Perkecambahan Dan Pertumbuhan Benih Pala (Myristica Fragrans Houtt)akibat Lama Perendaman Pada Atonik Dan Komposisi Media Tanam

This research aimed at determining the process of germination and growth of nutmeg seeds to obtain superior nutmeg seeds in Baolan Sub District of Tolitoli District. The research was conducted in two stages of experiments. The first stage involved soaking the seeds under different periods including with no soaking (A0), two hour soaking period (A1) and three hour soaking period (A2). This experiment used a Completely Randomized Design where each treatment had seven replicates. Greatest germination rate of 34.29% was found in the three hour soaking period after the seeds were allowed to sproud for six weeks. This was significantly different from the other two treatments under which treatments the germination rate was only 26.43%. At the second stage, the soaking period treatments were combined with various growing media including T1 (soil:sand:cow manure bokashi; v/v/v), T2 (soil:sand:cow manure bokashi; v/v/2v), and T3 (soil:sand:cow manure bokashi; v/v/3v) . The experiment was arranged in a Two Factorial Randomized Block Design where each treatments had four replicates. The average seedling height of 18.85 cm found in the A2T3 treatment 12 weeks after planting (WAP) was significantly higher than in the A1T2 and A0T1 under which treatments the heights were only 14.45 cm and 12.77 cm, respectively

Comment: Microarrays, Empirical Bayes and the Two-Group Model

Comment on ``Microarrays, Empirical Bayes and the Two-Group Model'' [arXiv:0808.0572]Comment: Published in at http://dx.doi.org/10.1214/07-STS236C the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Comment: Microarrays, Empirical Bayes and the Two-Groups Model

Comment on ``Microarrays, Empirical Bayes and the Two-Groups Model'' [arXiv:0808.0572]Comment: Published in at http://dx.doi.org/10.1214/07-STS236A the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Minimax and Adaptive Inference in Nonparametric Function Estimation

Since Stein's 1956 seminal paper, shrinkage has played a fundamental role in both parametric and nonparametric inference. This article discusses minimaxity and adaptive minimaxity in nonparametric function estimation. Three interrelated problems, function estimation under global integrated squared error, estimation under pointwise squared error, and nonparametric confidence intervals, are considered. Shrinkage is pivotal in the development of both the minimax theory and the adaptation theory. While the three problems are closely connected and the minimax theories bear some similarities, the adaptation theories are strikingly different. For example, in a sharp contrast to adaptive point estimation, in many common settings there do not exist nonparametric confidence intervals that adapt to the unknown smoothness of the underlying function. A concise account of these theories is given. The connections as well as differences among these problems are discussed and illustrated through examples.Comment: Published in at http://dx.doi.org/10.1214/11-STS355 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org