There is significant interest in developing new acoustic models for speech recognition that overcome traditional HMM restrictions. In this work, we propose to use Ngram based augmented HMMs. Two approaches are presented. The first one consists on overcoming the parameter independence assumption. This is achieved by modeling the dependence between the different acoustic parameters, using N-gram ...

We develop a √ n-consistent and asymptotically normal estimator of the parameters (regression coefficients and threshold points) of a semiparametric ordered response model under the assumption of independence of errors and regressors. The independence assumption implies shift restrictions allowing identification of threshold points up to location and scale. The estimator is useful in various ap...

Naïve Bayes (NB) is an efficient and effective classifier in many cases. However, NB might suffer from poor performance when its conditional independence assumption is violated. While most recent research focuses on improving NB by alleviating the conditional independence assumption, we propose a new Meta learning technique to scale up NB by assuming an altered strategy to the traditional Casca...

Several methods for screening gene-environment interaction have recently been proposed that address the issue of using gene-environment independence in a data-adaptive way. In this report, the authors present a comparative simulation study of power and type I error properties of 3 classes of procedures: 1) the standard 1-step case-control method; 2) the case-only method that requires an assumpt...

Several methods for screening gene-environment interaction have recently been proposed that address the issue of using gene-environment independence in a data-adaptive way. In this report, the authors present a comparative simulation study of power and type I error properties of 3 classes of procedures: 1) the standard 1-step case-control method; 2) the case-only method that requires an assumpt...

The Elephant in the Corner: A Cautionary Tale about Measurement Error in Treatment Effects Models Researchers in economics and other disciplines are often interested in the causal effect of a binary treatment on outcomes. Econometric methods used to estimate such effects are divided into one of two strands depending on whether they require the conditional independence assumption (i.e., independ...

In automobile insurance, it is useful to achieve a priori ratemaking by resorting to generalized linear models, and here the Poisson regression model constitutes the most widely accepted basis. However, insurance companies distinguish between claims with or without bodily injuries, or claims with full or partial liability of the insured driver. This paper examines an a priori ratemaking procedu...

Actuaries intuitively feel that positive correlations between individual risks reveal a more dangerous situation compared to independence. The purpose of this short note is to formalize this natural idea. Specifically, it is shown that the sum of risks exhibiting a weak form of dependence known as positive cumulative dependence is larger in convex order than the corresponding sum under the theo...

holds. This is the assertion of the Second Borel-Cantelli Lemma. If the assumption of independence is dropped, (1) fails in general. However, (1) remains true if independence is replaced by pairwise independence (Durrett (1991), Theorem (6.6) ch. 1) or by uniform mixing (Iosifescu and Theodorescu (1969) Lemma 1.1.2’). Rieders (1993) showed by an example that strong mixing without any further as...

1 Naive Bayes, 20 points Problem 1. Basic concepts, 10 points Naive Bayes reduces the number of parameters that must be estimated for a Bayesian classifier, by making a conditional independence assumption when modeling P (X|Y). The definition for conditional independence is the following: Given this definition, please answer the following questions: