychoses in a Portuguese population.
Demographic histories and patterns of linkage disequilibrium in Chinese and Indian rhesus macaques
DEFF Research Database (Denmark)
Hernandez, Ryan D; Hubisz, Melissa J; Wheeler, David A;
2007-01-01
To understand the demographic history of rhesus macaques (Macaca mulatta) and document the extent of linkage disequilibrium (LD) in the genome, we partially resequenced five Encyclopedia of DNA Elements regions in 9 Chinese and 38 captive-born Indian rhesus macaques. Population genetic analyses o...
Jiang, GJ; Knight, JL
1997-01-01
In this paper, we propose a nonparametric identification and estimation procedure for an Ito diffusion process based on discrete sampling observations. The nonparametric kernel estimator for the diffusion function developed in this paper deals with general Ito diffusion processes and avoids any
Jiang, GJ; Knight, JL
1997-01-01
In this paper, we propose a nonparametric identification and estimation procedure for an Ito diffusion process based on discrete sampling observations. The nonparametric kernel estimator for the diffusion function developed in this paper deals with general Ito diffusion processes and avoids any func
Inter-organizational linkages and resource dependence
Directory of Open Access Journals (Sweden)
Rod B. McNaughton
2014-12-01
Full Text Available Few studies have examined the relationship between inter-industry, inter-corporate ownership (ICO patterns and inter-industry resource exchange patterns. Using data from Statistics Canada, this paper reveals a positive association between the degree of ICO linkages and the degree of input–output dependence among Canadian industry groups. This provides empirical support for the primary assertion of resource dependence theory: that corporations employ ICO linkages to manage their input–output dependence resulting from recurrent resource exchanges. This research differs from extant tests of resource dependence in that it uses data for the population of firms (over a size threshold in Canada and includes all forms of interdependence between enterprises. The findings suggest scenarios in which corporations can adopt ICO linkages to manage resource dependence and reduce transaction costs.
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
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.
Nonparametric estimation of population density for line transect sampling using FOURIER series
Crain, B.R.; Burnham, K.P.; Anderson, D.R.; Lake, J.L.
1979-01-01
A nonparametric, robust density estimation method is explored for the analysis of right-angle distances from a transect line to the objects sighted. The method is based on the FOURIER series expansion of a probability density function over an interval. With only mild assumptions, a general population density estimator of wide applicability is obtained.
A non-parametric peak finder algorithm and its application in searches for new physics
Chekanov, S
2011-01-01
We have developed an algorithm for non-parametric fitting and extraction of statistically significant peaks in the presence of statistical and systematic uncertainties. Applications of this algorithm for analysis of high-energy collision data are discussed. In particular, we illustrate how to use this algorithm in general searches for new physics in invariant-mass spectra using pp Monte Carlo simulations.
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...
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...
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...
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 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 p...
The Probability of Exceedance as a Nonparametric Person-Fit Statistic for Tests of Moderate Length
Tendeiro, Jorge N.; Meijer, Rob R.
2013-01-01
To classify an item score pattern as not fitting a nonparametric item response theory (NIRT) model, the probability of exceedance (PE) of an observed response vector x can be determined as the sum of the probabilities of all response vectors that are, at most, as likely as x, conditional on the test
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...
Jang, Eunice Eunhee; Roussos, Louis
2007-01-01
This article reports two studies to illustrate methodologies for conducting a conditional covariance-based nonparametric dimensionality assessment using data from two forms of the Test of English as a Foreign Language (TOEFL). Study 1 illustrates how to assess overall dimensionality of the TOEFL including all three subtests. Study 2 is aimed at…
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.
Comparison of reliability techniques of parametric and non-parametric method
Directory of Open Access Journals (Sweden)
C. Kalaiselvan
2016-06-01
Full Text Available Reliability of a product or system is the probability that the product performs adequately its intended function for the stated period of time under stated operating conditions. It is function of time. The most widely used nano ceramic capacitor C0G and X7R is used in this reliability study to generate the Time-to failure (TTF data. The time to failure data are identified by Accelerated Life Test (ALT and Highly Accelerated Life Testing (HALT. The test is conducted at high stress level to generate more failure rate within the short interval of time. The reliability method used to convert accelerated to actual condition is Parametric method and Non-Parametric method. In this paper, comparative study has been done for Parametric and Non-Parametric methods to identify the failure data. The Weibull distribution is identified for parametric method; Kaplan–Meier and Simple Actuarial Method are identified for non-parametric method. The time taken to identify the mean time to failure (MTTF in accelerating condition is the same for parametric and non-parametric method with relative deviation.
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...
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 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 ...
A Comparison of Shewhart Control Charts based on Normality, Nonparametrics, and Extreme-Value Theory
Ion, R.A.; Does, R.J.M.M.; Klaassen, C.A.J.
2000-01-01
Several control charts for individual observations are compared. The traditional ones are the well-known Shewhart control charts with estimators for the spread based on the sample standard deviation and the average of the moving ranges. The alternatives are nonparametric control charts, based on emp
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
An Assessment of the Nonparametric Approach for Evaluating the Fit of Item Response Models
Liang, Tie; Wells, Craig S.; Hambleton, Ronald K.
2014-01-01
As item response theory has been more widely applied, investigating the fit of a parametric model becomes an important part of the measurement process. There is a lack of promising solutions to the detection of model misfit in IRT. Douglas and Cohen introduced a general nonparametric approach, RISE (Root Integrated Squared Error), for detecting…
Agasisti, Tommaso
2011-01-01
The objective of this paper is an efficiency analysis concerning higher education systems in European countries. Data have been extracted from OECD data-sets (Education at a Glance, several years), using a non-parametric technique--data envelopment analysis--to calculate efficiency scores. This paper represents the first attempt to conduct such an…
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Additive Models.
Fan, Jianqing; Feng, Yang; Song, Rui
2011-06-01
A variable screening procedure via correlation learning was proposed in Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonparametric learning. Our nonparametric independence screening is called NIS, a specific member of the sure independence screening. Several closely related variable screening procedures are proposed. Under general nonparametric models, it is shown that under some mild technical conditions, the proposed independence screening methods enjoy a sure screening property. The extent to which the dimensionality can be reduced by independence screening is also explicitly quantified. As a methodological extension, a data-driven thresholding and an iterative nonparametric independence screening (INIS) are also proposed to enhance the finite sample performance for fitting sparse additive models. The simulation results and a real data analysis demonstrate that the proposed procedure works well with moderate sample size and large dimension and performs better than competing methods.
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Varying Coefficient Models.
Fan, Jianqing; Ma, Yunbei; Dai, Wei
2014-01-01
The varying-coefficient model is an important class of nonparametric statistical model that allows us to examine how the effects of covariates vary with exposure variables. When the number of covariates is large, the issue of variable selection arises. In this paper, we propose and investigate marginal nonparametric screening methods to screen variables in sparse ultra-high dimensional varying-coefficient models. The proposed nonparametric independence screening (NIS) selects variables by ranking a measure of the nonparametric marginal contributions of each covariate given the exposure variable. The sure independent screening property is established under some mild technical conditions when the dimensionality is of nonpolynomial order, and the dimensionality reduction of NIS is quantified. To enhance the practical utility and finite sample performance, two data-driven iterative NIS methods are proposed for selecting thresholding parameters and variables: conditional permutation and greedy methods, resulting in Conditional-INIS and Greedy-INIS. The effectiveness and flexibility of the proposed methods are further illustrated by simulation studies and real data applications.
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...
Do Former College Athletes Earn More at Work? A Nonparametric Assessment
Henderson, Daniel J.; Olbrecht, Alexandre; Polachek, Solomon W.
2006-01-01
This paper investigates how students' collegiate athletic participation affects their subsequent labor market success. By using newly developed techniques in nonparametric regression, it shows that on average former college athletes earn a wage premium. However, the premium is not uniform, but skewed so that more than half the athletes actually…
Nonparametric Tests of Collectively Rational Consumption Behavior : An Integer Programming Procedure
Cherchye, L.J.H.; de Rock, B.; Sabbe, J.; Vermeulen, F.M.P.
2008-01-01
We present an IP-based nonparametric (revealed preference) testing proce- dure for rational consumption behavior in terms of general collective models, which include consumption externalities and public consumption. An empiri- cal application to data drawn from the Russia Longitudinal Monitoring
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...
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...
Wei, Jiawei; Carroll, Raymond J; Maity, Arnab
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.
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
Candidate region linkage analysis in twins discordant or concordant for depression symptomatology
DEFF Research Database (Denmark)
Christiansen, Lene; Tan, Q; Kruse, T A
2009-01-01
Genetic risk factors contribute considerably to both clinical affective disorders and subsyndromal mood level. There is moreover evidence to suggest that the genetic basis of bipolar disorder and unipolar depression overlap to some extent, and several linkage analyses have suggested evidence...... for a common susceptibility locus in affective disorders on chromosome 12q24. In this study we investigated the chromosome 12 candidate region for linkage to the mean level of depression symptomatology, over a 10-year follow-up, using a highly informative sample of concordant and discordant twin pairs selected...
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...
Detection of tandam duplications and implications for linkage analysis
Energy Technology Data Exchange (ETDEWEB)
Matise, T.C.; Weeks, D.E. (Univ. of Pittsburgh, PA (United States)); Chakravarti, A. (Case Western Reserve Univ., Cleveland, OH (United States)); Patel, P.I.; Lupski, J.R. (Baylor College of Medicine, Houston, TX (United States)); Nelis, E.; Timmerman, V.; Van Broeckhoven, C. (Univ. of Antwerp (Belgium))
1994-06-01
The first demonstration of an autosomal dominant human disease caused by segmental trisomy came in 1991 for Charcot-Marie-Tooth disease type 1A (CMT1A). For this disorder, the segmental trisomy is due to a large tandem duplication of 1.5 Mb of DNA located on chromosome 17p11.2-p12. The search for the CMT1A disease gene was misdirected and impeded because some chromosome 17 genetic markers that are linked to CMT1A lie within this duplication. To better understand how such a duplication might affect genetic analyses in the context of disease gene mapping, the authors studied the effects of marker duplication on transmission probabilities of marker alleles, on linkage analysis of an autosomal dominant disease, and on tests of linkage homogeneity. They demonstrate that the undetected presence of a duplication distorts transmission ratios, hampers fine localization of the disease gene, and increases false evidence of linkage heterogeneity. In addition, they devised a likelihood-based method for detecting the presence of a tandemly duplicated marker when one is suspected. They tested their methods through computer simulations and on CMT1A pedigrees genotyped at several chromosome 17 markers. On the simulated data, the method detected 96% of duplicated markers (with a false-positive rate of 5%). On the CMT1A data the method successfully identified two of three loci that are duplicated (with no false positives). This method could be used to identify duplicated markers in other regions of the genome and could be used to delineate the extent of duplications similar to that involved in CMT1A. 18 refs., 5 figs., 6 tabs.
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.
Genome-wide family-based linkage analysis of exome chip variants and cardiometabolic risk.
