1,721,188 research outputs found
Indian NARS Statistical Computing Usage 2012-2016
No full textThis is log on Users on Indian NARS Statistical Computing Porta
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No full textNot AvailableThe Society of Statistics, Computer and Applications was founded in 1998 with a goal to
provide a platform for promotion and dissemination of research in Statistics among the
statisticians, who have keen interest in the applications of Statistics to varied fields like
agriculture, biological sciences, medical sciences, finance statistics, and industrial statistics
blended with information technology. Since then the Society has been performing several
activities and promoting development of theoretical and applied research work in Statistics.
The Nineteenth Annual Conference of the Society of Statistics, Computer and Applications
(SSCA) was organized during 06-08 March 2017 at the Department of Statistics and
Computer Science, Sher-e-Kashmir University of Agricultural Sciences and Technology of
Jammu (SKUAST-J), Jammu, Jammu & Kashmir. The conference was academically very
enriching. There were some very interesting and important presentations made by scientists
of international repute and eminence. Among the many technical sessions organized, was a
session on Macro-Financial Statistics, in which delegates from Reserve Bank of India,
Securities and Exchange Board of India, National Bank for Agriculture and Rural
Development, Ministry of Statistics and Programme Implementation, Government of India
made presentations.
The Executive Council of the Society decided to bring out “Special Proceedings" of the
conference covering Keynote address, Dr. M.N. Das Memorial Lecture, Plenary talks and all
the presentations made in the session on Macro-Financial Statistics. The Executive Council
of the Society nominated Aloke Dey, V.K. Gupta, Rajender Parsad, Ashish Das, L.M. Bhar
and Dipak Roy Choudhary as the Guest editors for bringing out these special proceedings.
Distinguished speakers for Keynote Address, Dr. M.N. Das Memorial Lecture, Plenary
lectures and invited speakers in the session on Macro-Financial Statistics were invited to
submit research papers for possible inclusion in the special proceedings. After the usual
review process, 09 research papers were accepted for publication and are included in these
special proceedings.Not Availabl
Annual Report 2006 - 2007
No full textNot AvailableIn agricultural experiments, generally data on more than one character is observed. The experiments, where corresponding to the application of a treatment, more than one response variables are recorded, are known as multi-response experiments. Analytical procedures of analysis of data from multi-response experiments conducted using block designs have been developed. A method based on Euclidean distance and J-plot has also been developed for identification of the best treatment. To tackle the problem of outlier(s) in multi-response experiments, a test statistic has been developed for identification of a single outlier observation vector in complete multi-response experiments run in a block design. A catalogue of block designs with minimally connected designs with 4, 5, 6, 7 or 8 extra observations has been prepared alongwith lower bounds to A- and D- efficiencies and block contents. Extended Group Divisible (EGD) designs for three factors that permit the estimation of all main effects with no loss of information were obtained using self-complementary GD designs with replication number less than 6 and block size less than 11. A catalogue of such designs along with efficiencies for main effects and interactions was prepared. These designs are useful for crop sequence experiments. Results on non-existence of NPBIB designs based on group divisible designs has been obtained. Methods of construction of NPBIB designs based on Latin Square association scheme and Rectangular association scheme have been obtained and catalogued. Nested block designs for nearest neighbour correlation structure within sub-blocks of a block in a nested block design set up and for zero correlation structure in bigger blocks ignoring the sub-block classification were obtained. Analytical techniques based on mixed effects models and biplots have been developed for analysis of data generated from Farmers’ participatory research trials for Resource Conservation Technologies conducted by the Rice-Wheat Consortium (RWC) for Indo-Gangetic Plains. Doubly nested partially balanced incomplete block designs have been introduced. Some general methods of construction of doubly nested partially balanced incomplete block designs are obtained using doubly nested balanced incomplete block (DNBIB) designs, nested balanced incomplete block designs and partially balanced incomplete block designs. Robustness of BIB and PBIB [Group Divisible (GD) and Cyclic] designs has been studied under correlated error structure [NN and AR(1)] for a given value of correlation coefficient in terms of A-efficiency. Binary variance balanced block designs have been shown robust in the presence of two outliers. Robust estimation procedure of treatment effects based on Least Median Squares has been developed. Developed a beta version of On-line Software for the generation of Hadamard matrices up to the order 1000. Supersaturated designs for asymmetrical factorial experiments have been obtained using resolvable orthogonal arrays and Hadamard matrices. Some criteria for comparing supersaturated designs for asymmetrical factorial experiments are also given. To disseminate the knowledge available on combinatorial aspects of designs and analytical procedures acquired to scientists engaged in research in the National Agricultural Research System. The advisory services are pursued rigorously. For the benefit of the experimenters and practicing statisticians, Design Resources Server has been strengthened by adding 6574 efficient block designs for making all possible pairwise treatment comparisons.Not Availabl
