Showing papers by "Indian Agricultural Statistics Research Institute published in 2005"

Nonparametric regression with autocorrelated errors methodology for describing India's marine production data

Abstract: Nonparametric regression with autocorrelated errors methodology for describing India's marine production data

Mass Transfer During Osmotic Dehydration of Banana (Dwarf Cavendish) Slices

Abstract: Mass transfer during osmotic dehydration of banana has been studied with respect to the solution concentration (30–70°Brix), temperature (25–65°C) and solution to sample ratio (1–9). The diffusion coefficients have been calculated using the sorption data by a method of successive approximations. The diffusion coefficient has been empirically correlated with the process variables and a high degree of correlation (R2 = 0.91) was observed between predicted and experimental values. The solution of Fick's Law for unstady state mass transfer in a plane sheet has been used to predict the moisture ratios over the entire experimental range. The error between the observed and the predicted values of moisture ratios was less than 10% indicating the adequacy of diffusion coefficient estimation.

Optimal Designs for Best Linear Unbiased Prediction in Diallel Crosses

Abstract: Recently, Ghosh and Das (2003) considered the estimation of variance components and the variances of these estimates. While comparing the yielding capacities of the cross (i, j), Kempthorne and Curnow (1961) proposed the estimation of the yielding capacity of any cross based on the least square estimators of the general combining ability effects and/or the mean yield of the cross (i, j). In this article, the problem of predicting the yielding capacity of the cross (i, j) from the sample of inbred lines has been considered. The properties of the best linear unbiased predictor for predicting the unobserved general combining ability effects together with general mean effect has been studied. We characterize A-optimal complete diallel cross designs and some efficient partial diallel cross designs under this setup.

Fitting of nonlinear Schaefer model

Abstract: Nonlinear Schaefer model is extensively used to obtain maximum sustainable yield and corresponding optimum fishing effort. One reason for its widespread use is that mere knowledge of catch and effort time-series data, which is readily available for most fisheries, is sufficient to apply this model. Nonlinear estimation procedures, like Levenberg-Marquardt method are employed for fitting purposes as the inherent relationship is nonlinear. However, if the parameters of this model are replaced by ‘expected-value’ parameters, the resulting form, as a pleasant surprise, reduces to an exact linear form. Thus the nonlinear Schaefer model can be fitted easily by using “Multiple linear regression” technique. The procedure is illustrated on some shrimps catch - effort data.

Comparison of bootstrap methods for missing survey data: A simulation study

Abstract: Bootstrap technique is used in the estimation of variance of non-linear statistics in case of complex surveys. This technique is gaining popularity for survey data with missing observations. In this paper, bootstrap techniques with missing observations have been compared through a simulation study under different imputation techniques. The technique namely “Proportional Bootstrap Without Replacement (PBWO)” for missing observations has also been compared with the Rescaling Bootstrap Without Replacement (RSBWO) method for complete data set. Further, the efficiency of Proportional Bootstrap With Replacement (PBWR) technique for missing observations has been compared with the standard bootstrap technique for complete data set. An optimum number of bootstrap samples required for the reliable estimation of variance in the case of missing observations has also been obtained.

Mixture Nonlinear Time-Series Analysis : Modelling and Forecasting

Abstract: Gaussian mixture transition distribution (GMTD) models and mixture autoregressive (MAR) models are generally employed to describe those data sets that depict sudden bursts, outliers and flat stretches at irregular time epochs. In this paper , these two approaches are compared by considering weekly wholesale onion price data during April, 1998 to November, 2001. After eliminating trend, seasonal fluctuations are studied by fitting BoxJenkins airline model to residual series. To this end, null hypothesis of presence of nonseasonal and seasonal stochastic trends is tested by using OsboruChuiSmithBirchenhall (OCSB) auxiliary regression. Subsequently, appropriate filters in airline model for seasonal fluctuations are selected. Presence of autoregressive co nditional heteroscedasticity (ARCH) is tested by Naive Lagrange multiplier (Nave LM) test. Estimation of parameters is carric~d out using ExpectationMaximization (EM) algorithm and the best model is selected on the basis of Bayesian information criterion (BIC). Outofsample forecasting is performed for onestep and twostep ahead prediction by uaive approach, proposed by Wong and Li (2000). It is concluded that, for data under consideration, a threecomponent MAR model performs the best.