Hellwege, Jacklyn N; Palmer, Nicholette D; Raffield, Laura M; Ng, Maggie C Y; Hawkins, Gregory A; Long, Jirong; Lorenzo, Carlos; Norris, Jill M; Ida Chen, Y-D; Speliotes, Elizabeth K; Rotter, Jerome I; Langefeld, Carl D; Wagenknecht, Lynne E; Bowden, Donald W
2014-05-01
Linkage analysis of complex traits has had limited success in identifying trait-influencing loci. Recently, coding variants have been implicated as the basis for some biomedical associations. We tested whether coding variants are the basis for linkage peaks of complex traits in 42 African-American (n = 596) and 90 Hispanic (n = 1,414) families in the Insulin Resistance Atherosclerosis Family Study (IRASFS) using Illumina HumanExome Beadchips. A total of 92,157 variants in African Americans (34%) and 81,559 (31%) in Hispanics were polymorphic and tested using two-point linkage and association analyses with 37 cardiometabolic phenotypes. In African Americans 77 LOD scores greater than 3 were observed. The highest LOD score was 4.91 with the APOE SNP rs7412 (MAF = 0.13) with plasma apolipoprotein B (ApoB). This SNP was associated with ApoB (P-value = 4 × 10(-19)) and accounted for 16.2% of the variance in African Americans. In Hispanic families, 104 LOD scores were greater than 3. The strongest evidence of linkage (LOD = 4.29) was with rs5882 (MAF = 0.46) in CETP with HDL. CETP variants were strongly associated with HDL (0.00049 evidence of strong linkage in this genome wide survey of primarily coding variants was uncommon. Loci with strong evidence of linkage was characterized by large contributions to the variance, and, in these cases, are common variants. Less compelling evidence of linkage and association was observed with additional loci that may require larger family sets to confirm. © 2014 WILEY PERIODICALS, INC.
Producer Services, Manufacturing Linkages, and Trade
J.F. François (Joseph); J. Kepler; J. Woerz
2007-01-01
textabstractWorking with a mix of panel data on goods and services trade for the OECD for 1994-2004, combined with social accounts data (i.e. data on intermediate linkages) for 78 countries benchmarked to the panel midpoint, we examine the role of services as inputs in manufacturing, with a particul
The Barley Chromosome 5 Linkage Map
DEFF Research Database (Denmark)
Jensen, J.; Jørgensen, Jørgen Helms
1975-01-01
: wst5 (white streaks), necl (necrotic leaf spots), Ml-nn (powdery mildew resistance), and Pa4 (leaf rust resistance). Further, the two sections of the map are united, and the precision of the map is improved. A system for designating the positions of the loci on the linkage map is proposed. A 0...
Linkage between sexual orientation and chromosome Xq28 in males but not in females.
Hu, S; Pattatucci, A M; Patterson, C; Li, L; Fulker, D W; Cherny, S S; Kruglyak, L; Hamer, D H
1995-11-01
We have extended our analysis of the role of the long arm of the X chromosome (Xq28) in sexual orientation by DNA linkage analyses of two newly ascertained series of families that contained either two gay brothers or two lesbian sisters as well as heterosexual siblings. Linkage between the Xq28 markers and sexual orientation was detected for the gay male families but not for the lesbian families or for families that failed to meet defined inclusion criteria for the study of sex-linked sexual orientation. Our results corroborate the previously reported linkage between Xq28 and male homosexuality in selected kinships and suggest that this region contains a locus that influences individual variations in sexual orientation in men but not in women.
DEFF Research Database (Denmark)
Mckinney, G. J.; Seeb, L. W.; Larson, W. A.
2016-01-01
Salmonids are an important cultural and ecological resource exhibiting near worldwide distribution between their native and introduced range. Previous research has generated linkage maps and genomic resources for several species as well as genome assemblies for two species. We first leveraged...... improvements in mapping and genotyping methods to create a dense linkage map for Chinook salmon Oncorhynchus tshawytscha by assembling family data from different sources. We successfully mapped 14 620 SNP loci including 2336 paralogs in subtelomeric regions. This improved map was then used as a foundation...... to integrate genomic resources for gene annotation and population genomic analyses. We anchored a total of 286 scaffolds from the Atlantic salmon genome to the linkage map to provide a framework for the placement 11 728 Chinook salmon ESTs. Previously identified thermotolerance QTL were found to colocalize...
Pato, Carlos N; Middleton, Frank A; Gentile, Karen L; Morley, Christopher P; Medeiros, Helena; Macedo, Antonio; Azevedo, M Helena; Pato, Michele T
2005-04-05
We recently reported genome-wide significant linkage to chromosome 6q for bipolar disorder, in a study of 25 Portuguese families, using the Human Mapping Assay Xba 131 (HMA10K). To explore the generalizability of this finding, we reanalyzed our SNP linkage data according to the families' geographic origin. Specifically, the 25 families included 20 families from the Portuguese island collection (PIC; 15 families from the Azores Islands and 5 from the Madeira Islands) and 5 families from continental Portugal. Non-parametric linkage analysis (NPL) was performed as previously described and indicated that each of these subpopulations showed evidence of linkage for the same region. To further address the potential generalizability of these findings to other populations, we have also examined allelic heterozygosity in our subpopulations and in three reference populations (Caucasian, East Asian, and African-American). This analysis indicated that the PIC population is highly correlated to the Caucasian reference population (R = 0.86) for all of chromosome 6. In contrast allelic heterozygosity was more weakly correlated between PIC and both East Asian (R = 0.37) and African-American (R = 0.32) reference populations. Taken together these observations suggest a shared genetic liability among Portuguese populations for bipolar disorder on chromosome 6q, and that the PIC population is likely representative of Caucasians in general. Copyright 2005 Wiley-Liss, Inc.
Linkage studies on Gilles de la Tourette syndrome: what is the strategy of choice?
Heutink, P; van de Wetering, B J; Pakstis, A J; Kurlan, R; Sandor, P; Oostra, B A; Sandkuijl, L A
1995-08-01
For a linkage study it is important to ascertain family material that is sufficiently informative. The statistical power of a 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 suggest 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.
Path analytic, sib-pair linkage and co-twin control studies of asthma and atopy
Energy Technology Data Exchange (ETDEWEB)
Duffy, D.L.; Healey, S.C.; Martin, N.G. [Queensland Institute of Medical Research, Brisband (Austria)
1994-09-01
Asthma and atopy are complex traits with multifactorial determinants, and require appropriate choice of phenotypes and analyses, including a linkage analysis of the putative 11q atopy locus. Participants in a large registry-based twin study of asthma were invited to take part in clinical testing. A total of 863 individuals including 419 complete twin pairs (where one or both members reported a history of wheeze) underwent histamine inhalation challenge, allergen skin prick testing, and venesection. Total serum immunoglobulin E (IgE) and bronchial responsiveness (BR) to histamine were highest in those who had wheezed most recently, and whose skin tests demonstrated allergy to house dust mite, cockroach, and rye grass. In ascertainment-corrected path analyses (FISHER), the heritability of IgE and BR were both 60%. Monozygotic (MZ) co-twin control analyses suggested house dust mite sensitization was the single strongest environmentally controlled risk factor for wheeze, while path analyses suggested genetic determination. In dizygotic (DZ) co-twin control analyses, sensitization to grasses was also an important predictor, suggesting pollinosis to be genetically correlated with wheezing, rather than causative. Multivariate path analyses suggested separate (correlated) genetic factors for BR, IgE, and allergy to house dust mite. A sib-pair (Haseman-Elston) linkage analysis of 220 DZ twin pairs did not support linkage to the high-affinity IgE receptor beta-subunit gene on 11q13 of atopy or BR. More recent linkage analyses that include parental genotyping will also be discussed. We conclude that the atopic phenotype consists of a number of traits with specific genetic allergens. Exposure to particular allergens can then cause specific outcomes, such as asthma.
The effects of selection on linkage analysis for quantitative traits.
Mackinnon, M J; Georges, M A
1992-12-01
The effects of within-sample selection on the outcome of analyses detecting linkage between genetic markers and quantitative traits were studied. It was found that selection by truncation for the trait of interest significantly reduces the differences between marker genotype means thus reducing the power to detect linked quantitative trait loci (QTL). The size of this reduction is a function of proportion selected, the magnitude of the QTL effect, recombination rate between the marker locus and the QTL, and the allele frequency of the QTL. Proportion selected was the most influential of these factors on bias, e.g., for an allele substitution effect of one standard deviation unit, selecting the top 80%, 50% or 20% of the population required 2, 6 or 24 times the number of progeny, respectively, to offset the loss of power caused by this selection. The effect on power was approximately linear with respect to the size of gene effect, almost invariant to recombination rate, and a complex function of QTL allele frequency. It was concluded that experimental samples from animal populations which have been subjected to even minor amounts of selection will be inefficient in yielding information on linkage between markers and loci influencing the quantitative trait under selection.
Linkage disequilibrium interval mapping of quantitative trait loci
Directory of Open Access Journals (Sweden)
de Rochambeau Hubert
2006-03-01
Full Text Available Abstract Background For many years gene mapping studies have been performed through linkage analyses based on pedigree data. Recently, linkage disequilibrium methods based on unrelated individuals have been advocated as powerful tools to refine estimates of gene location. Many strategies have been proposed to deal with simply inherited disease traits. However, locating quantitative trait loci is statistically more challenging and considerable research is needed to provide robust and computationally efficient methods. Results Under a three-locus Wright-Fisher model, we derived approximate expressions for the expected haplotype frequencies in a population. We considered haplotypes comprising one trait locus and two flanking markers. Using these theoretical expressions, we built a likelihood-maximization method, called HAPim, for estimating the location of a quantitative trait locus. For each postulated position, the method only requires information from the two flanking markers. Over a wide range of simulation scenarios it was found to be more accurate than a two-marker composite likelihood method. It also performed as well as identity by descent methods, whilst being valuable in a wider range of populations. Conclusion Our method makes efficient use of marker information, and can be valuable for fine mapping purposes. Its performance is increased if multiallelic markers are available. Several improvements can be developed to account for more complex evolution scenarios or provide robust confidence intervals for the location estimates.
Energy Technology Data Exchange (ETDEWEB)
Figlewicz, D.A. [Univ. of Rochester, NY (United States); Dube, M.P.; Rouleau, G.A. [McGill Univ., Montreal (Canada)] [and others
1994-09-01
Familial spastic paraplegia (FSP) is a degenerative disorder of the motor system characterized by progressive weakness and spasticity of the lower limbs. Little is known about the pathophysiology of this disorder. FSP can be inherited as an autosomal dominant (AD), autosomal recessive, or X-linked trait. We have undertaken linkage analysis for a group of 36 AD FSP families from which we have collected blood samples from 427 individuals, including 148 affected individuals. Typing of polymorphic markers has allowed us to exclude more than 50% of the genome. Recently, linkage for AD FSP to a locus on chromosome 14q was reported. Our AD FSP kindreds were tested for linkage to markers spanning the 20 cM region between D14S69 and D14S66; however, we were not able to establish linkage for any of our families to chromosome 14. Lod scores suggestive of linkage for some AD FSP kindreds have been obtained for markers on chromosome 2p. We have tested seven polymorphic markers spanning the region between D2S405 and D2S177. Our highest aggregate lod score, including all families tested, was obtained at the locus D2S352: 2.4 at 20 cM. Results from HOMOG analysis for linkage heterogeneity will be reported.