Annual Report 2005 - 2006
No full textNot AvailableAlpha designs are essentially resolvable block designs. In a resolvable block design, the blocks can be grouped such that in each group, every treatment appears exactly once. Resolvable block designs allow performing an experiment with one replication at a time. For example, field trials with large number of crop varieties cannot always be laid out in a single location or a single season. Therefore, it is desired that variation due to location or time periods may also be controlled along with controlling within location or time period variation. This can be handled by using resolvable block designs. Here, locations or time periods may be taken as replications and the variation within a location or a time period can be taken care of by blocking. In an agricultural field experiment, the land may be divided into a number of large areas corresponding to the replications and then each area is subdivided into blocks. These designs are also quite useful for varietal trials conducted in the National Agricultural Research System (NARS) and will help in improving the precision of treatment comparisons. A critical look at the experimentation in the NARS reveals that alpha designs have not found much favour from the experimenters. It may possibly be due to the fact that the experimenters find it difficult to lay their hands on alpha designs. The construction of these designs is not easy. An experimenter has to get associated with a statistician to get a randomized layout of this design. For the benefit of the experimenters, a comprehensive catalogue of alpha designs upto 150 treatments has been prepared along with lower bounds to A- and D- efficiencies and generating arrays. The layout of these designs along with block contents has also been prepared. The designs obtained have been compared with corresponding square lattice, rectangular lattice, resolvable two-associate class partially balanced incomplete block (PBIB (2)) designs and the -designs obtainable from basic arrays given by Patterson, Williams and Hunter (1978, J. Agric. Sci., 90, 395-499). Eleven designs are more efficient than the corresponding resolvable PBIB (2) designs (S11, S38, S69, S114, LS8, LS30, LS54, LS76, LS89, LS126 and LS140). It is interesting to note here that for the PBIB (2) designs based on Latin square association scheme, the concurrences of the treatments were 0 or 2 and for singular group divisible designs the concurrences are either 1 or 5. Further all the designs LS8, LS30, LS54, LS76, LS89, LS126 and LS140 were obtained by taking two copies of a design with 2-replications. 10 designs were found to be more efficient than the designs obtainable from basic arrays. 48 designs (29 with k = 4 and 19 with k = 3) are more efficient than the designs obtainable by dualization of basic arrays. 25 designs have been obtained for which no corresponding resolvable solution of PBIB(2) designs is available in the literature. The list of corresponding resolvable PBIB(2) designs is S28, S86, SR18, SR41, SR52, SR58, SR66, SR75, SR80, R42, R70, R97, R109, R139, T14, T16, T20, T44, T48, T49, T72, T73, T86, T87 and M16. Here X # denotes the design of type X at serial number # in Clatworthy, W. H. (1973, Table of two-associate partially balanced designs. NBS Applied Maths Series No. 63. Washington D.C.). In some experimental situations, the user may be interested in getting designs outside the above parametric range. To circumvent such situations, a beta Version of user friendly software module for the generation of -designs has been developed. This module generates the alpha array along with lower bounds to A and D-efficiency. The -array and the design is generated once the user enter the number of treatments (v), number of replications (r) and the block size (k). The module generates the design for any v, k, r provided v is a multiple of k. It also gives the block contents of the design generated. A nested block design is defined as two systems of blocks such that the second system of blocks is nested within the first system of blocks. These designs are quite useful in many experimental situations. For example, consider a field experiment conducted using a block design and harvesting is done block wise. Harvested samples are to be analyzed for their contents either by different technicians at same time or by a technician over different periods of time. The variation due to technicians or time periods may be controlled by another blocking system. Technicians or time periods form a system of blocks that are nested within blocks. Such experimental situations are also common in post harvest value addition of horticultural and vegetable crops. Nested block designs are also quite useful in agricultural field experiments where the plots with similiar fertility occur in patches rather than in a uniform direction. Preece, D.A. (1967 Biometrika, 54, 479-486) was the first to introduce nested block designs and termed them as nested balanced incomplete block (NBIB) designs. In a NBIB design block classification ignoring sub-blocks is a balanced incomplete block (BIB) design and sub-block classification ignoring blocks is also a BIB design. We have prepared a complete catalogue of NBIB designs with number of replications . The catalogue contains a total of 299 designs. Out of 299 designs, 8 designs are non-existent. A new method of construction of NBIB designs has been obtained. Using this method and trial and error solutions, block layouts of 22 new NBIB designs have been obtained. The layout of 199 designs with block contents has been completed. The solution for the block layout for remaining 