Bayesian analysis under ordered functions of parameters

Ordered parameter problems arise in a wide variety of real world situations and are dealt with extensively in the literature. Traditional frequentist methods for dealing with these problems are rather complicated theoretically, especially when sample sizes are small. Bayesian methods are not widely used because high dimensional numerical integration is often required. However, Markov chain Monte Carlo methods provide alternatives to such numerical integration and also deal with ordered parameter problems in a straightforward manner. Little is known about the situation where functions of parameters are ordered. Such problems may seem to be of little practical concern initially, but one can readily see their importance in situations where ordering is placed on the means and variances of several normal or Gamma populations. For the Gamma distribution we will present real examples where we will analyze monthly precipitation data from San Francisco, California and Oakland Mills, Iowa. For the San Francisco data we will simultaneously order both monthly precipitation means and variances. For the Iowa data we will place ordering on seasonal average while still estimating monthly means. Our results show that we would obtain sharper, more accurate inference when order restrictions are employed.     

城市污泥与园林废弃物混合堆肥施用对林地土壤微生物量碳、氮及酶活性的影响

研究城市污泥和园林废弃物混合堆肥施用对土壤微生物量碳(SMBC)、微生物量氮(SMBN)及酶活性的影响,可为林地施用混合堆肥产品的安全及功效性评价提供科学依据.本研究采用大田试验,选取紫穗槐为研究对象,将污泥和园林废弃物以3种比例(GF(纯园林废弃物)、SGA(V(污泥)∶V(园林废弃物)=1∶3)、SGB(V(污泥)∶V(园林废弃物)=1∶1))进行混合堆肥,每种堆肥产品分别按0、5、10、50 kg·株-1(分别记作CK、T5、T10、T50)均匀施入样地.施肥1年后,于田间采样测定土壤生物活性指标及理化性质,采用主成分分析及单、双因素方差分析法研究混合堆肥产品施用后对土壤酶活性及微生物量碳、氮含量的影响.结果表明:施入GF、SGA和SGB时,SMBC和SMBN均在施用量50 kg·株-1时达到最大.随着施用量的增加,土壤酶指数(SEI)总体上呈现逐步递增的趋势,施入GF、SGA、SGB时,各个施肥梯度处理的土壤酶指数较CK分别增加93.33%~186.67%、14.58%~54.17%、119.05%~204.76%.通过主成分分析,依据各处理综合得分情况,不同堆肥产品总体上表现为:SGBGFSGA.施入GF、SGA和SGB,不同处理综合得分均为T50T10T5CK.说明施用堆肥产品能够提高土壤微生物量,增加土壤酶活性,改善土壤的生物环境.     

Hierarchical Bayes estimation of hunting success rates

Although most post-season harvest surveys are conducted at the state level, the effective management of wildlife populations often requires estimates of hunting success rate, hunting pressure and harvest at the sub-area (such as management unit, regional, or county) level.Sample sizes for some sub-areas are often very small or even zero. Because of small sample sizes, estimates for small sub-areas often yield unacceptably large standard errors. In this article, a hierarchical Bayes model is used to estimate hunting success rates at the sub-area level from post-season harvest surveys. The computation is done by Gibbs sampling and adaptive rejection sampling techniques. The method is illustrated using data from the Missouri Turkey Hunting Survey 1994 Spring Season. The Bayesian estimates are close to the frequency estimates for the sub-areas with large sample sizes and more stable than the frequency estimates for those with small sample sizes. The Bayesian estimates will be more useful to wildlife biologists in estab-lishing hunting regulation on small sub-areas at no additional survey cost.     

红壤双季稻田不同施肥下的氨挥发损失及其影响因素

在红壤双季稻田上采用密闭气室法测定了施用等氮量化肥(尿素)、有机肥(猪粪)及化肥有机肥(有机氮无机氮各占一半)配施下氨挥发及其田间表面水NH4 -N含量、温度和pH动态变化,研究氨挥发损失及影响因素.结果表明,与施用尿素相比,单施有机肥及化肥有机肥配施显著降低了稻田氨挥发,施用尿素的早稻和晚稻氨挥发损失率分别达41.4%和39.9%,单施有机肥的分别为0.3%和0.9%,化肥有机肥配施的分别为19.6%和8.9%.施肥后,短期内田面水NH4 -N含量和pH均显著上升,其与氨挥发速率均呈显著正相关.