Spline Nonparametric Regression Analysis of Stress-Strain Curve of Confined Concrete
Directory of Open Access Journals (Sweden)
Tavio Tavio
2008-01-01
Full Text Available Due to enormous uncertainties in confinement models associated with the maximum compressive strength and ductility of concrete confined by rectilinear ties, the implementation of spline nonparametric regression analysis is proposed herein as an alternative approach. The statistical evaluation is carried out based on 128 large-scale column specimens of either normal-or high-strength concrete tested under uniaxial compression. The main advantage of this kind of analysis is that it can be applied when the trend of relation between predictor and response variables are not obvious. The error in the analysis can, therefore, be minimized so that it does not depend on the assumption of a particular shape of the curve. This provides higher flexibility in the application. The results of the statistical analysis indicates that the stress-strain curves of confined concrete obtained from the spline nonparametric regression analysis proves to be in good agreement with the experimental curves available in literatures
Non-parametric Bayesian human motion recognition using a single MEMS tri-axial accelerometer.
Ahmed, M Ejaz; Song, Ju Bin
2012-09-27
In this paper, we propose a non-parametric clustering method to recognize the number of human motions using features which are obtained from a single microelectromechanical system (MEMS) accelerometer. Since the number of human motions under consideration is not known a priori and because of the unsupervised nature of the proposed technique, there is no need to collect training data for the human motions. The infinite Gaussian mixture model (IGMM) and collapsed Gibbs sampler are adopted to cluster the human motions using extracted features. From the experimental results, we show that the unanticipated human motions are detected and recognized with significant accuracy, as compared with the parametric Fuzzy C-Mean (FCM) technique, the unsupervised K-means algorithm, and the non-parametric mean-shift method.
Non-Parametric Bayesian Human Motion Recognition Using a Single MEMS Tri-Axial Accelerometer
Directory of Open Access Journals (Sweden)
M. Ejaz Ahmed
2012-09-01
Full Text Available In this paper, we propose a non-parametric clustering method to recognize the number of human motions using features which are obtained from a single microelectromechanical system (MEMS accelerometer. Since the number of human motions under consideration is not known a priori and because of the unsupervised nature of the proposed technique, there is no need to collect training data for the human motions. The infinite Gaussian mixture model (IGMM and collapsed Gibbs sampler are adopted to cluster the human motions using extracted features. From the experimental results, we show that the unanticipated human motions are detected and recognized with significant accuracy, as compared with the parametric Fuzzy C-Mean (FCM technique, the unsupervised K-means algorithm, and the non-parametric mean-shift method.
Applications of non-parametric statistics and analysis of variance on sample variances
Myers, R. H.
1981-01-01
Nonparametric methods that are available for NASA-type applications are discussed. An attempt will be made here to survey what can be used, to attempt recommendations as to when each would be applicable, and to compare the methods, when possible, with the usual normal-theory procedures that are avavilable for the Gaussion analog. It is important here to point out the hypotheses that are being tested, the assumptions that are being made, and limitations of the nonparametric procedures. The appropriateness of doing analysis of variance on sample variances are also discussed and studied. This procedure is followed in several NASA simulation projects. On the surface this would appear to be reasonably sound procedure. However, difficulties involved center around the normality problem and the basic homogeneous variance assumption that is mase in usual analysis of variance problems. These difficulties discussed and guidelines given for using the methods.
Testing the Non-Parametric Conditional CAPM in the Brazilian Stock Market
Directory of Open Access Journals (Sweden)
Daniel Reed Bergmann
2014-04-01
Full Text Available This paper seeks to analyze if the variations of returns and systematic risks from Brazilian portfolios could be explained by the nonparametric conditional Capital Asset Pricing Model (CAPM by Wang (2002. There are four informational variables available to the investors: (i the Brazilian industrial production level; (ii the broad money supply M4; (iii the inflation represented by the Índice de Preços ao Consumidor Amplo (IPCA; and (iv the real-dollar exchange rate, obtained by PTAX dollar quotation.This study comprised the shares listed in the BOVESPA throughout January 2002 to December 2009. The test methodology developed by Wang (2002 and retorted to the Mexican context by Castillo-Spíndola (2006 was used. The observed results indicate that the nonparametric conditional model is relevant in explaining the portfolios’ returns of the sample considered for two among the four tested variables, M4 and PTAX dollar at 5% level of significance.
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.
Stahel-Donoho kernel estimation for fixed design nonparametric regression models
Institute of Scientific and Technical Information of China (English)
LIN; Lu
2006-01-01
This paper reports a robust kernel estimation for fixed design nonparametric regression models.A Stahel-Donoho kernel estimation is introduced,in which the weight functions depend on both the depths of data and the distances between the design points and the estimation points.Based on a local approximation,a computational technique is given to approximate to the incomputable depths of the errors.As a result the new estimator is computationally efficient.The proposed estimator attains a high breakdown point and has perfect asymptotic behaviors such as the asymptotic normality and convergence in the mean squared error.Unlike the depth-weighted estimator for parametric regression models,this depth-weighted nonparametric estimator has a simple variance structure and then we can compare its efficiency with the original one.Some simulations show that the new method can smooth the regression estimation and achieve some desirable balances between robustness and efficiency.
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.
Directory of Open Access Journals (Sweden)
Mustafa Koroglu
2016-02-01
Full Text Available This paper considers a functional-coefficient spatial Durbin model with nonparametric spatial weights. Applying the series approximation method, we estimate the unknown functional coefficients and spatial weighting functions via a nonparametric two-stage least squares (or 2SLS estimation method. To further improve estimation accuracy, we also construct a second-step estimator of the unknown functional coefficients by a local linear regression approach. Some Monte Carlo simulation results are reported to assess the finite sample performance of our proposed estimators. We then apply the proposed model to re-examine national economic growth by augmenting the conventional Solow economic growth convergence model with unknown spatial interactive structures of the national economy, as well as country-specific Solow parameters, where the spatial weighting functions and Solow parameters are allowed to be a function of geographical distance and the countries’ openness to trade, respectively.
Saad, Walid; Poor, H Vincent; Başar, Tamer; Song, Ju Bin
2012-01-01
This paper introduces a novel approach that enables a number of cognitive radio devices that are observing the availability pattern of a number of primary users(PUs), to cooperate and use \\emph{Bayesian nonparametric} techniques to estimate the distributions of the PUs' activity pattern, assumed to be completely unknown. In the proposed model, each cognitive node may have its own individual view on each PU's distribution, and, hence, seeks to find partners having a correlated perception. To address this problem, a coalitional game is formulated between the cognitive devices and an algorithm for cooperative coalition formation is proposed. It is shown that the proposed coalition formation algorithm allows the cognitive nodes that are experiencing a similar behavior from some PUs to self-organize into disjoint, independent coalitions. Inside each coalition, the cooperative cognitive nodes use a combination of Bayesian nonparametric models such as the Dirichlet process and statistical goodness of fit techniques ...
非参数判别模型%Nonparametric discriminant model
Institute of Scientific and Technical Information of China (English)
谢斌锋; 梁飞豹
2011-01-01
提出了一类新的判别分析方法,主要思想是将非参数回归模型推广到判别分析中,形成相应的非参数判别模型.通过实例与传统判别法相比较,表明非参数判别法具有更广泛的适用性和较高的回代正确率.%In this paper, the author puts forth a new class of discriminant method, which the main idea is applied non- parametric regression model to discriminant analysis and forms the corresponding nonparametric discriminant model. Compared with the traditional discriminant methods by citing an example, the nonparametric discriminant method has more comprehensive adaptability and higher correct rate of back subsitution.
Non-Parametric Tests of Structure for High Angular Resolution Diffusion Imaging in Q-Space
Olhede, Sofia C
2010-01-01
High angular resolution diffusion imaging data is the observed characteristic function for the local diffusion of water molecules in tissue. This data is used to infer structural information in brain imaging. Non-parametric scalar measures are proposed to summarize such data, and to locally characterize spatial features of the diffusion probability density function (PDF), relying on the geometry of the characteristic function. Summary statistics are defined so that their distributions are, to first order, both independent of nuisance parameters and also analytically tractable. The dominant direction of the diffusion at a spatial location (voxel) is determined, and a new set of axes are introduced in Fourier space. Variation quantified in these axes determines the local spatial properties of the diffusion density. Non-parametric hypothesis tests for determining whether the diffusion is unimodal, isotropic or multi-modal are proposed. More subtle characteristics of white-matter microstructure, such as the degre...
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.
Floating Car Data Based Nonparametric Regression Model for Short-Term Travel Speed Prediction
Institute of Scientific and Technical Information of China (English)
WENG Jian-cheng; HU Zhong-wei; YU Quan; REN Fu-tian
2007-01-01
A K-nearest neighbor (K-NN) based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways, a specically designed database was developed via the processes including data filtering, wavelet analysis and clustering. The relativity based weighted Euclidean distance was used as the distance metric to identify the K groups of nearest data series. Then, a K-NN nonparametric regression model was built to predict the average travel speeds up to 6 min into the future. Several randomly selected travel speed data series,collected from the floating car data (FCD) system, were used to validate the model. The results indicate that using the FCD, the model can predict average travel speeds with an accuracy of above 90%, and hence is feasible and effective.
Spin Transfer in Polymer Degradation of Abnormal Linkage
Yu, Tianrong; Tian, Chuanjin; Liu, Xizhe; Wang, Jia; Gao, Yang; Wang, Zhigang
2016-09-01
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.
The CEPH consortium linkage map of human chromosome 13
Energy Technology Data Exchange (ETDEWEB)
Bowcock, A.M.; Barnes, R.I. [Univ. of Texas Southwestern Medical Center, Dallas, TX (United States); Gerken, S.C.; Leppert, M. [Univ. of Utah School of Medicine, Salt Lake City, UT (United States); Shiang, R. [Univ. of Iowa, Iowa City, IA (United States); Jabs, E.W.; Warren, A.C.; Antonarakis, S. [Johns Hopkins School of Medicine, Baltimore, MD (United States); Retief, A.E. [Univ. of Stellenbosch, Tygerberg (South Africa); Vergnaud, G. [Centre d`Etudes du Bouchet, Vert le Petit (France)] [and others
1993-05-01
The CEPH consortium map of chromosome 13 is presented. This map contains 59 loci defined by genotypes generated from CEPH family DNAs with 94 different probe and restriction enzyme combinations contributed by 9 laboratories. A total of 25 loci have been placed on the map with likelihood support of at least 1000:1. The map extends from loci in the centromeric region of chromosome 13 to the terminal band of the long arm. Multipoint linkage analyses provided estimates that the male, female, and sex-averaged maps extend for 158, 203, and 178cM respectively. The largest interval is 24 cM and is between D13Z1 (alphaRI) and ATP1AL1. The mean genetic distance between the 25 uniquely placed loci is 7 cM. 76 refs., 3 figs., 5 tabs.
