We show several high-probability concentration bounds for learning unigrams language model. One interesting quantity is the probability of all words appearing exactly k times in a sample of size m. A standard estimator for this quantity is the Good-Turing estimator. The existing analysis on its error shows a high-probability bound of approximately O ( k √ m ) . We improve its dependency on k to...

let be a random sample from a normal distribution with unknown mean and known variance the usual estimator of the mean, i.e., sample mean is the maximum likelihood estimator which under squared error loss function is minimax and admissible estimator. in many practical situations, is known in advance to lie in an interval, say for some in this case, the maximum likelihood estimator changes and d...

The paper presents a new (to the best of the authors’ knowledge) estimator of probability called the “Eph√2 completeness estimator” along with a theoretical derivation of its optimality. The estimator is especially suitable for a small number of sample items, which is the feature of many real problems characterized by data insufficiency. The control parameter of the estimator is not assumed in ...

Equality and proportionality of the ordinary least-squares estimator (OLSE), the weighted least-squares estimator (WLSE), and the best linear unbiased estimator (BLUE) for Xb in the general linear (Gauss–Markov) model M 1⁄4 fy;Xb; sRg are investigated through the matrix rank method. r 2006 Elsevier B.V. All rights reserved. MSC: Primary 62J05; 62H12; 15A24

We propose a communication-e cient distributed estimation method for sparse linear discriminant analysis (LDA) in the high dimensional regime. Our method distributes the data of size N into m machines, and estimates a local sparse LDA estimator on each machine using the data subset of size N/m. After the distributed estimation, our method aggregates the debiased local estimators from m machines...