Accuracy of regularized D-rule for binary classification

We consider a regularized D-classification rule for high dimensional binary classification, which adapts the linear shrinkage estimator of a covariance matrix as an alternative to the sample covariance matrix in the D-classification rule (D-rule in short). We find an asymptotic expression for misclassification rate of the regularized D-rule, when the sample size $n$ and the dimension $p$ both increase and their ratio $p∕n$ approaches a positive constant $\gamma$. In addition, we compare its misclassification rate to the standard D-rule under various settings via simulation.     

Some properties of the exponential distribution class with applications to risk theory

This paper derives some equivalent conditions for tail equivalence of a distribution $G$ and the convolution $G1H$, where $G$ belongs to the exponential distribution class and $H$ is another distribution. This generalizes some existing sufficient conditions and gives further insight into closure properties of the exponential distribution class. If $G$ also is $O$-subexponential, then the new conditions are satisfied. The obtained results are applied to investigating asymptotic behavior for the finite-time ruin probability in a discrete-time risk model with both insurance and financial risks, where the distributions of the insurance risk or the product of the two risks may not belong to the convolution equivalence distribution class.     

Confidence intervals for P(Y

《Statistical Methodology》2012,9(3):445-455

Asymptotic normality of the deconvolution kernel density estimator under the vanishing error variance

Let $X_1,\dots ,X_n$ be i.i.d. observations, where $X_i=Y_i+{\sigma }_n{Z}_i$ and the $Y$’s and $Z$’s are independent. Assume that the $Y$’s are unobservable and that they have the density $f$ and also that the $Z$’s have a known density $k$. Furthermore, let ${\sigma }_n$ depend on $n$ and let ${\sigma }_n\to 0$ as $n\to \infty$. We consider the deconvolution problem, i.e. the problem of estimation of the density $f$ based on the sample $X_1,\dots ,X_n$. A popular estimator of $f$ in this setting is the deconvolution kernel density estimator. We derive its asymptotic normality under two different assumptions on the relation between the sequence ${\sigma }_n$ and the sequence of bandwidths $h_n$. We also consider several simulation examples which illustrate different types of asymptotics corresponding to the derived theoretical results and which show that there exist situations where models with ${\sigma }_n\to 0$ have to be preferred to the models with fixed $\sigma$.     

Simultaneous selection of treatments better and worse than the best and worst controls

In this paper, we consider $p(p\ge 2)$ and $q(q\ge 2)$ independent treatment and control populations respectively, such that an appropriate probability model for the data from $i\mathrm{th}\left(j\mathrm{th}\right)$ treatment (control) population is a member of absolutely continuous location and scale family of distributions which have common scale parameter and possibly differ in location parameters. For example, there may be $p$ newly invented drugs/varieties of seeds/components which have to compete with their existing $q$ standard competitors in terms of their average responses. A newly invented drug/variety of seed/component is said to be good (bad) if the distance of its average response from the largest (smallest) average response of $q$ control populations is more (less) than ${\delta }_1\left({\delta }_2\right)$ units, where ${\delta }_1$ and ${\delta }_2$ are positive constants to be specified by the experimenter. In this setting a selection procedure is proposed to select simultaneously two subsets $S_U$ and $S_L$ of the $p$ treatment populations such that the subset $S_U$ contains all the good treatments and the subset $S_L$ contains all the bad treatments with probability at least $P^1$, where $P^1$ is a pre-assigned value. The proposed procedure was applied to normal and two parameters exponential probability models separately and the relevant selection constants have been tabulated. The implementation of the proposed methodology is demonstrated through a numerical example based on real life data. The authenticity of numerically computed critical constants have been verified through simulation. Further, if we define the $i\mathrm{th}$ treatment population as bad (good) if the distance of its average response from the largest (smallest) average response of $q$ control populations is less (more) than ${\delta }_3\left({\delta }_4\right)$ units, where ${\delta }_3$ and ${\delta }_4$ are to be specified by the experimenter such that ${\delta }_4>{\delta }_3>0$, then we have proposed a simultaneous selection procedure to select $S_U$ and $S_L$ and a sample size is determined so that the probability of omitting a good (bad) treatment population from $S_U\left(S_L\right)$ or selecting a bad (good) treatment population in $S_U\left(S_L\right)$ is at most $1-P^1$.     

Optimal control of the surplus in an insurance policy

A classical continuous time surplus process is modified by adding two actions. If the level of the surplus goes below $\tau \ge 0$, we increase the level of the surplus up to initial level $u>\tau$ by injecting capital to the surplus. Meanwhile, the excess amount of the surplus over $V>u$ is invested continuously to other business. After assigning several costs related to managing the surplus, we obtain the long-run average cost per unit time and illustrate a numerical example to show how to find an optimal investment policy minimizing the cost.     

Remarks on asymptotic behavior of weighted quadratic variation of subfractional Brownian motion

The present note is devoted to prove, by means of Malliavin calculus, the convergence in $L^2$ of some properly renormalized weighted quadratic variation of sub-fractional Brownian motion $S^H$ with parameter $H<\frac{1}{4}$.     

Beta-Riesz distributions on symmetric cones

The Riesz distributions on a symmetric cone are used to introduce a class of beta-Riesz distributions. Some fundamental properties of these distributions are established. In particular, we study the effect of a projection on a beta-Riesz distribution and we give some properties of independence. We also calculate the expectation of a beta-Riesz random variable. As a corollary, we give the regression on the mean of a Riesz random variable; that is, we determine the conditional expectation $E(U\mid U+V)$ where $U$ and $V$ are two independent Riesz random variables.     

Optimal number of components in a load-sharing system for maximizing reliability

A $k$-out-of-$n$:G load sharing system is a cluster of $n$ components designed to withstand a certain amount of load in field operation, working only if no fewer than $k$ components work. Previous research on a load sharing system has focused on predicting the time-independent reliability from the stress–strength model or estimating the unknown parameters of the time-dependent reliability for a given load sharing rule. Differently, in this paper, we consider the problem of determining the optimal $n$ to maximize the reliability of both $n$-out-of-$n$:G and ($n$–$1$)-out-of-$n$:G load sharing systems. Since the load of each component decreases in $n$, the proportional hazard model is employed to relate the component failure rate with the load, assuming that the components, which have exponential distributions for given loads, are independent of each other. We then derive a sufficient condition under which a smaller number of components each withstanding a high load is preferred to a larger number of components each withstanding a small load. A numerical example is given for the rocket propulsion system to illustrate the result.