background and objectives: survival models are statistical technique to estimate or predict the overall time up to specific events. prediction is important in medical science and the accuracy of prediction is determined by a measurement, generally based on loss functions, called prediction error. the aim of this study is using parametric models to determine the factors influencing predicted sur...

The aim of this article is to study the estimation of the parameter of ЭРланга distribution based on complete samples. The Bayes estimators of the parameter of ЭРланга distribution are obtained under three different loss functions, namely, weighted square error loss, squared log error loss and entropy loss functions by using conjugate prior inverse Gamma distribution. Then the minimax estimator...

Estimation in truncated parameter space is one of the most important features in statistical inference, because the frequently used criterion of unbiasedness is useless, since no unbiased estimator exists in general. So, other optimally criteria such as admissibility and minimaxity have to be looked for among others. In this paper we consider a subclass of the exponential families of distributi...

Extended Abstract. The study of truncated parameter space in general is of interest for the following reasons: 1.They often occur in practice. In many cases certain parameter values can be excluded from the parameter space. Nearly all problems in practice have a truncated parameter space and it is most impossible to argue in practice that a parameter is not bounded. In truncated parameter...

We prove a theorem about the relative entropy of quantum states, which roughly states that if the relative entropy, S(ρ‖σ) ∆ = Tr ρ(log ρ− log σ), of two quantum states ρ and σ is at most c, then ρ/2 ‘sits inside’ σ. Using this ‘substate’ theorem, we give tight lower bounds for the privacy loss of bounded error quantum communication protocols for the index function problem. We also give tight l...

this paper is concerned with the problem of finding the minimax estimators of the scale parameter ? in a family of transformed chi-square distributions, under asymmetric squared log error (sle) and modified linear exponential (mlinex) loss functions, using the lehmann theorem [2]. also we show that the results of podder et al. [4] for pareto distribution are a special case of our results for th...

Modern control systems widely use network to decrease the implementation cost and also increase the performance. Although they have several advantages, they suffer from some limitations and deficiencies. Packet loss is one of the main limitations which affect the control system in different conditions and finally can lead to system instability. To prevent such problems it is important to model ...

This paper is concerned with the problem of finding the minimax estimators of the scale parameter ? in a family of transformed chi-square distributions, under asymmetric squared log error (SLE) and modified linear exponential (MLINEX) loss functions, using the Lehmann Theorem [2]. Also we show that the results of Podder et al. [4] for Pareto distribution are a special case of our results for th...

We consider the problem of estimating the transition probability kernel to be used by a model-based reinforcement learning (RL) algorithm. We argue that estimating a generative model that minimizes a probabilistic loss, such as the log-loss, is an overkill because it does not take into account the underlying structure of decision problem and the RL algorithm that intends to solve it. We introdu...