Indian Agricultural Statistics Research Institute

About: Indian Agricultural Statistics Research Institute is a facility organization based out in New Delhi, India. It is known for research contribution in the topics: Population & Small area estimation. The organization has 454 authors who have published 870 publications receiving 7987 citations.

Dual designs and their application in genetical experiments

Abstract: The dual of incomplete block designs has been studied with their applications in genetical experiments. Partial diallel crosses (PDC) of type I have been constructed using balanced incomplete block (BIB) designs, partially balanced incomplete block (PBIB) designs and their dual designs. Simplified analysis of PDC has been presented using the dual property of these designs. List of optimal PDC having simple analysis has been given.

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.

Shrunken predictors in stratified sampling under super-population model

Abstract: Using the concept of the shrinkage technique [J.F. Thompson, Some shrinkage techniques for estimating the mean, J. Am. Stat. Assoc. 63 (1968), pp. 113–122], Bouza [Robustness of shrunken predictors in stratified populations, Biom. J. 36 (1994), pp. 95–102] studied the robustness of the shrunken predictors measured in terms of bias and mean square error in stratified sampling under super-population model ξ (0, 1:1). He showed that the robustness depends on stratified balanced sample and the difference between the common slope (β ) and uncommon slopes (βh) to strata. In the present paper, a new set of predictors is proposed under the model ξ (0, 1:1) in stratified sampling. The proposed predictors are compared with that due to Bouza [Robustness of shrunken predictors in stratified populations, Biom. J. 36 (1994), pp. 95–102] and it has been found more efficient under certain conditions. The robustness of the proposed predictors is also studied on the same lines as discussed by Bouza [Robustness of shrunken ...

A Review of Block Designs for Test Treatments – Control(s) Comparisons

Abstract: In practice there may arise experimental situations where it is desired to compare several treatments called the test treatments to a standard treatment called control. The main interest here lies in making test treatment-control comparison with as much precision as possible and comparison within the test treatments are of less importance. For example in agricultural experiments, the aim of the experimenter is to test a set of new varieties of a crop with an already existing variety and to determine which of the varieties perform better in comparison to the existing variety. Balanced Treatment Incomplete Block (BTIB) designs have been defined for this situation. The designs are balanced with respect to test treatment-control comparisons. The concept of BTIB is further extended to define Balanced Two Disjoint Sets of Treatments (BTDT) designs when there are more than one control. Some methods of constructing these designs are presented here. Some class of row-column designs, which are balanced for test treatments vs. control comparisons, referred to as the Balanced Treatment vs. Control Row- Column (BTCRC) designs are also described when heterogeneity is to be eliminated in two directions. Key words: Balanced Treatment Incomplete Block (BTIB) design; Balanced Two Disjoint Sets of Treatments (BTDT) design; Balanced Treatment vs. Control Row-Column (BTCRC) design. DOI: http://dx.doi.org/10.4038/jfa.v2i1.3939 JFA 2009; 2(1): 22-29

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.