31 research outputs found
View of uncut timber left between clearcut strips, showing the mixture of white pine with hemlock and fir
No full textLower Sands Creek, Deception Creek Experimental Forest, View of uncut timber left between clearcut strips, showing the mixture of white pine with hemlock and fir. Reproduction of cut strips secured through seeding-in from the uncut timber. Understory of uncut strip mostly hemlock. Uncut timber 161-200 years old. Reproduction about 20 years old
De toestand van calcium, magnesium en enkele andere mineralen in darminhoud en mest van herkauwers in verband met hun resorptie
No full text
De toestand van calcium, magnesium en enkele andere mineralen in darminhoud en mest van herkauwers in verband met hun resorptie
No full textRaw MEP values (mV). Data are presented as mean and SD.
No full text<p>Raw MEP values (mV). Data are presented as mean and SD.</p
Re: the persistence of folly: a critical examination of dissociative identity disorder. What are Dr Piper and Dr Merskey trying to do?
No full textSemiparametric estimation of volatility: some models and complexity choice in the adaptive functional-coefficient class
No full textIn this paper, semiparametric methods are applied to estimate multivariate volatility functions, using a residual approach as in [J. Fan and Q. Yao, Efficient estimation of conditional variance functions in stochastic regression, Biometrika 85 (1998), pp. 645-660; F.A. Ziegelmann, Nonparametric estimation of volatility functions: The local exponential estimator, Econometric Theory 18 (2002), pp. 985-991; F.A. Ziegel-mann, A local linear least-absolute-deviations estimator of volatility, Comm. Statist. Simulation Comput. 37 (2008), pp. 1543-1564], among others. Our main goal here is two-fold: (1) describe and implement a number of semiparametric models, such as additive, single-index ae class of adaptive functional-coefficient models, choosing simultaneously the bandwidth, the number of covariates in the model and also the single-index smoothing variable. The modified cross-validation algorithm is able to tackle the computational burden caused by the model complexity, providing an important tool in semiparametric volatility estimation. We briefly discuss model identifiability when estimating volatility as well as nonnegativity of the resulting estimators. Furthermore, Monte Carlo simulations for several underlying generating models are implemented and applications to real data are provided
Simultaneous identification of long similar substrings in large sets of sequences-0
No full text<p><b>Copyright information:</b></p><p>Taken from "Simultaneous identification of long similar substrings in large sets of sequences"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S7</p><p>BMC Bioinformatics 2007;8(Suppl 5):S7-S7.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892095.</p><p></p> (len2 = 298559 bp). The alignment has length aln = 105638 including 20 gaps in AC148340 and 9 gaps in AC148483. Its displayed part shows five clusters of mismatches surrounded by long perfect matches so that the number of errors does not exceed 10 in each window of size 40. Therefore, ClustDB does not look for improvements by introducing gaps and reports 69 errors, i.e. 16 errors more than the exact alignment computes. Note that each cluster of mismatches can be realigned with two gaps