Weak linkage between the heaviest rainfall and tallest storms.
Hamada, Atsushi; Takayabu, Yukari N; Liu, Chuntao; Zipser, Edward J
2015-02-24
Conventionally, the heaviest rainfall has been linked to the tallest, most intense convective storms. However, the global picture of the linkage between extreme rainfall and convection remains unclear. Here we analyse an 11-year record of spaceborne precipitation radar observations and establish that a relatively small fraction of extreme convective events produces extreme rainfall rates in any region of the tropics and subtropics. Robust differences between extreme rainfall and convective events are found in the rainfall characteristics and environmental conditions, irrespective of region; most extreme rainfall events are characterized by less intense convection with intense radar echoes not extending to extremely high altitudes. Rainfall characteristics and environmental conditions both indicate the importance of warm-rain processes in producing extreme rainfall rates. Our results demonstrate that, even in regions where severe convective storms are representative extreme weather events, the heaviest rainfall events are mostly associated with less intense convection.
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.
Middleton, F. A.; Pato, M. T.; Gentile, K. L.; Morley, C. P.; Zhao, X.; Eisener, A. F.; Brown, A.; Petryshen, T. L.; Kirby, A. N.; Medeiros, H.; Carvalho, C.; Macedo, A.; Dourado, A.; Coelho, I.; Valente, J.; Soares, M. J.; Ferreira, C. P.; Lei, M.; Azevedo, M. H.; Kennedy, J. L.; Daly, M. J.; Sklar, P.; Pato, C. N.
2004-01-01
We performed a linkage analysis on 25 extended multiplex Portuguese families segregating for bipolar disorder, by use of a high-density single-nucleotide–polymorphism (SNP) genotyping assay, the GeneChip Human Mapping 10K Array (HMA10K). Of these families, 12 were used for a direct comparison of the HMA10K with the traditional 10-cM microsatellite marker set and the more dense 4-cM marker set. This comparative analysis indicated the presence of significant linkage peaks in the SNP assay in chromosomal regions characterized by poor coverage and low information content on the microsatellite assays. The HMA10K provided consistently high information and enhanced coverage throughout these regions. Across the entire genome, the HMA10K had an average information content of 0.842 with 0.21-Mb intermarker spacing. In the 12-family set, the HMA10K-based analysis detected two chromosomal regions with genomewide significant linkage on chromosomes 6q22 and 11p11; both regions had failed to meet this strict threshold with the microsatellite assays. The full 25-family collection further strengthened the findings on chromosome 6q22, achieving genomewide significance with a maximum nonparametric linkage (NPL) score of 4.20 and a maximum LOD score of 3.56 at position 125.8 Mb. In addition to this highly significant finding, several other regions of suggestive linkage have also been identified in the 25-family data set, including two regions on chromosome 2 (57 Mb, NPL = 2.98; 145 Mb, NPL = 3.09), as well as regions on chromosomes 4 (91 Mb, NPL = 2.97), 16 (20 Mb, NPL = 2.89), and 20 (60 Mb, NPL = 2.99). We conclude that at least some of the linkage peaks we have identified may have been largely undetected in previous whole-genome scans for bipolar disorder because of insufficient coverage or information content, particularly on chromosomes 6q22 and 11p11. PMID:15060841
Multivariate linkage scan for metabolic syndrome traits in families with type 2 diabetes.
Edwards, Karen L; Wan, Jia Y; Hutter, Carolyn M; Fong, Pui Yee; Santorico, Stephanie A
2011-06-01
The purpose of this study was to evaluate evidence for linkage to interrelated quantitative features of the metabolic syndrome (MetS). Data on eight quantitative MetS traits (body weight, waist circumference, systolic and diastolic blood pressure, high-density lipoprotein (HDL) cholesterol, triglycerides (TGs), and fasting glucose and insulin measurements) and a 10 cM genome scan were available for 78 white families (n = 532 subjects). These data were used to conduct multipoint, multivariate linkage analyses, including tests for coincident linkage and complete pleiotropy. The strongest evidence for linkage from the bivariate analyses was observed on chromosome 1 (1p22.2) (HDL-TG; univariate lod score equivalent (lod(eq) = 3.99)) with stronger results from the trivariate analysis at the same location (HDL-TG-Insulin; lod(eq) = 4.32). Seven additional susceptibility regions (lod(eq) scores >1.9) were observed (1p36, 1q23, 2q21.2, 8q23.3, 14q23.2, 14q32.11, and 20p11.21). The results from this study indicate that several correlated traits of the MetS are influenced by the same gene(s) that account for some of the clustering of the MetS features.
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.
Directory of Open Access Journals (Sweden)
Andreas H. Melcher
2012-09-01
Full Text Available This study analyses multidimensional spawning habitat suitability of the fish species “Nase” (latin: Chondrostoma nasus. This is the first time non-parametric methods were used to better understand biotic habitat use in theory and practice. In particular, we tested (1 the Decision Tree technique, Chi-squared Automatic Interaction Detectors (CHAID, to identify specific habitat types and (2 Prediction-Configural Frequency Analysis (P-CFA to test for statistical significance. The combination of both non-parametric methods, CHAID and P-CFA, enabled the identification, prediction and interpretation of most typical significant spawning habitats, and we were also able to determine non-typical habitat types, e.g., types in contrast to antitypes. The gradual combination of these two methods underlined three significant habitat types: shaded habitat, fine and coarse substrate habitat depending on high flow velocity. The study affirmed the importance for fish species of shading and riparian vegetation along river banks. In addition, this method provides a weighting of interactions between specific habitat characteristics. The results demonstrate that efficient river restoration requires re-establishing riparian vegetation as well as the open river continuum and hydro-morphological improvements to habitats.
Variable selection in identification of a high dimensional nonlinear non-parametric system
Institute of Scientific and Technical Information of China (English)
Er-Wei BAI; Wenxiao ZHAO; Weixing ZHENG
2015-01-01
The problem of variable selection in system identification of a high dimensional nonlinear non-parametric system is described. The inherent difficulty, the curse of dimensionality, is introduced. Then its connections to various topics and research areas are briefly discussed, including order determination, pattern recognition, data mining, machine learning, statistical regression and manifold embedding. Finally, some results of variable selection in system identification in the recent literature are presented.
Estimating Financial Risk Measures for Futures Positions:A Non-Parametric Approach
Cotter, John; dowd, kevin
2011-01-01
This paper presents non-parametric estimates of spectral risk measures applied to long and short positions in 5 prominent equity futures contracts. It also compares these to estimates of two popular alternative measures, the Value-at-Risk (VaR) and Expected Shortfall (ES). The spectral risk measures are conditioned on the coefficient of absolute risk aversion, and the latter two are conditioned on the confidence level. Our findings indicate that all risk measures increase dramatically and the...
DEFF Research Database (Denmark)
Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H. C. A.
All business transactions as well as achieving innovations take up resources, subsumed under the concept of transaction costs (TAC). One of the major factors in TAC theory is information. Information networks can catalyse the interpersonal information exchange and hence, increase the access to no...... are unveiled by reduced productivity. A cross-validated local linear non-parametric regression shows that good information networks increase the productivity of farms. A bootstrapping procedure confirms that this result is statistically significant....
Asymmetry Effects in Chinese Stock Markets Volatility: A Generalized Additive Nonparametric Approach
Hou, Ai Jun
2007-01-01
The unique characteristics of the Chinese stock markets make it difficult to assume a particular distribution for innovations in returns and the specification form of the volatility process when modeling return volatility with the parametric GARCH family models. This paper therefore applies a generalized additive nonparametric smoothing technique to examine the volatility of the Chinese stock markets. The empirical results indicate that an asymmetric effect of negative news exists in the Chin...
Using a nonparametric PV model to forecast AC power output of PV plants
Almeida, Marcelo Pinho; Perpiñan Lamigueiro, Oscar; Narvarte Fernández, Luis
2015-01-01
In this paper, a methodology using a nonparametric model is used to forecast AC power output of PV plants using as inputs several forecasts of meteorological variables from a Numerical Weather Prediction (NWP) model and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quantile Regression Forests as machine learning tool to forecast the AC power with a confidence interval. Real data from five PV plants was used to validate the methodology, an...
An exact predictive recursion for Bayesian nonparametric analysis of incomplete data
Garibaldi, Ubaldo; Viarengo, Paolo
2010-01-01
This paper presents a new derivation of nonparametric distribution estimation with right-censored data. It is based on an extension of the predictive inferences to compound evidence. The estimate is recursive and exact, and no stochastic approximation is needed: it simply requires that the censored data are processed in decreasing order. Only in this case the recursion provides exact posterior predictive distributions for subsequent samples under a Dirichlet process prior. The resulting estim...
Varieties of religion-family linkages.
Snarey, J R; Dollahite, D C
2001-12-01
The 4 articles in this special issue make important contributions to both family and religious studies as well as to their interface. This commentary begins by considering 4 unifying themes present across all of the articles, including meaningful religion-family linkages, the importance of gender differences in the faith-family interface, the significance of intergenerational relationships, and the need for better theory. The authors then discuss the unique major strength and secondary limitations of each study. Finally, the commentary focuses on two challenges inhibiting the contemporary study of religion and the family--a relative lack of racial and religious diversity in samples and the lack of a unifying theory of religion-family linkages--and suggests how to adjust the trajectory of future theory and research to address these issues.
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.
t-tests, non-parametric tests, and large studies—a paradox of statistical practice?
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Fagerland Morten W
2012-06-01
Full Text Available Abstract Background During the last 30 years, the median sample size of research studies published in high-impact medical journals has increased manyfold, while the use of non-parametric tests has increased at the expense of t-tests. This paper explores this paradoxical practice and illustrates its consequences. Methods A simulation study is used to compare the rejection rates of the Wilcoxon-Mann-Whitney (WMW test and the two-sample t-test for increasing sample size. Samples are drawn from skewed distributions with equal means and medians but with a small difference in spread. A hypothetical case study is used for illustration and motivation. Results The WMW test produces, on average, smaller p-values than the t-test. This discrepancy increases with increasing sample size, skewness, and difference in spread. For heavily skewed data, the proportion of p Conclusions Non-parametric tests are most useful for small studies. Using non-parametric tests in large studies may provide answers to the wrong question, thus confusing readers. For studies with a large sample size, t-tests and their corresponding confidence intervals can and should be used even for heavily skewed data.
Nonparametric Kernel Smoothing Methods. The sm library in Xlisp-Stat
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Luca Scrucca
2001-06-01
Full Text Available In this paper we describe the Xlisp-Stat version of the sm library, a software for applying nonparametric kernel smoothing methods. The original version of the sm library was written by Bowman and Azzalini in S-Plus, and it is documented in their book Applied Smoothing Techniques for Data Analysis (1997. This is also the main reference for a complete description of the statistical methods implemented. The sm library provides kernel smoothing methods for obtaining nonparametric estimates of density functions and regression curves for different data structures. Smoothing techniques may be employed as a descriptive graphical tool for exploratory data analysis. Furthermore, they can also serve for inferential purposes as, for instance, when a nonparametric estimate is used for checking a proposed parametric model. The Xlisp-Stat version includes some extensions to the original sm library, mainly in the area of local likelihood estimation for generalized linear models. The Xlisp-Stat version of the sm library has been written following an object-oriented approach. This should allow experienced Xlisp-Stat users to implement easily their own methods and new research ideas into the built-in prototypes.
