论文检索

识别产气荚膜梭菌致病菌株间一致变化蛋白的定制多元混合模型AI 解读

Necrotic enteritis (NE) is a serious disease of poultry caused by the bacterium C. perfringens. To identify proteins of C. perfringens that confer virulence with respect to NE, the protein secretions of four NE disease-producing strains and one baseline nondisease-producing strain of C. perfringens were examined. The problem then becomes a clustering task, for the identification of two extreme groups of proteins that were produced at either concordantly higher or…

含有序多分类响应的非参数贝叶斯生物测定AI 解读

Previous attempts at implementing fully Bayesian nonparametric bioassay have enjoyed limited success due to computational difficulties. We show here how this problem may be generally handled using a sampling based approach to develop desired marginal posterior distributions and their features. A useful extension is presented which treats the case of ordered polytomous response. Illustrative examples are provided.

牲畜调查总量的组合估计AI 解读

Four different preliminary estimators are employed by the National Agricultural Statistics Service of the U.S. Department of Agriculture to obtain the final estimate of livestock inventories of major states. To combine these four estimators a composite estimator (which is a weighted average) is proposed. Two types of composite estimators—one with fixed weights, the other with adaptive weights—are investigated. The composite estimator with adaptive weights is derived by…

生物测定效力曲线的线性贝叶斯估计量AI 解读

The prior distribution on the class of potency curves in quantal bioassay is assumed to be Ferguson's Dirichlet distribution. Given integrated squared error loss and quantal response data, we derive the Bayes estimator in a linear space generated by the data. Some numerical examples and asymptotic results are also given.

生物测定效价曲线的线性贝叶斯估计AI 解读

The prior distribution on the class of potency curves in quantal bioassay is assumed to be Ferguson's Dirichlet distribution. Given integrated squared error loss and quantal response data, we derive the Bayes estimator in a linear space generated by the data. Some numerical examples and asymptotic results are also given.

经验分布函数的可容许性AI 解读

Consider the problem of estimating an unknown distribution function $F$ from the class of all distribution functions given a random sample of size $n$ from $F$. It is proved that the empirical distribution function is admissible for the loss functions $L(F, \hat{F}) = \int (\hat{F}(t) - F(t))^2(F(t))^\alpha(1 - F(t))^b dW(t)$ for any $a < 1$ and $b < 1$ and finite measure $W$. Related results for simultaneous estimation of distribution functions and for finite population…