International journal of applied mathematics and statistics 

About: International journal of applied mathematics and statistics is an academic journal. The journal publishes majorly in the area(s): Estimator & Fuzzy logic. It has an ISSN identifier of 0973-1377. Over the lifetime, 788 publications have been published receiving 3102 citations.

Chi-squared goodness-of-fit test for right censored data

Abstract: In this communication we consider chi-squared goodness-of-fit tests from censored data. Choice of random grouping intervals as data functions is considered. Simple formulas useful for computing of test statistics for mostly applied classes of survival distributions are given.

On The Use of Chain-Type Ratio to Difference Estimator in Successive Sampling

Abstract: Use of auxiliary information for improving the precision of estimates is well known technique in sample surveys. In successive (rotation) sampling it is advantageous to use the information collected on previous occasions for improving the precision of estimates at current occasion. The previous information may be in form of an auxiliary character or the character under study itself or both. In the present work, a new chaintype ratio to difference estimator has been proposed using the information on an auxiliary character in successive (rotation) sampling over two occasions. The proposed estimator has been compared with sample mean estimator when there is no matching and the optimum estimator, which is a combination of the means of the matched and unmatched portions of the sample at the second occasion. Optimum replacement policy is also discussed. Results have been justified empirically.

Some Convergence Theorems of Uncertain Sequence

Abstract: The emphasis in this paper is mainly on some convergence theorems of uncertain sequence. Firstly, five main types convergence concepts of uncertain sequence: convergence almost surely (a.s.), convergence almost uniformly (a.u.), convergence in uncertain measure, convergence in uncertain distribution and convergence in mean square (ms) are proposed. Secondly, several sufficient and necessity conditions and relations among of convergence concepts are presented. Thirdly, Egorov theorem, monotone convergence theorem, Fatou lemma and bounded convergence theorem of expected value for uncertain sequence on continuous uncertainty space are obtained.

Fitting Smooth Paths on Riemannian Manifolds

Abstract: In this paper we formulate a least squares problem on a Riemannian manifold M , in order to generate smoothing spline curves fitting a given data set of points in M , q0, q1, . . . , qN , at given instants of time t0 < t1 < · · · < tN . Using tools from Riemannian geometry, we derive the Euler-Lagrange equations associated to this variational problem and prove that its solutions are Riemannian cubic polynomials defined at each interval [ti, ti+1[, i = 0, . . . , N − 1, and satisfying some smoothing constraints at the knot points ti. The geodesic that best fits the data, arises as a limiting process of the above. When M is replaced by the Euclidean space IR, the proposed problem has a unique solution which is a natural cubic spline given explicitly in terms of the data. We prove that, in this case, the straight line obtained from the limiting process is precisely the linear regression line associated to the data. Using tools from optimization on Riemannian manifolds we also present a direct procedure to generate geodesics fitting a given data set of time labelled points for the particular cases when M is the Lie group SO(n) and the unitary n−sphere S.