Kianisarkaleh, Azadeh; Ghassemian, Hassan
2016-09-01
Feature extraction plays a crucial role in improvement of hyperspectral images classification. Nonparametric feature extraction methods show better performance compared to parametric ones when distribution of classes is non normal-like. Moreover, they can extract more features than parametric methods do. In this paper, a new nonparametric linear feature extraction method is introduced for classification of hyperspectral images. The proposed method has no free parameter and its novelty can be discussed in two parts. First, neighbor samples are specified by using Parzen window idea for determining local mean. Second, two new weighting functions are used. Samples close to class boundaries will have more weight in the between-class scatter matrix formation and samples close to class mean will have more weight in the within-class scatter matrix formation. The experimental results on three real hyperspectral data sets, Indian Pines, Salinas and Pavia University, demonstrate that the proposed method has better performance in comparison with some other nonparametric and parametric feature extraction methods.
Non-parametric foreground subtraction for 21cm epoch of reionization experiments
Harker, Geraint; Bernardi, Gianni; Brentjens, Michiel A; De Bruyn, A G; Ciardi, Benedetta; Jelic, Vibor; Koopmans, Leon V E; Labropoulos, Panagiotis; Mellema, Garrelt; Offringa, Andre; Pandey, V N; Schaye, Joop; Thomas, Rajat M; Yatawatta, Sarod
2009-01-01
An obstacle to the detection of redshifted 21cm emission from the epoch of reionization (EoR) is the presence of foregrounds which exceed the cosmological signal in intensity by orders of magnitude. We argue that in principle it would be better to fit the foregrounds non-parametrically - allowing the data to determine their shape - rather than selecting some functional form in advance and then fitting its parameters. Non-parametric fits often suffer from other problems, however. We discuss these before suggesting a non-parametric method, Wp smoothing, which seems to avoid some of them. After outlining the principles of Wp smoothing we describe an algorithm used to implement it. We then apply Wp smoothing to a synthetic data cube for the LOFAR EoR experiment. The performance of Wp smoothing, measured by the extent to which it is able to recover the variance of the cosmological signal and to which it avoids leakage of power from the foregrounds, is compared to that of a parametric fit, and to another non-parame...
The properties and mechanism of long-term memory in nonparametric volatility
Li, Handong; Cao, Shi-Nan; Wang, Yan
2010-08-01
Recent empirical literature documents the presence of long-term memory in return volatility. But the mechanism of the existence of long-term memory is still unclear. In this paper, we investigate the origin and properties of long-term memory with nonparametric volatility, using high-frequency time series data of the Chinese Shanghai Composite Stock Price Index. We perform Detrended Fluctuation Analysis (DFA) on three different nonparametric volatility estimators with different sampling frequencies. For the same volatility series, the Hurst exponents reduce as the sampling time interval increases, but they are still larger than 1/2, which means that no matter how the interval changes, it still cannot change the existence of long memory. RRV presents a relatively stable property on long-term memory and is less influenced by sampling frequency. RV and RBV have some evolutionary trends depending on time intervals, which indicating that the jump component has no significant impact on the long-term memory property. This suggests that the presence of long-term memory in nonparametric volatility can be contributed to the integrated variance component. Considering the impact of microstructure noise, RBV and RRV still present long-term memory under various time intervals. We can infer that the presence of long-term memory in realized volatility is not affected by market microstructure noise. Our findings imply that the long-term memory phenomenon is an inherent characteristic of the data generating process, not a result of microstructure noise or volatility clustering.
Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition Times.
Xu, Yanxun; Müller, Peter; Wahed, Abdus S; Thall, Peter F
2016-01-01
We analyze a dataset arising from a clinical trial involving multi-stage chemotherapy regimes for acute leukemia. The trial design was a 2 × 2 factorial for frontline therapies only. Motivated by the idea that subsequent salvage treatments affect survival time, we model therapy as a dynamic treatment regime (DTR), that is, an alternating sequence of adaptive treatments or other actions and transition times between disease states. These sequences may vary substantially between patients, depending on how the regime plays out. To evaluate the regimes, mean overall survival time is expressed as a weighted average of the means of all possible sums of successive transitions times. We assume a Bayesian nonparametric survival regression model for each transition time, with a dependent Dirichlet process prior and Gaussian process base measure (DDP-GP). Posterior simulation is implemented by Markov chain Monte Carlo (MCMC) sampling. We provide general guidelines for constructing a prior using empirical Bayes methods. The proposed approach is compared with inverse probability of treatment weighting, including a doubly robust augmented version of this approach, for both single-stage and multi-stage regimes with treatment assignment depending on baseline covariates. The simulations show that the proposed nonparametric Bayesian approach can substantially improve inference compared to existing methods. An R program for implementing the DDP-GP-based Bayesian nonparametric analysis is freely available at https://www.ma.utexas.edu/users/yxu/.
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/.
A Statistical Approach to Crime Linkage
Porter, Michael D.
2014-01-01
The object of this paper is to develop a statistical approach to criminal linkage analysis that discovers and groups crime events that share a common offender and prioritizes suspects for further investigation. Bayes factors are used to describe the strength of evidence that two crimes are linked. Using concepts from agglomerative hierarchical clustering, the Bayes factors for crime pairs are combined to provide similarity measures for comparing two crime series. This facilitates crime series...
Farcomeni, Alessio; Serranti, Silvia; Bonifazi, Giuseppe
2008-01-01
Glass ceramic detection in glass recycling plants represents a still unsolved problem, as glass ceramic material looks like normal glass and is usually detected only by specialized personnel. The presence of glass-like contaminants inside waste glass products, resulting from both industrial and differentiated urban waste collection, increases process production costs and reduces final product quality. In this paper an innovative approach for glass ceramic recognition, based on the non-parametric analysis of infrared spectra, is proposed and investigated. The work was specifically addressed to the spectral classification of glass and glass ceramic fragments collected in an actual recycling plant from three different production lines: flat glass, colored container-glass and white container-glass. The analyses, carried out in the near and mid-infrared (NIR-MIR) spectral field (1280-4480 nm), show that glass ceramic and glass fragments can be recognized by applying a wavelet transform, with a small classification error. Moreover, a method for selecting only a small subset of relevant wavelength ratios is suggested, allowing the conduct of a fast recognition of the two classes of materials. The results show how the proposed approach can be utilized to develop a classification engine to be integrated inside a hardware and software sorting architecture for fast "on-line" ceramic glass recognition and separation.
Methods for genetic linkage analysis using trisomies
Energy Technology Data Exchange (ETDEWEB)
Feingold, E. [Emory Univ. School of Public Health, Atlanta, GA (United States); Lamb, N.E.; Sherman, S.L. [Emory Univ., Atlanta, GA (United States)
1995-02-01
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 {open_quotes}susceptibility{close_quotes} alleles inherited from the nondisjoining parent give increased likelihood of having the trait. Our mapping method is similar to identity-by-descent-based mapping methods using affected relative pairs and also to methods for mapping recessive traits using inbred individuals by looking for markers with greater than expected homozygosity by descent. In the trisomy case, one would take trisomic individuals and look for markers with greater than expected homozygosity in the chromosomes inherited from the nondisjoining parent. We present statistical methods for performing such a linkage analysis, including a test for linkage to a marker, a method for estimating the distance from the marker to the trait gene, a confidence interval for that distance, and methods for computing power and sample sizes. We also resolve some practical issues involved in implementing the methods, including how to use partially informative markers and how to test candidate genes. 20 refs., 5 figs., 1 tab.
Model-based methods for linkage analysis.
Rice, John P; Saccone, Nancy L; Corbett, Jonathan
2008-01-01
The logarithm of an odds ratio (LOD) score method originated in a seminal article by Newton Morton in 1955. The method is broadly concerned with issues of power and the posterior probability of linkage, ensuring that a reported linkage has a high probability of being a true linkage. In addition, the method is sequential so that pedigrees or LOD curves may be combined from published reports to pool data for analysis. This approach has been remarkably successful for 50 years in identifying disease genes for Mendelian disorders. After discussing these issues, we consider the situation for complex disorders where the maximum LOD score statistic shares some of the advantages of the traditional LOD score approach, but is limited by unknown power and the lack of sharing of the primary data needed to optimally combine analytic results. We may still learn from the LOD score method as we explore new methods in molecular biology and genetic analysis to utilize the complete human DNA sequence and the cataloging of all human genes.
Structural synthesis of linkages for quadruped bio-robot legs
Antonescu, O.; Robu, C.; Antonescu, P.
2016-08-01
The paper presents a few kinematic schemes of planar mechanisms with bars (linkages) used as part of the quadruped robot legs. The Dunshee linkage having only four elements as crank-rocker mechanism is analyzed. Further, the Klann linkage, which is accomplished by amplifying the crank-rocker mechanism with a dyadic kinematic chain, is also presented. More than that, the Jansen linkage, which is obtained by extending and amplifying the crank-rocker mechanism with two dyadic kinematic chains, is also analyzed. At the end of the paper, the authors present a novel linkage application consisting of a quadric kinematic chain.
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...... Vector Decomposition model recently proposed by Plümper & Tröger, 2007).Turning to the results, both estimators give empirical support for our chosen gravity setup as an appropriate framework in explaining German trade and FDI activity. Looking carefully at cross-variable linkages we basically find...... 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...
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.
