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Testing of Poisson Incidence Rate Restriction
Singh, Karan,Shanmugam, Ramalingam The Korean Reliability Society 2001 International Journal of Reliability and Applicati Vol.2 No.4
Shanmugam(1991) generalized the Poisson distribution to capture a restriction on the incidence rate $\theta$ (i.e. $\theta$ < $\beta$, an unknown upper limit), and named it incidence rate restricted Poisson (IRRP) distribution. Using Neyman's C($\alpha$) concept, Shanmugam then devised a hypothesis testing procedure for $\beta$ when $\theta$ remains unknown nuisance parameter. Shanmugam's C ($\alpha$) based .results involve inverse moments which are not easy tools, This article presents an alternate testing procedure based on likelihood ratio concept. It turns out that likelihood ratio test statistic offers more power than the C($\alpha$) test statistic. Numerical examples are included.
A Characterization of Negative Binomial Distribution Truncated at Zero
Shanmugam, R. The Korean Statistical Society 1982 Journal of the Korean Statistical Society Vol.11 No.2
Analogous to Singh's (1978) characterization of positive-Poisson distributioin and Shanmugam and Singh's (1992) characterization of logarithmic series distribution, a characterization and its statistical application of the negative binomial distribution truncated at zero are given in this paper. While it is known that under certain conditions the negative binomial distribution truncted at zero approaches the positive-Poisson and the logarithmic series distributions, we show here that the results of this paper approach in limit the results of Singh, and Shanmugam and Singh, respectively. Using the biologicla data from Sampford (1955), we illusrate our results. Also, expressions are explicitly given to test the hypothesis whether a random sample is indeed from a geometric distribution.
Shanmugam, Palanisamy,Ahn, Yu-Hwan,Sanjeevi , Shanmugam The Korean Society of Remote Sensing 2005 大韓遠隔探査學會誌 Vol.21 No.3
This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.
Shanmugam, P.,Ahn, Yu-Hwan,Sanjeevi, S.,Manjunath, A.S. The Korean Society of Remote Sensing 2003 大韓遠隔探査學會誌 Vol.19 No.5
As the launches of a series of remote sensing satellites, there are various multiresolution and multi-spectral images available nowadays. This diversity in remotely sensed image data has created a need to be able to integrate data from different sources. The C-band imaging radar of ERS-2 due to its high sensitivity to coastal wetlands holds tremendous potential in mapping and monitoring coastal wetland features. This paper investigates the advantages of using ERS-2 SAR data combined with IRS-ID LISS-3 data for mapping complex coastal wetland features of Tamil Nadu, southern India. We present a methodology in this paper that highlights the mapping potential of different combinations of filtering and integration techniques. The methodology adopted here consists of three major steps as following: (i) speckle noise reduction by comparative performance of different filtering algorithms, (ii) geometric rectification and coregistration, and (iii) application of different integration techniques. The results obtained from the analysis of optical and microwave image data have proved their potential use in improving interpretability of different coastal wetland features of southern India. Based visual and statistical analyzes, this study suggests that brovey transform will perform well in terms of preserving spatial and spectral content of the original image data. It was also realized that speckle filtering is very important before fusing optical and microwave data for mapping coastal mangrove wetland ecosystem.
Shanmugam Suresh Kumar,Vetriselvi Sampath,Jae Hong Park,In Ho Kim 한국가금학회 2021 韓國家禽學會誌 Vol.48 No.4
In this study, we investigated the effects of feeding diets with different levels of energy and nutrient density on the egg quality of laying hens during the pre-peak and peak periods. A total of 192 (Hy-line brown) laying hens were used in a 15-week trial. The hens were randomly allotted to one of four treatments, each with four replicates (12 hens per replication). We assessed the effects of four level of dietary energy (2,710, 2,850, 2,870 and 2,890 kcal/kg) and three levels of nutrient density (Methionine + Cysteine: 0.56%, 0.85%, 0.80% and Crude Protein: 14.5%, 19%, 18%). Differences in the energy and nutrient density contents of diets showed no significant effect (P>0.05) on the average daily gain, average daily feed intake, feed conversion ratio, egg weight, or egg production of hens during the pre-peak and peak periods. However, hens subjected to 2,890 kcal/kg during the pre-peak period were found to lay eggs with significantly thicker shells, and yolk color was found to be significant enhanced in hens fed this diet during the pre-and peak periods. In contrast, we detected no significant effects of dietary energy or nutrient density on the Haugh unit or eggshell strength. In summary, increasing the energy level of diets from 2,710 to 2,890 kcal/kg was found to have positive effects on the shell thickness and yolk color of eggs produced by laying hens.