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排序方式: 共有82条查询结果,搜索用时 15 毫秒
1.
Randomization models for the matched and unmatched 2 ? 2 tables 总被引:1,自引:0,他引:1
2.
Rosenbaum PR 《Biometrics》1999,55(2):560-564
When a treatment has a dilated effect, with larger effects when responses are higher, there can be much less sensitivity to bias at upper quantiles than at lower quantiles; i.e., small, plausible hidden biases might explain the ostensible effect of the treatment for many subjects, and yet only quite large hidden biases could explain the effect on a few subjects having dramatically elevated responses. An example concerning kidney function of cadmium workers is discussed in detail. In that example, the treatment effect is far from additive: It is plausibly zero at the lower quartile of responses to control, and it is large and fairly insensitive to bias at the upper quartile. 相似文献
3.
Valid inference in random effects meta-analysis 总被引:2,自引:0,他引:2
The standard approach to inference for random effects meta-analysis relies on approximating the null distribution of a test statistic by a standard normal distribution. This approximation is asymptotic on k, the number of studies, and can be substantially in error in medical meta-analyses, which often have only a few studies. This paper proposes permutation and ad hoc methods for testing with the random effects model. Under the group permutation method, we randomly switch the treatment and control group labels in each trial. This idea is similar to using a permutation distribution for a community intervention trial where communities are randomized in pairs. The permutation method theoretically controls the type I error rate for typical meta-analyses scenarios. We also suggest two ad hoc procedures. Our first suggestion is to use a t-reference distribution with k-1 degrees of freedom rather than a standard normal distribution for the usual random effects test statistic. We also investigate the use of a simple t-statistic on the reported treatment effects. 相似文献
4.
Geometric morphometric studies are increasingly becoming common in systematics and palaeontology. The samples in such studies
are often small, due to the paucity of material available for analysis. However, very few studies have tried to assess the
impact of sampling error on analytical results. Here, this issue is addressed empirically using repeated randomized selection
experiments to build progressively smaller samples from an original dataset of ∼400 vervet monkey (Cercopithecus aethiops) skulls. Size and shape parameters (including mean size and shape, size and shape variances, angles of allometric trajectories)
that are commonly used in geometric morphometric studies, are estimated first in the original sample and then in the random
subsamples. Estimates are then compared to give an indication of what is the minimum desirable sample size for each parameter.
Mean size, standard deviation of size and variance of shape are found to be fairly accurate even in relatively small samples.
In contrast, mean shapes and angles between static allometric trajectories are strongly affected by sampling error. If confirmed
in other groups, our findings may have substantial implications for studies of morphological variation in present and fossil
species. By performing rarefaction analyses like those presented in our study, morphometricians can be easily provided with
important clues on how a simple but crucial factor like sample size can alter results of their studies. 相似文献
5.
Sndor Bartha 《应用植被学》2001,4(1):53-62
Abstract. Fine‐scale spatial patterns of small mammal disturbances and local accumulation of plant litter were studied together with the spatial pattern of vegetation in different stages of old‐field succession at Cedar Creek Natural History Area, Minnesota, USA. Seven stands from one to 66 years old were sampled. Presence of living plant species, local soil disturbances by pocket gophers (Geomys bursarius) and plant litter accumulation were recorded in 10 cm × 10 cm contiguous microquadrats along elliptical transects. Spatial patterns and associations were analyzed using information theory models. Dominant grasses were spatially independent, while subordinate functional groups were strongly dependent on the existing patchwork of dominant species, plant litter and gopher disturbances. Litter had consistent negative associations with subordinate functional groups in all but the initial years. Gopher disturbances were abundant but had weak and variable associations with vegetation. These results suggest that gopher disturbance does not facilitate the colonization of native prairie species and that diversity can be improved by controlling litter accumulation in Minnesota old‐fields. 相似文献
6.
Tests for no treatment effect in randomized clinical trials 总被引:1,自引:0,他引:1
7.
Sensitivity analysis for matching with multiple controls 总被引:1,自引:0,他引:1
8.
C. Johan Dourleijn Stefan G. A. J. Driessen 《Biometrical journal. Biometrische Zeitschrift》1993,35(3):267-282
Starting from the principle that there exists a randomization procedure that assigns treatments to experimental units, four subset selection rules for the problem of selecting the best treatment from a set of different treatments are proposed. Two of these are extensions of already existing subset selection procedures, which were defined for unbalanced designs, and need a separate selection constant for each individual treatment. The other two rules proposed are new and need only one selection constant for all treatments. The various procedures are compared, and illustrated by application to a plant breeding variety trial. 相似文献
9.
Randomization models, often termed “null” models, have been widely used since the 1970s in studies of species community and
biogeographic patterns. More recently they have been used to test for nested species subset patterns (or nestedness) among
assemblages of species occupying spatially subdivided habitats, such as island archipelagoes and terrestrial habitat patches.
Nestedness occurs when the species occupying small or species-poor sites have a strong tendency to form proper subsets of
richer species assemblages. In this paper, we examine the ability of several published simulation models to detect, in an
unbiased way, nested subset patterns from a simple matrix of site-by-species presence-absence data. Each approach attempts
to build in biological realism by following the assumption that the ecological processes that generated the patterns observed
in nature would, if they could be repeated many times over using the same species and landscape configuration, produce islands
with the same number of species and species present on the same number of islands as observed. In mathematical terms, the
mean marginal totals (column and row sums) of many simulated matrices would match those of the observed matrix. Results of
model simulations suggest that the true probability of a species occupying any given site cannot be estimated unambiguously.
Nearly all of the models tested were shown to bias simulation matrices toward low levels of nestedness, increasing the probability
of a Type I statistical error. Further, desired marginal totals could be obtained only through ad-hoc manipulation of the
calculated probabilities. Paradoxically, when such results are achieved, the model is shown to have little statistical power
to detect nestedness. This is because nestedness is determined largely by the marginal totals of the matrix themselves, as
suggested earlier by Wright and Reeves. We conclude that at the present time, the best null model for nested subset patterns
may be one based on equal probabilities of occurrence for all species. Examples of such models are readily available in the
literature.
Received: 3 February 1997 / Accepted: 21 September 1997 相似文献