Conderino, Sarah; Fung, Lawrence; Sedlar, Slavenka; Norton, Jennifer M
2017-04-01
Motor vehicle traffic (MVT) crashes kill or seriously injure approximately 4250 people in New York City (NYC) each year. Traditionally, NYC surveillance practices use hospitalization and crash data separately to monitor trends in MVT-related injuries, but key information linking crash circumstances to health outcomes is lost when analyzing these data sources in isolation. Our objective was to match crash reports to hospitalization records to create a traffic injury surveillance dataset that can be used to describe crash circumstances and related injury outcomes. The linkage of the two systems presents a unique challenge since the system tracking crashes and the system tracking hospitalizations and emergency department (ED) visits lack key identifying data such as names and dates of birth. NYC Department of Transportation provided electronic records based on reports of motor vehicle crashes submitted to the New York State Department of Motor Vehicles for all crashes occurring in NYC from 2009 to 2013. New York Statewide Planning and Research Cooperative System (SPARCS) ED and hospitalization administrative data from NYC hospitals were used to identify unintentional MVT-related injuries using external cause of injury codes. Since the two systems do not share unique individual identifiers, probabilistic record linkage was conducted using LinkSolv9.0. Sensitivity/specificity calculations and chi-square analyses of linkage rates were conducted to assess linkage results. From 2009-2013, there were 1,054,344 individuals involved in MVT crashes in NYC and 280,340 ED visits and hospitalizations from MVT-related injuries. There were 145,003 linked pairs, giving a linkage rate of 52% of the total MVT-related hospital records. This linkage had a sensitivity of 74% and a specificity of 93%. Linkage rates were comparable by age, sex, crash role, collision type, hospital county, injury location, hospital type, and hospital status, indicating no apparent biases in the match by
Woodbury-Smith, M; Bilder, D A; Morgan, J; Jerominski, L; Darlington, T; Dyer, T; Paterson, A D; Coon, H
2017-01-01
It has long been recognized that there is an association between enlarged head circumference (HC) and autism spectrum disorder (ASD), but the genetics of HC in ASD is not well understood. In order to investigate the genetic underpinning of HC in ASD, we undertook a genome-wide linkage study of HC followed by linkage signal targeted association among a sample of 67 extended pedigrees with ASD. HC measurements on members of 67 multiplex ASD extended pedigrees were used as a quantitative trait in a genome-wide linkage analysis. The Illumina 6K SNP linkage panel was used, and analyses were carried out using the SOLAR implemented variance components model. Loci identified in this way formed the target for subsequent association analysis using the Illumina OmniExpress chip and imputed genotypes. A modification of the qTDT was used as implemented in SOLAR. We identified a linkage signal spanning 6p21.31 to 6p22.2 (maximum LOD = 3.4). Although targeted association did not find evidence of association with any SNP overall, in one family with the strongest evidence of linkage, there was evidence for association (rs17586672, p = 1.72E-07). Although this region does not overlap with ASD linkage signals in these same samples, it has been associated with other psychiatric risk, including ADHD, developmental dyslexia, schizophrenia, specific language impairment, and juvenile bipolar disorder. The genome-wide significant linkage signal represents the first reported observation of a potential quantitative trait locus for HC in ASD and may be relevant in the context of complex multivariate risk likely leading to ASD.
Population-environment linkages in international law
Energy Technology Data Exchange (ETDEWEB)
Babor, D.D.M.
1999-03-31
This article explores population-environment linkages both within developed and developing nations, and considers the consequences of a population growth rate which, as one hectare of arable land is simultaneously lost or destroyed, currently results in eight live births every three seconds. In order to better comprehend the forces governing their perceptions, Part 1 of this article will discuss eight interactive variables which inform decision-making. Part 2 will examine the existence of legal duties under international law to limit or constrain the level of consumption and the right to freely reproduce, particularly as applicable in states considered free of a population problem.
Non-parametric change-point method for differential gene expression detection.
Directory of Open Access Journals (Sweden)
Yao Wang
Full Text Available BACKGROUND: We proposed a non-parametric method, named Non-Parametric Change Point Statistic (NPCPS for short, by using a single equation for detecting differential gene expression (DGE in microarray data. NPCPS is based on the change point theory to provide effective DGE detecting ability. METHODOLOGY: NPCPS used the data distribution of the normal samples as input, and detects DGE in the cancer samples by locating the change point of gene expression profile. An estimate of the change point position generated by NPCPS enables the identification of the samples containing DGE. Monte Carlo simulation and ROC study were applied to examine the detecting accuracy of NPCPS, and the experiment on real microarray data of breast cancer was carried out to compare NPCPS with other methods. CONCLUSIONS: Simulation study indicated that NPCPS was more effective for detecting DGE in cancer subset compared with five parametric methods and one non-parametric method. When there were more than 8 cancer samples containing DGE, the type I error of NPCPS was below 0.01. Experiment results showed both good accuracy and reliability of NPCPS. Out of the 30 top genes ranked by using NPCPS, 16 genes were reported as relevant to cancer. Correlations between the detecting result of NPCPS and the compared methods were less than 0.05, while between the other methods the values were from 0.20 to 0.84. This indicates that NPCPS is working on different features and thus provides DGE identification from a distinct perspective comparing with the other mean or median based methods.
A Conservative Meta-Analysis of Linkage and Linkage-Association Studies of Developmental Dyslexia
Grigorenko, Elena L.
2005-01-01
Linkage studies of complex phenotypes such as reading ability/disability (developmental dyslexia or reading disorder) and related componential processes, where the effects attributable to individual genes appear to be modest, are critically dependent on the nature and composition of the samples and the phenotypes analyzed. Thus, it might be…
Applications of Parametric and Nonparametric Tests for Event Studies on ISE
Handan YOLSAL
2011-01-01
In this study, we conducted a research as to whether splits in shares on the ISE-ON Index at the Istanbul Stock Exchange have had an impact on returns generated from shares between 2005 and 2011 or not using event study method. This study is based on parametric tests, as well as on nonparametric tests developed as an alternative to them. It has been observed that, when cross-sectional variance adjustment is applied to data set, such null hypothesis as “there is no average abnormal return at d...
Ohkubo, Jun
2011-12-01
A scheme is developed for estimating state-dependent drift and diffusion coefficients in a stochastic differential equation from time-series data. The scheme does not require to specify parametric forms for the drift and diffusion coefficients in advance. In order to perform the nonparametric estimation, a maximum likelihood method is combined with a concept based on a kernel density estimation. In order to deal with discrete observation or sparsity of the time-series data, a local linearization method is employed, which enables a fast estimation.
Blundell, Richard; Horowitz, Joel L.; Parey, Matthias
2011-01-01
This paper develops a new method for estimating a demand function and the welfare consequences of price changes. The method is applied to gasoline demand in the U.S. and is applicable to other goods. The method uses shape restrictions derived from economic theory to improve the precision of a nonparametric estimate of the demand function. Using data from the U.S. National Household Travel Survey, we show that the restrictions are consistent with the data on gasoline demand and remove the anom...
Two new non-parametric tests to the distance duality relation with galaxy clusters
Costa, S S; Holanda, R F L
2015-01-01
The cosmic distance duality relation is a milestone of cosmology involving the luminosity and angular diameter distances. Any departure of the relation points to new physics or systematic errors in the observations, therefore tests of the relation are extremely important to build a consistent cosmological framework. Here, two new tests are proposed based on galaxy clusters observations (angular diameter distance and gas mass fraction) and $H(z)$ measurements. By applying Gaussian Processes, a non-parametric method, we are able to derive constraints on departures of the relation where no evidence of deviation is found in both methods, reinforcing the cosmological and astrophysical hypotheses adopted so far.
Nonparametric variance estimation in the analysis of microarray data: a measurement error approach.
Carroll, Raymond J; Wang, Yuedong
2008-01-01
This article investigates the effects of measurement error on the estimation of nonparametric variance functions. We show that either ignoring measurement error or direct application of the simulation extrapolation, SIMEX, method leads to inconsistent estimators. Nevertheless, the direct SIMEX method can reduce bias relative to a naive estimator. We further propose a permutation SIMEX method which leads to consistent estimators in theory. The performance of both SIMEX methods depends on approximations to the exact extrapolants. Simulations show that both SIMEX methods perform better than ignoring measurement error. The methodology is illustrated using microarray data from colon cancer patients.
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.
López Fontán, J L; Costa, J; Ruso, J M; Prieto, G; Sarmiento, F
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.
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.)
Poage, J. L.
1975-01-01
A sequential nonparametric pattern classification procedure is presented. The method presented is an estimated version of the Wald sequential probability ratio test (SPRT). This method utilizes density function estimates, and the density estimate used is discussed, including a proof of convergence in probability of the estimate to the true density function. The classification procedure proposed makes use of the theory of order statistics, and estimates of the probabilities of misclassification are given. The procedure was tested on discriminating between two classes of Gaussian samples and on discriminating between two kinds of electroencephalogram (EEG) responses.
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
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
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....
Nonparametric Bayesian Sparse Factor Models with application to Gene Expression modelling
Knowles, David
2010-01-01
A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data Y is modeled as a linear superposition, G, of a potentially infinite number of hidden factors, X. The Indian Buffet Process (IBP) is used as a prior on G to incorporate sparsity and to allow the number of latent features to be inferred. The model's utility for modeling gene expression data is investigated using randomly generated datasets based on a known sparse connectivity matrix for E. Coli, and on three biological datasets of increasing complexity.
Non-parametric trend analysis of water quality data of rivers in Kansas
Yu, Y.-S.; Zou, S.; Whittemore, D.
1993-01-01
Surface water quality data for 15 sampling stations in the Arkansas, Verdigris, Neosho, and Walnut river basins inside the state of Kansas were analyzed to detect trends (or lack of trends) in 17 major constituents by using four different non-parametric methods. The results show that concentrations of specific conductance, total dissolved solids, calcium, total hardness, sodium, potassium, alkalinity, sulfate, chloride, total phosphorus, ammonia plus organic nitrogen, and suspended sediment generally have downward trends. Some of the downward trends are related to increases in discharge, while others could be caused by decreases in pollution sources. Homogeneity tests show that both station-wide trends and basinwide trends are non-homogeneous. ?? 1993.
Noise and speckle reduction in synthetic aperture radar imagery by nonparametric Wiener filtering.
Caprari, R S; Goh, A S; Moffatt, E K
2000-12-10
We present a Wiener filter that is especially suitable for speckle and noise reduction in multilook synthetic aperture radar (SAR) imagery. The proposed filter is nonparametric, not being based on parametrized analytical models of signal statistics. Instead, the Wiener-Hopf equation is expressed entirely in terms of observed signal statistics, with no reference to the possibly unobservable pure signal and noise. This Wiener filter is simple in concept and implementation, exactly minimum mean-square error, and directly applicable to signal-dependent and multiplicative noise. We demonstrate the filtering of a genuine two-look SAR image and show how a nonnegatively constrained version of the filter substantially reduces ringing.
Rotondi, R.
2009-04-01
According to the unified scaling theory the probability distribution function of the recurrence time T is a scaled version of a base function and the average value of T can be used as a scale parameter for the distribution. The base function must belong to the scale family of distributions: tested on different catalogues and for different scale levels, for Corral (2005) the (truncated) generalized gamma distribution is the best model, for German (2006) the Weibull distribution. The scaling approach should overcome the difficulty of estimating distribution functions over small areas but theorical limitations and partial instability of the estimated distributions have been pointed out in the literature. Our aim is to analyze the recurrence time of strong earthquakes that occurred in the Italian territory. To satisfy the hypotheses of independence and identical distribution we have evaluated the times between events that occurred in each area of the Database of Individual Seismogenic Sources and then we have gathered them by eight tectonically coherent regions, each of them dominated by a well characterized geodynamic process. To solve problems like: paucity of data, presence of outliers and uncertainty in the choice of the functional expression for the distribution of t, we have followed a nonparametric approach (Rotondi (2009)) in which: (a) the maximum flexibility is obtained by assuming that the probability distribution is a random function belonging to a large function space, distributed as a stochastic process; (b) nonparametric estimation method is robust when the data contain outliers; (c) Bayesian methodology allows to exploit different information sources so that the model fitting may be good also to scarce samples. We have compared the hazard rates evaluated through the parametric and nonparametric approach. References Corral A. (2005). Mixing of rescaled data and Bayesian inference for earthquake recurrence times, Nonlin. Proces. Geophys., 12, 89
A NONPARAMETRIC PROCEDURE OF THE SAMPLE SIZE DETERMINATION FOR SURVIVAL RATE TEST
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Objective This paper proposes a nonparametric procedure of the sample size determination for survival rate test. Methods Using the classical asymptotic normal procedure yields the required homogenetic effective sample size and using the inverse operation with the prespecified value of the survival function of censoring times yields the required sample size. Results It is matched with the rate test for censored data, does not involve survival distributions, and reduces to its classical counterpart when there is no censoring. The observed power of the test coincides with the prescribed power under usual clinical conditions. Conclusion It can be used for planning survival studies of chronic diseases.
