Japanese Dairy Cattle Productivity Analysis is carried out based on Bayesian Network Model (BNM). Through the experiment with 280 Japanese anestrus Holstein dairy cow, it is found that the estimation for finding out the presence of estrous cycle using BNM represents almost 55% accuracy while considering all samples. On the contrary, almost 73% accurate estimation could be achieved while using s...

Sophisticated voters assess incumbent competence by filtering out economic cycles (which they do not like) from trend growth (which they do). Naive voters on the other hand respond only to raw economic growth. This implies that voting in aggregate should respond asymmetrically to the economic cycle. Upswings are rewarded by the naive, but punished by the sophisticated. Downswings are punished b...

The problem of variance estimation for a t ime series with linear trend is studied using model based procedures. The work done by Royall and Cumberland ((2), (3)) is refined and adapted to the special problems of the Current Employment Survey (790 Survey) at the Bureau of Labor Statistics. A variety of variance estimation techniques are examined; including a variation of jackknife estimation an...

We propose a method to detect the onset of linear trend in a time series and estimate the change point T from the profile of a linear trend test statistic, computed on consecutive overlapping time windows along the time series. We compare our method to two standard methods for trend change detection and evaluate them with Monte Carlo simulations for different time series lengths, autocorrelatio...

A common problem in the analysis of time series is how to deal with a possible trend component, which is usually thought of as large scale (or low frequency) variations or patterns in the series that might be best modeled separately from the rest of the series. Trend is often confounded with low frequency stochastic fluctuations, particularly in the case of models such as fractionally differenc...

We consider the use of generalized additive models with correlated errors for analysing trends in time series. The trend is represented as a smoothing spline so that it can be extrapolated. A method is proposed for choosing the smoothing parameter. It is based on the ability to predict a short term into the future. The choice not only addresses the purpose in hand, but also performs very well, ...

In this paper, we address the problem of frequency estimation when multiple stationary nonsinusoidal resonances oscillate about a trend in nonuniformly sampled data when the number and shape of the resonances are unknown. To solve this problem we postulate a model that relates the resonances to the data and then apply Bayesian probability theory to derive the posterior probability for the numbe...

A jump process approach is proposed for the trend estimation of time series. The proposed jump process estimator can locally minimize two important features of a trend, the smoothness and 0delity, and explicitly balance the fundamental tradeo2 between them. A weighted average form of the jump process estimator is derived. The connection of the proposed approach to the Hanning 0lter, Gaussian ke...

In the general geometric asset price model, the asset price P (t) at time t satisfies the relation P (t) = P0 · eα·f(t)+σ·F (t) , t ∈ [0, T ], where f is a deterministic trend function, the stochastic process F describes the random fluctuations of the market, α is the trend coefficient, and σ denotes the volatility. The paper examines the problem of optimal trend estimation by utilizing the con...