(1.5) R(x) > cx log log log log x, (1 .6) R(x) < cx log log log log x, (1 .7) H(x) > c log log log log x, (1 .8) H(x) < c log log log log x . In this paper we propose to continue the study of the error function H(x), and will prove that H(x) possesses a continuous distribution function . By this we mean that for N(n, u) = the number of m < n such that H(m) > u, we have for each u, o < u < -, th...

this paper aims to investigate the effect of budget deficit shock on government spending in indonesia. for this propose, this reasearch uses an alternative error correction model based on loss function of government spending. the model assumes the short run disequilibrium, in which shock variables may play an important role. a spesific loss function model is applied to develop the long run gove...

parameters as interception, infiltration, water storage on surface holes and soil profile, evapotranspiration are factors of loss water in a watershed and avoid from changing of precipitation to runoff. in this study, by use of hec-hms model and comparison of results of different methods of precipitation loss evaluation (initial and constant, green & ampt, scs curve number, deficit & constant a...

We study universal prediction w.r.t. an indexed class of sources (e.g., parametric families) and general loss functions. We explore the centrality of the self-information loss function (log-loss) in the theory of universal prediction by showing that under certain assumptions, the feasibility of universal prediction w.r.t. the log-loss function, over an indexed class of sources (that is, univers...

AIM The aim of this study is to determine the factors influencing predicted survival time for patients with colorectal cancer (CRC) using parametric models and select the best model by predicting error's technique. BACKGROUND Survival models are statistical techniques to estimate or predict the overall time up to specific events. Prediction is important in medical science and the accuracy of ...

We consider the learning task consisting in predicting as well as the best function in a finite reference set G up to the smallest possible additive term. IfR(g) denotes the generalization error of a prediction function g, under reasonable assumptions on the loss function (typically satisfied by the least square loss when the output is bounded), it is known that the progressive mixture rule ĝ s...

We consider the learning task consisting in predicting as well as the best function in a finite reference set G up to the smallest possible additive term. If R(g) denotes the generalization error of a prediction function g, under reasonable assumptions on the loss function (typically satisfied by the least square loss when the output is bounded), it is known that the progressive mixture rule ĝ ...

A method is proposed to incorporate mixture density splitting into the acoustic model discriminative training for speech recognition. The standard method is to obtain a high resolution acoustic model by maximum likelihood training and density splitting, and then improving this model by discriminative training. We choose a log-linear form of acoustic model because for a single Gaussian density p...