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
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...
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.
Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images
2010-04-01
OF EACH CELL ARE RESULTS OF KSVD AND BPFA, RESPECTIVELY. σ C.man House Peppers Lena Barbara Boats F.print Couple Hill 5 37.87 39.37 37.78 38.60 38.08...INTERPOLATION PSNR RESULTS, USING PATCH SIZE 8× 8. BOTTOM: BPFA RGB IMAGE INTERPOLATION PSNR RESULTS, USING PATCH SIZE 7× 7. data ratio C.man House Peppers Lena...of subspaces. IEEE Trans. Inform. Theory, 2009. [16] T. Ferguson . A Bayesian analysis of some nonparametric problems. Annals of Statistics, 1:209–230
BOOTSTRAP WAVELET IN THE NONPARAMETRIC REGRESSION MODEL WITH WEAKLY DEPENDENT PROCESSES
Institute of Scientific and Technical Information of China (English)
林路; 张润楚
2004-01-01
This paper introduces a method of bootstrap wavelet estimation in a nonparametric regression model with weakly dependent processes for both fixed and random designs. The asymptotic bounds for the bias and variance of the bootstrap wavelet estimators are given in the fixed design model. The conditional normality for a modified version of the bootstrap wavelet estimators is obtained in the fixed model. The consistency for the bootstrap wavelet estimator is also proved in the random design model. These results show that the bootstrap wavelet method is valid for the model with weakly dependent processes.
An adaptive nonparametric method in benchmark analysis for bioassay and environmental studies.
Bhattacharya, Rabi; Lin, Lizhen
2010-12-01
We present a novel nonparametric method for bioassay and benchmark analysis in risk assessment, which averages isotonic MLEs based on disjoint subgroups of dosages. The asymptotic theory for the methodology is derived, showing that the MISEs (mean integrated squared error) of the estimates of both the dose-response curve F and its inverse F(-1) achieve the optimal rate O(N(-4/5)). Also, we compute the asymptotic distribution of the estimate ζ~p of the effective dosage ζ(p) = F(-1) (p) which is shown to have an optimally small asymptotic variance.
Linkages between development and climate change
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Halsnaes, K. [UNEP, Roskilde (Denmark); Verhagen, J. [Plant Res. International, Wageningen (Netherlands); Rovere, E. La [Centro Clima. Centre for Integrated Studies on Climate Change and Environment, Rio de Janeiro (Brazil); Klein, R. [Potsdam Inst. for Climate Impacts Res., PIK, Potsdam (DE); Huq, S. [International Inst. for Environment and Development, IIED, London (United Kingdom)
2003-11-01
This paper aims at assessing how the development and climate change literature has considered potential linkages and synergies between general development policies and climate change adaptation and mitigation policies. The starting point for this review is to give an overview of how alternative economic development paradigms can be used as a background for understanding and assessing development and climate linkages. In this way, it is demonstrated how climate change issues are related to basic factors in economic and social development processes, as an introduction to a discussion about how alternative policy recommendations for integrated development and climate policies can be understood in the context of different development paradigms. The last part of the paper returns to the climate change and sustainable development discussion that in recent years has been running in parallel to the Third Assessment of IPCC. This discussion, to a large extent has been dominated by the climate change agenda rather than a broader development policy perspectives, and the paper finally suggests a number of areas where integrated development and climate studies could anchor climate change studies more in the development agenda. (au)
Methods for genetic linkage analysis using trisomies
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Feingold, E.; Lamb, N.E.; Sherman, S.L. [Emory Univ., Atlanta, GA (United States)
1994-09-01
Certain genetic disorders (e.g. congenital cataracts, duodenal atresia) are rare in the general population, but more common in people with Down`s syndrome. We present a method for using individuals with trisomy 21 to map genes for such traits. Our methods are analogous to methods for mapping autosomal dominant traits using affected relative pairs by looking for markers with greater than expected identity-by-descent. In the trisomy case, one would take trisomic individuals and look for markers with greater than expected reduction to homozygosity in the chromosomes inherited form the non-disjoining parent. We present statistical methods for performing such a linkage analysis, including a test for linkage to a marker, a method for estimating the distance from the marker to the gene, a confidence interval for that distance, and methods for computing power and sample sizes. The methods are described in the context of gene-dosage model for the etiology of the disorder, but can be extended to other models. We also resolve some practical issues involved in implementing the methods, including how to use partially informative markers, how to test candidate genes, and how to handle the effect of reduced recombination associated with maternal meiosis I non-disjunction.
Viral Genetic Linkage Analysis in the Presence of Missing Data.
Directory of Open Access Journals (Sweden)
Shelley H Liu
Full Text Available Analyses of viral genetic linkage can provide insight into HIV transmission dynamics and the impact of prevention interventions. For example, such analyses have the potential to determine whether recently-infected individuals have acquired viruses circulating within or outside a given community. In addition, they have the potential to identify characteristics of chronically infected individuals that make their viruses likely to cluster with others circulating within a community. Such clustering can be related to the potential of such individuals to contribute to the spread of the virus, either directly through transmission to their partners or indirectly through further spread of HIV from those partners. Assessment of the extent to which individual (incident or prevalent viruses are clustered within a community will be biased if only a subset of subjects are observed, especially if that subset is not representative of the entire HIV infected population. To address this concern, we develop a multiple imputation framework in which missing sequences are imputed based on a model for the diversification of viral genomes. The imputation method decreases the bias in clustering that arises from informative missingness. Data from a household survey conducted in a village in Botswana are used to illustrate these methods. We demonstrate that the multiple imputation approach reduces bias in the overall proportion of clustering due to the presence of missing observations.
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.
Gragnoli, Claudia
2014-09-01
Proteasome modulator 9 (PSMD9) gene single nucleotide polymorphism (SNP) rs1043307/rs2514259 (E197G) is associated with significant clinical response to the anti-depressant desipramine. PSMD9 SNP rs74421874 [intervening sequence (IVS) 3 + nt460 G>A], rs3825172 (IVS3 + nt437 C>T) and rs1043307/rs2514259 (E197G A>G) are all linked to type 2 diabetes (T2D), maturity-onset-diabetes-of the young 3 (MODY3), obesity and waist circumference, hypertension, hypercholesterolemia, T2D-macrovascular and T2D-microvascular disease, T2D-neuropathy, T2D-carpal tunnel syndrome, T2D-nephropathy, T2D-retinopathy, non-diabetic retinopathy and depression. PSMD9 rs149556654 rare SNP (N166S A>G) and the variant S143G A>G also contribute to T2D. PSMD9 is located in the chromosome 12q24 locus, which per se is in linkage with depression, bipolar disorder and anxiety. In the present study, we wanted to determine whether PSMD9 is linked to general anxiety disorder in Italian T2D families. Two-hundred Italian T2D families were phenotyped for generalized anxiety disorder, using the diagnostic criteria of DSM-IV. When the diagnosis was unavailable or unclear, the trait was reported as unknown. The 200 Italians families were tested for the PSMD9 T2D risk SNPs rs74421874 (IVS3 + nt460 G>A), rs3825172 (IVS3 +nt437 T>C) and for the T2D risk and anti-depressant response SNP rs1043307/rs2514259 (E197G A>G) for evidence of linkage with generalized anxiety disorder. Non-parametric linkage analysis was executed via Merlin software. One-thousand simulation tests were performed to exclude results due to random chance. In our study, the PSMD9 gene SNPs rs74421874, rs3825172, and rs1043307/rs2514259 result in linkage to generalized anxiety disorder. This is the first report describing PSMD9 gene SNPs in linkage to generalized anxiety disorder in T2D families.
Genetic Mapping in Xenopus Laevis: Eight Linkage Groups Established
Graf, J. D.
1989-01-01
Inheritance of alleles at 29 electrophoretically detected protein loci and one pigment locus (albinism) was analyzed in Xenopus laevis by backcrossing multiply heterozygous individuals generated by intersubspecies hybridization. Pairwise linkage tests revealed eight classical linkage groups. These groups have been provisionally numbered from 1 to 8 in an arbitrarily chosen order. Linkage group 1 includes ALB-2 (albumin), ADH-1 (alcohol dehydrogenase), NP (nucleoside phosphorylase), and a(p) (...
Adaptive Linkage Disequilibrium Between Two Esterase Loci of a Salamander
Webster, T. Preston
1973-01-01
In some populations of the salamander Plethodon cinereus, two polymorphic esterase loci are in linkage disequilibrium. Short-term stability of the linkage disequilibrium is demonstrated by an age class analysis. Long, perhaps very long, term stability is suggested by its distribution. This stability and concordant geographic variation in allelic frequencies imply selective origin and maintenance. Data on the frequencies of two color morphs suggest that formation of the linkage disequilibrium is dependent on the genetic background. Images PMID:4515614
Linkage disequilibrium (LD) is the nonrandom association of alleles and loci within sets of genetic data and when measured over the genomes of a species can provide important indications for how future association analyses should proceed. This information can be advantageous especially for slow-gro...
Automated Generation of Kempe Linkage and Its Complexity
Institute of Scientific and Technical Information of China (English)
高小山; 朱长才
1999-01-01
It is a famous result of Kempe that a linkage can be designed to generate any given plane algebraic curve.In this paper,Kempe's result is improved to give a precise algorithm for generating Kempe linkage.We proved that for an algebraic plane curve of degrenn n,Kempe linkage uses at most O(n4) links.Efforts to implement a program which may generate Kempe linkage and simulation of the generation process of the plane curves are presented in the paper.
A Critical Evaluation of the Nonparametric Approach to Estimate Terrestrial Evaporation
Directory of Open Access Journals (Sweden)
Yongmin Yang
2016-01-01
Full Text Available Evapotranspiration (ET estimation has been one of the most challenging problems in recent decades for hydrometeorologists. In this study, a nonparametric approach to estimate terrestrial evaporation was evaluated using both model simulation and measurements from three sites. Both the model simulation and the in situ evaluation at the Tiger Bush Site revealed that this approach would greatly overestimate ET under dry conditions (evaporative fraction smaller than 0.4. For the evaluation at the Tiger Bush Site, the difference between ET estimates and site observations could be as large as 130 W/m2. However, this approach provided good estimates over the two crop sites. The Nash-Sutcliffe coefficient (E was 0.9 and 0.94, respectively, for WC06 and Yingke. A further theoretical analysis indicates the nonparametric approach is very close to the equilibrium evaporation equation under wet conditions, and this can explain the good performance of this approach at the two crop sites in this study. The evaluation indicates that this approach needs more careful appraisal and that its application in dry conditions should be avoided.
Distributed Nonparametric and Semiparametric Regression on SPARK for Big Data Forecasting
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Jelena Fiosina
2017-01-01
Full Text Available Forecasting in big datasets is a common but complicated task, which cannot be executed using the well-known parametric linear regression. However, nonparametric and semiparametric methods, which enable forecasting by building nonlinear data models, are computationally intensive and lack sufficient scalability to cope with big datasets to extract successful results in a reasonable time. We present distributed parallel versions of some nonparametric and semiparametric regression models. We used MapReduce paradigm and describe the algorithms in terms of SPARK data structures to parallelize the calculations. The forecasting accuracy of the proposed algorithms is compared with the linear regression model, which is the only forecasting model currently having parallel distributed realization within the SPARK framework to address big data problems. The advantages of the parallelization of the algorithm are also provided. We validate our models conducting various numerical experiments: evaluating the goodness of fit, analyzing how increasing dataset size influences time consumption, and analyzing time consumption by varying the degree of parallelism (number of workers in the distributed realization.
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.
A web application for evaluating Phase I methods using a non-parametric optimal benchmark.
Wages, Nolan A; Varhegyi, Nikole
2017-06-01
In evaluating the performance of Phase I dose-finding designs, simulation studies are typically conducted to assess how often a method correctly selects the true maximum tolerated dose under a set of assumed dose-toxicity curves. A necessary component of the evaluation process is to have some concept for how well a design can possibly perform. The notion of an upper bound on the accuracy of maximum tolerated dose selection is often omitted from the simulation study, and the aim of this work is to provide researchers with accessible software to quickly evaluate the operating characteristics of Phase I methods using a benchmark. The non-parametric optimal benchmark is a useful theoretical tool for simulations that can serve as an upper limit for the accuracy of maximum tolerated dose identification based on a binary toxicity endpoint. It offers researchers a sense of the plausibility of a Phase I method's operating characteristics in simulation. We have developed an R shiny web application for simulating the benchmark. The web application has the ability to quickly provide simulation results for the benchmark and requires no programming knowledge. The application is free to access and use on any device with an Internet browser. The application provides the percentage of correct selection of the maximum tolerated dose and an accuracy index, operating characteristics typically used in evaluating the accuracy of dose-finding designs. We hope this software will facilitate the use of the non-parametric optimal benchmark as an evaluation tool in dose-finding simulation.
Application of the LSQR algorithm in non-parametric estimation of aerosol size distribution
He, Zhenzong; Qi, Hong; Lew, Zhongyuan; Ruan, Liming; Tan, Heping; Luo, Kun
2016-05-01
Based on the Least Squares QR decomposition (LSQR) algorithm, the aerosol size distribution (ASD) is retrieved in non-parametric approach. The direct problem is solved by the Anomalous Diffraction Approximation (ADA) and the Lambert-Beer Law. An optimal wavelength selection method is developed to improve the retrieval accuracy of the ASD. The proposed optimal wavelength set is selected by the method which can make the measurement signals sensitive to wavelength and decrease the degree of the ill-condition of coefficient matrix of linear systems effectively to enhance the anti-interference ability of retrieval results. Two common kinds of monomodal and bimodal ASDs, log-normal (L-N) and Gamma distributions, are estimated, respectively. Numerical tests show that the LSQR algorithm can be successfully applied to retrieve the ASD with high stability in the presence of random noise and low susceptibility to the shape of distributions. Finally, the experimental measurement ASD over Harbin in China is recovered reasonably. All the results confirm that the LSQR algorithm combined with the optimal wavelength selection method is an effective and reliable technique in non-parametric estimation of ASD.
LSTA, Rawane Samb
2010-01-01
This thesis deals with the nonparametric estimation of density f of the regression error term E of the model Y=m(X)+E, assuming its independence with the covariate X. The difficulty linked to this study is the fact that the regression error E is not observed. In a such setup, it would be unwise, for estimating f, to use a conditional approach based upon the probability distribution function of Y given X. Indeed, this approach is affected by the curse of dimensionality, so that the resulting estimator of the residual term E would have considerably a slow rate of convergence if the dimension of X is very high. Two approaches are proposed in this thesis to avoid the curse of dimensionality. The first approach uses the estimated residuals, while the second integrates a nonparametric conditional density estimator of Y given X. If proceeding so can circumvent the curse of dimensionality, a challenging issue is to evaluate the impact of the estimated residuals on the final estimator of the density f. We will also at...
Passenger Flow Prediction of Subway Transfer Stations Based on Nonparametric Regression Model
Directory of Open Access Journals (Sweden)
Yujuan Sun
2014-01-01
Full Text Available Passenger flow is increasing dramatically with accomplishment of subway network system in big cities of China. As convergence nodes of subway lines, transfer stations need to assume more passengers due to amount transfer demand among different lines. Then, transfer facilities have to face great pressure such as pedestrian congestion or other abnormal situations. In order to avoid pedestrian congestion or warn the management before it occurs, it is very necessary to predict the transfer passenger flow to forecast pedestrian congestions. Thus, based on nonparametric regression theory, a transfer passenger flow prediction model was proposed. In order to test and illustrate the prediction model, data of transfer passenger flow for one month in XIDAN transfer station were used to calibrate and validate the model. By comparing with Kalman filter model and support vector machine regression model, the results show that the nonparametric regression model has the advantages of high accuracy and strong transplant ability and could predict transfer passenger flow accurately for different intervals.
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...
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.
Identification and well-posedness in a class of nonparametric problems
Zinde-Walsh, Victoria
2010-01-01
This is a companion note to Zinde-Walsh (2010), arXiv:1009.4217v1[MATH.ST], to clarify and extend results on identification in a number of problems that lead to a system of convolution equations. Examples include identification of the distribution of mismeasured variables, of a nonparametric regression function under Berkson type measurement error, some nonparametric panel data models, etc. The reason that identification in different problems can be considered in one approach is that they lead to the same system of convolution equations; moreover the solution can be given under more general assumptions than those usually considered, by examining these equations in spaces of generalized functions. An important issue that did not receive sufficient attention is that of well-posedness. This note gives conditions under which well-posedness obtains, an example that demonstrates that when well-posedness does not hold functions that are far apart can give rise to observable arbitrarily close functions and discusses ...
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.
Kim, Junmo; Fisher, John W; Yezzi, Anthony; Cetin, Müjdat; Willsky, Alan S
2005-10-01
In this paper, we present a new information-theoretic approach to image segmentation. We cast the segmentation problem as the maximization of the mutual information between the region labels and the image pixel intensities, subject to a constraint on the total length of the region boundaries. We assume that the probability densities associated with the image pixel intensities within each region are completely unknown a priori, and we formulate the problem based on nonparametric density estimates. Due to the nonparametric structure, our method does not require the image regions to have a particular type of probability distribution and does not require the extraction and use of a particular statistic. We solve the information-theoretic optimization problem by deriving the associated gradient flows and applying curve evolution techniques. We use level-set methods to implement the resulting evolution. The experimental results based on both synthetic and real images demonstrate that the proposed technique can solve a variety of challenging image segmentation problems. Futhermore, our method, which does not require any training, performs as good as methods based on training.
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.
Ryu, Duchwan; Li, Erning; Mallick, Bani K
2011-06-01
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.
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.
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.
A non-parametric approach to estimate the total deviation index for non-normal data.
Perez-Jaume, Sara; Carrasco, Josep L
2015-11-10
Concordance indices are used to assess the degree of agreement between different methods that measure the same characteristic. In this context, the total deviation index (TDI) is an unscaled concordance measure that quantifies to which extent the readings from the same subject obtained by different methods may differ with a certain probability. Common approaches to estimate the TDI assume data are normally distributed and linearity between response and effects (subjects, methods and random error). Here, we introduce a new non-parametric methodology for estimation and inference of the TDI that can deal with any kind of quantitative data. The present study introduces this non-parametric approach and compares it with the already established methods in two real case examples that represent situations of non-normal data (more specifically, skewed data and count data). The performance of the already established methodologies and our approach in these contexts is assessed by means of a simulation study. Copyright © 2015 John Wiley & Sons, Ltd.
A non-parametric Bayesian approach for clustering and tracking non-stationarities of neural spikes.
Shalchyan, Vahid; Farina, Dario
2014-02-15
Neural spikes from multiple neurons recorded in a multi-unit signal are usually separated by clustering. Drifts in the position of the recording electrode relative to the neurons over time cause gradual changes in the position and shapes of the clusters, challenging the clustering task. By dividing the data into short time intervals, Bayesian tracking of the clusters based on Gaussian cluster model has been previously proposed. However, the Gaussian cluster model is often not verified for neural spikes. We present a Bayesian clustering approach that makes no assumptions on the distribution of the clusters and use kernel-based density estimation of the clusters in every time interval as a prior for Bayesian classification of the data in the subsequent time interval. The proposed method was tested and compared to Gaussian model-based approach for cluster tracking by using both simulated and experimental datasets. The results showed that the proposed non-parametric kernel-based density estimation of the clusters outperformed the sequential Gaussian model fitting in both simulated and experimental data tests. Using non-parametric kernel density-based clustering that makes no assumptions on the distribution of the clusters enhances the ability of tracking cluster non-stationarity over time with respect to the Gaussian cluster modeling approach. Copyright © 2013 Elsevier B.V. All rights reserved.
Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation.
Freiman, M; Kronman, A; Esses, S J; Joskowicz, L; Sosna, J
2010-01-01
We present a new non-parametric model constraint graph min-cut algorithm for automatic kidney segmentation in CT images. The segmentation is formulated as a maximum a-posteriori estimation of a model-driven Markov random field. A non-parametric hybrid shape and intensity model is treated as a latent variable in the energy functional. The latent model and labeling map that minimize the energy functional are then simultaneously computed with an expectation maximization approach. The main advantages of our method are that it does not assume a fixed parametric prior model, which is subjective to inter-patient variability and registration errors, and that it combines both the model and the image information into a unified graph min-cut based segmentation framework. We evaluated our method on 20 kidneys from 10 CT datasets with and without contrast agent for which ground-truth segmentations were generated by averaging three manual segmentations. Our method yields an average volumetric overlap error of 10.95%, and average symmetric surface distance of 0.79 mm. These results indicate that our method is accurate and robust for kidney segmentation.
Montiel, Ariadna; Sendra, Irene; Escamilla-Rivera, Celia; Salzano, Vincenzo
2014-01-01
In this work we present a nonparametric approach, which works on minimal assumptions, to reconstruct the cosmic expansion of the Universe. We propose to combine a locally weighted scatterplot smoothing method and a simulation-extrapolation method. The first one (Loess) is a nonparametric approach that allows to obtain smoothed curves with no prior knowledge of the functional relationship between variables nor of the cosmological quantities. The second one (Simex) takes into account the effect of measurement errors on a variable via a simulation process. For the reconstructions we use as raw data the Union2.1 Type Ia Supernovae compilation, as well as recent Hubble parameter measurements. This work aims to illustrate the approach, which turns out to be a self-sufficient technique in the sense we do not have to choose anything by hand. We examine the details of the method, among them the amount of observational data needed to perform the locally weighted fit which will define the robustness of our reconstructio...