Kun-Liang Lu

Bio: Kun-Liang Lu is an academic researcher from Cornell University. The author has contributed to research in topics: Nonparametric statistics & Cumulative distribution function. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

U–Pb and Lu–Hf zircon data in young sediments reflect sedimentary recycling in eastern South Africa

Abstract: Detrital zircon from unconsolidated, Cenozoic sediments from eastern South Africa has been analysed for U–Pb and Lu–Hf isotopes by laser ablation inductively coupled plasma mass spectrometry. Identifiable bedrock sources have made local contributions to the detrital zircon populations, but the dominant zircon components are of regional distribution: late Mesoproterozoic (e Hf = –5 to +10), Neoproterozoic to early Palaeozoic (e Hf = –10 to +10), and minor late Palaeozoic (e Hf ≈ 0). Archaean zircons are scarce even in sediments deposited on exposed Archaean basement or by rivers eroding it. The dominant components cannot be tied to specific first-generation sources in southern Africa or its former Gondwana neighbours. Instead, we see the effect of mixing and remobilization of debris from large parts of the supercontinent in the early Phanerozoic, which was stored in the Karoo basin and other continental cover sequences and shed from there to the present site of deposition. Therefore, data from detrital zircon in these deposits tell us less about the path of detritus from source to sink in a recent sedimentary system than about processes in much earlier erosion–transport–deposition cycles. To facilitate comparison of detrital zircon age distribution patterns, a simple and intuitive method that takes sampling uncertainty explicitly into account is proposed. Supplementary materials: U–Pb and Lu–Hf data, and concordia diagrams and discussion of effects of discordance are available at http://www.geolsoc.org.uk/SUP18884.

p-p plots and precedence tests for planar point processes

Abstract: Let Xi,X2,. • • ,Xn be a random sample of size n from a continuous distribution F and Y1}Y2,..., Ym be a random sample of size m from a continuous distribution G. One of the ways to test the hypothesis of equality of F and G against the alternative that F < G when both distributions are univariate is to perform a precedence test -a test that not only requires only a portion of the samples, but which is distribution-free under the null hypothesis. The initial purpose of this thesis was to extend the notion of a precedence test to higher dimensions. In doing so, we found two different tests that are appropriate for both partial and complete data sets. These tests are based on two different extensions of the usual definition of a procentile-procentile plot -which is closely related to the precedence test statistic on the lineto the plane. The first of the above mentioned extensions involves the contours formed by the distribution function F; the second of our tests uses the marginal quantiles of F. For both extensions of the empirical p — p plot, we have proven a Glivenko-Cantelli type of result. Also, we have developed their asymptotic convergence to Gaussian limits. The choice between tests based on these two plots depends on the kind of information that the data of our experiment generates. All the results presented here, although mostly presented for !ft, are valid for 3?-valued data.