ke inference on parameters of population without assuming the form of the underlying distribution, such as mean, quantiles and regression parameters. We will take advantage of DAC and EL. Compared with BLB and SDB, we not only take full data information, but also save the cost computation. Our method is very simple and efficient. It has two steps. In the first step, we split the sample into random subsets and the estimate of each subset is obtained. In the second step, the estimates are regarded as one sample from a population so that one can apply EL to this simplified sample.

The rest of this article is organized as follows. In Section \[sec2\], we explain our method in details, and establish its theoretical property. In Section \[sec3\], we assess the finite sample performance of proposed method via Monte Carlo simulations. A real data set is analyzed in Section \[sec4\]. All technical proofs of main results are postponed to Appendix.

Methodology {#sec2}
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Let $\mathcal{X}_{n}=\{X_{1}, \dots, X_{n}\}$ be a sample consisting of independent and identically distributed observations form some unknown $q$ dimensional distribution $F$. The parameter of interest is $\theta=\theta(F)\in {\mathbb{R}}^{p}$. Its estimator is ${\widehat}{\theta}_{n}={\widehat}{\theta}(\mathcal{X}_{n})$, which could be maximum likelihood estimator, M-estimator, sample correlation coefficient, U-statistics and many others. In this paper, we mainly focus on the inference of $\theta$. Here is our method.

We first divide the full data set into $K$ blocks randomly, say $\mathcal{X}_{1n_{1}},\dots, \mathcal{X}_{Kn_{K}}$, and then compute $\{ {\widehat}{\theta}_{1n_{1}}={\widehat}{\theta}(\mathcal{X}_{1n_{1}}), \dots  {\widehat}{\theta}_{Kn_{K}}={\widehat}{\theta}(\mathcal{X}_{Kn_{K}})\}$. For simplicity, we assume $n_{j}=m$ for all $1\leq j\leq K$. The DAC estimator is defined by $$\widetilde{\theta}_{n}=\frac{1}{K}\sum_{j=1}^{K} {\widehat}{\theta}_{jm}.$$

Now, we discuss the asymptotic properties of $\widetilde{\theta}_n$. We ass