This note variable selection in the semiparametric linear regression model for censored data. Semiparametric linear regression for censored data is a natural extension of the linear model for uncensored data; however, random censoring introduces substantial theoretical and numerical challenges. By now, a number of authors have made significant contributions for estimation and inference in the s...

The allocation of behavior among competing activities and goal objects depends on the payoffs they provide. Payoff is evaluated among multiple dimensions, including intensity, rate, delay, and kind. Recent findings suggest that by triggering a stream of action potentials in myelinated, medial forebrain bundle axons, rewarding electrical brain stimulation delivers a meaningful intensity signal t...

Developing the infrastructures for preventing non-communicable diseases is one of the most important goals of healthcare context in recent years. In this regard, the number and capacity of preventive healthcare facilities as well as assignment of customers to facilities should be determined for each region. Besides the accessibility, the utility of customers is a determinative factor in partici...

In various statistical model, such as density estimation and estimation of regression curves or hazard rates, monotonicity constraints can arise naturally. A frequently encountered problem in nonparametric statistics is to estimate a monotone density function f on a compact interval. A known estimator for density function of f under the restriction that f is decreasing, is Grenander estimator, ...

The purpose of this paper is to introduce a new estimation method for estimating the Archimedean copula dependence parameter in the non-parametric setting. The estimation of the dependence parameter has been selected as the value that minimizes the Cramér-von-Mises distance which measures the distance between Empirical Bernstein Kendall distribution function and true Kendall distribution functi...

this paper presents a probabilistic optimal power flow (popf) algorithm considering different uncertainties in a smart grid. different uncertainties such as variation of nodal load, change in system configuration, measuring errors, forecasting errors, and etc. can be considered in the proposed algorithm. by increasing the penetration of the renewable energies in power systems, it is more essent...

In the estimation of portfolios, it is natural to assume that the utility function depends on exogenous variable. From this point of view, in this paper, we develop the estimation under the utility function depending on exogenous variable. To estimate the optimal portfolio, we introduce a function of moments of the return process and cumulant between the return processes and exogenous variable,...

Although advances have improved our ability to describe the measurement precision of a test, it often remains challenging to summarize how well a test is performing overall. Reliability, for example, provides an overall summary of measurement precision, but it is sample-specific and might not reflect the potential usefulness of a test if the sample is poorly suited for the test's purposes. The ...

When training and test samples follow different input distributions (i.e., the situation called covariate shift), the maximum likelihood estimator is known to lose its consistency. For regaining consistency, the log-likelihood terms need to be weighted according to the importance (i.e., the ratio of test and training input densities). Thus, accurately estimating the importance is one of the key...

In classical revealed preference analysis we are given a sequence of linear prices (i.e., additive over goods) and an agent’s demand at each of the prices. The problem is to determine whether the observed demands are consistent with utility-maximizing behavior, and if so, recover a representation of the agent’s utility function. In this work, we consider a setting where an agent responds to non...