Penduga Konsisten dari Fungsi Sebaran dan Fungsi Kepekatan Peluang Waktu Tunggu Proses Poisson Periodik

ABSTRAKPenduga yang konsisten dari fungsi distribusi dan fungsi kepekatan peluang waktu tunggu dari proses Poisson periodik dibahas dalam artikel ini. Tidak ada asumsi bentuk parametrik tertentu dari fungsi intensitas proses Poisson periodik. Situasi dipertimbangkan ketika hanya ada realisasi tunggal dari proses Poisson periodik yang teramati dalam interval terbatas [0,n]. Hasil pembuktian menunjukkan bahwa penduga yang diusulkan konsisten ketika n-??. ABSTRACTThe consistent estimator of the distribution and the density functions of the waiting time of a cyclic Poisson process is considered and investigated. We do not assume any particular parametric form of the intensity function of the cyclic Poisson process. We consider the situation when there is only a single realization of the cyclic Poisson process is spotted in a bounded interval [0,n]. We proved that the propose estimators are consistent as n-??.

A Consistent Estimator of Structural Distribution

We consider sparse count data models with the sparsity rate ? = N/n = O(1) where N = N (n) is the number of observations and n ? ? is the number of cells. In this case the plug-in estimator of the structural distribution of expected frequencies is inconsistent. If ? = O(n ?? ) for some ? > 0, the nonparametric maximum likelihood estimator, in general, is also inconsistent. Assuming that some auxiliary information on the expected frequencies is available, we construct a consistent estimator of the structural distribution.

The conditionality principle in high-dimensional regression

Summary Consider a high-dimensional linear regression problem, where the number of covariates is larger than the number of observations and the interest is in estimating the conditional variance of the response variable given the covariates. A conditional and an unconditional framework are considered, where conditioning is with respect to the covariates, which are ancillary to the parameter of interest. In recent papers, a consistent estimator was developed in the unconditional framework when the marginal distribution of the covariates is normal with known mean and variance. In the present work, a certain Bayesian hypothesis test is formulated under the conditional framework, and it is shown that the Bayes risk is a constant. This implies that no consistent estimator exists in the conditional framework. However, when the marginal distribution of the covariates is normal, the conditional error of the above consistent estimator converges to zero, with probability converging to one. It follows that even in the conditional setting, information about the marginal distribution of an ancillary statistic may have a significant impact on statistical inference. The practical implication in the context of high-dimensional regression models is that additional observations where only the covariates are given are potentially very useful and should not be ignored. This finding is most relevant to semi-supervised learning problems where covariate information is easy to obtain.

Inference under covariate‐adaptive randomization with multiple treatments

This paper studies inference in randomized controlled trials with covariate‐adaptive randomization when there are multiple treatments. More specifically, we study in this setting inference about the average effect of one or more treatments relative to other treatments or a control. As in Bugni, Canay, and Shaikh (2018), covariate‐adaptive randomization refers to randomization schemes that first stratify according to baseline covariates and then assign treatment status so as to achieve “balance” within each stratum. Importantly, in contrast to Bugni, Canay, and Shaikh (2018), we not only allow for multiple treatments, but further allow for the proportion of units being assigned to each of the treatments to vary across strata. We first study the properties of estimators derived from a “fully saturated” linear regression, that is, a linear regression of the outcome on all interactions between indicators for each of the treatments and indicators for each of the strata. We show that tests based on these estimators using the usual heteroskedasticity‐consistent estimator of the asymptotic variance are invalid in the sense that they may have limiting rejection probability under the null hypothesis strictly greater than the nominal level; on the other hand, tests based on these estimators and suitable estimators of the asymptotic variance that we provide are exact in the sense that they have limiting rejection probability under the null hypothesis equal to the nominal level. For the special case in which the target proportion of units being assigned to each of the treatments does not vary across strata, we additionally consider tests based on estimators derived from a linear regression with “strata fixed effects,” that is, a linear regression of the outcome on indicators for each of the treatments and indicators for each of the strata. We show that tests based on these estimators using the usual heteroskedasticity‐consistent estimator of the asymptotic variance are conservative in the sense that they have limiting rejection probability under the null hypothesis no greater than and typically strictly less than the nominal level, but tests based on these estimators and suitable estimators of the asymptotic variance that we provide are exact, thereby generalizing results in Bugni, Canay, and Shaikh (2018) for the case of a single treatment to multiple treatments. A simulation study and an empirical application illustrate the practical relevance of our theoretical results.