Identification of survival functions through hazard functions in the Clayton-family

In a multivariate nonparametric setup the survival function is not identifiable from the hazard function. Things may change, however, if we restrict ourselves to semiparametric submodels. In this note we show that for the Clayton survival model, the answer is affirmative.     

On the identifiability of multivariate survival distribution functions

Let (T₁, T₂) be a non-negative random vector which is subjected to censoring random intervals [X₁, Y₁] and [X₂, Y₂]. The censoring mechanism is such that the available informations on T₁ and T₂ are expressed by a pair of random vectors W=(W₁, W₂) and δ=(δ₁, δ₂), where W_i=max(min(Y_i, T_i), X_i) and In this paper we will show that under some mild conditions the joint survival function of T₁ and T₂ can be expressed uniquely as functional of observable joint survival functions. Our results extend recent works on the randomly right censored bivariate data case and on the univariate problem with double censoring to the bivariate data with double censoring.     

Identifiability of the multinormal and other distributions under competing risks model

Let X₁, X₂ ,…, X_p be p random variables with joint distribution function F(x₁ ,…, x_p). Let Z = min(X₁, X₂ ,…, X_p) and I = i if Z = X_i. In this paper the problem of identifying the distribution function F(x₁ ,…, x_p), given the distribution Z or that of the identified minimum (Z, I), has been considered when F is a multivariate normal distribution. For the case p = 2, the problem is completely solved. If p = 3 and the distribution of (Z, I) is given, we get a partial solution allowing us to identify the independent case. These results seem to be highly nontrivial and depend upon Liouville's result that the (univariate) normal distribution function is a nonelementary function. Some other examples are given including the bivariate exponential distribution of Marshall and Olkin, Gumbel, and the absolutely continuous bivariate exponential extension of Block and Basu.     

Identifiability of kinetic models of isotope transient tracing in fixed bed plug flow reactors

A novel method is presented to address the identifiability of a class of kinetic models of monoatomic isotope transient tracing in plug flow reactors. It is shown that the identifiability of these models can be addressed solely from a knowledge of the rate functions. Dedicated to Professor Pál Tétényi on the occasion of his 70th birthday     

Reconstruction families of possibilistic structure systems

A method is presented for characterizing the family of overall systems reconstructable from a given possibilistic structure system. The technique is elaborated in terms of fuzzy relation equations.It is demonstrated that the reconstruction family of a given structure system is equivalent to the set of solutions of a special type of fuzzy relation equation. The solution set is partially ordered, and contains both minimal solutions and a unique maximum solution. When these elements are identified, all members of the reconstruction family are determined.Another characteristic of the reconstruction family is its reconstruction uncertainty, a measure of which is also developed in this paper. This measure is used to define an identifiability quotient that expresses the degree of confidence with which we may identify a single overall system given a particular structure system.     

Identifiability of finite mixtures using a new transform

Identifiability of finite mixtures of the following families of distributions are proved: Weibull, normal log, chi, pareto and power function.     

Takagi-Sugeno模糊模型的可辨识性问题

Takagi—Sugeno模糊模型广泛应用于对复杂系统的辨识,但可辨识性问题很少涉及。本文给出在规则数和规则前件确定的情况下,Takagi—Sugeno模糊模型的可辨识性条件。     

Identifiability issues of age–period and age–period–cohort models of the Lee–Carter type

The predominant way of modelling mortality rates is the Lee–Carter model and its many extensions. The Lee–Carter model and its many extensions use a latent process to forecast. These models are estimated using a two-step procedure that causes an inconsistent view on the latent variable. This paper considers identifiability issues of these models from a perspective that acknowledges the latent variable as a stochastic process from the beginning. We call this perspective the plug-in age–period or plug-in age–period–cohort model. Defining a parameter vector that includes the underlying parameters of this process rather than its realizations, we investigate whether the expected values and covariances of the plug-in Lee–Carter models are identifiable. It will be seen, for example, that even if in both steps of the estimation procedure we have identifiability in a certain sense it does not necessarily carry over to the plug-in models.