92 designs is unknown and the statisticians need to develop methods of construction of these NBIB designs. The designs catalogued have also been identified for 1-resolvable and 2-resolvable sets. A NBIB design may not exist for all parametric combinations or even if it exists may require a large number of replications, which the experimenter may not be able to afford. To deal with such situations, nested partially balanced incomplete block (NPBIB) designs have been introduced in the literature. Some new methods of construction of NPBIB designs based on group divisible association scheme have been given using these methods of construction, 31 new NPBIB designs based on group divisible association scheme with r <=15 have been obtained. Nested block (NBIB and NPBIB) designs are useful for experimental situations where the experimenter is interested in making all possible pairwise treatment comparisons with as high a precision as possible. However, there do occur experimental situations where the experimenter is interested in comparing several new treatments (called test treatments) with existing practice (a control treatment) with high precision and the comparisons among the test treatments are not of much importance. To deal with such situations, nested balanced treatment incomplete block (NBTIB) designs have been introduced. Some new methods of construction of NBTIB designs making use of NBIB designs, initial block solutions, etc. have been developed. A new method of construction of nested block designs for making test treatments-control treatment comparisons has been developed which yields minimally connected designs with respect to sub-blocks. The design with respect to bigger blocks is a group divisible treatment design. A new method of construction of efficient block designs for making test treatments-control treatment comparisons by making use of triangular association scheme has been developed. The number of replications of test treatments developed through this method is always 2. A new method of construction of semi-Latin squares based on initial column solution has been developed. This method yields semi-regular group divisible designs after ignoring the row and column classifications. Preece and Freeman (1983, J. Royal. Statist. Soc., 28, 154-163) reported that for could not be obtained by rearrangement in Semi-regular group divisible designs. These three semi-Latin squares can be obtained from the proposed method of construction. A catalogue of block designs with n = v+b-1+i, i=1, 2, 3 observations has been prepared, where v is the number of treatments; b the block size; k is the block size and n is the total number of experimental units. Block contents along with lower bounds to A- and D-efficiencies are also given. The lower bounds to A-efficiencies for making test treatments-control treatment comparisons are being obtained. The design resources server has been initiated and launched on the web site of the Institute. The main objective of this design resources server is to develop a WEB DESIGN in NARS. At present material on binary balanced block designs and designs for making test treatments- control treatment comparisons along with Electronic Book on Design and Analysis of Agricultural Experiments are available on this site. A discussion board has been created. Advisory services have been pursued rigorously in the NARS. Alpha designs, second order response surface designs have been recommended. The sophisticated statistical analytical procedures like contrast analysis and variance component estimation have also been recommended. Fertilizer response ratios were obtained using the data pertaining to an experiment conducted to find out the response of N, P and K under different sub agro-ecological zones/NARP zones under the aegis of Project Directorate of Cropping Systems Research, Modipuram 1999-2000.Not Availabl
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No full textoai:krishi.icar.gov.in:123456789/585Not AvailableStatistical computing provides a strong base for any scientific investigation and enables to
answer quantitative biological questions so that the amount of information generated is
maximized. The present project on Strengthening Statistical Computing for NARS
(SSCNARS) funded by NAIP of ICAR targets at providing research guidance in statistical
computing and creating sound and healthy statistical computing environment through advanced,
versatile, innovative and state-of the-art high end statistical packages for analysis of data so as to
enable drawing meaningful and valid inferences and converting research output into knowledge.
This consortium has been implemented in 3-tier structure with first tier as lead centre, 08 NARS
organizations as Statistical Computing Hubs and 142 other NARS organizations as nodes; signed
MOU with each NARS organization for the enhanced usage of the environment created which
has resulted into effective collaboration among all NARS organizations. This effort is unique in
the sense that all NARS organizations are linked with each other. This has empowered the
statisticians and other researchers by providing the facilities of statistical computing to them as is
available at premier Institutes. It has provided the statisticians an opportunity to interact with
each and every researcher of NARS through training programmes and e-resources.
Statistical Computing labs have been established at lead Centre (with 20 desktop computers) and
at 08 other Statistical Computing Hubs (with 10 desktop computers at each hub).
General purpose Statistical Software Package(s) SAS EAS, JMP and JMP Genomics have been
procured with 151 licenses (100 Standalone and 50 Intranet Based) + SAS Enterprise Business
Intelligence Server with all modules of SAS Intranet based for perpetual use with three years
updates and upgrades by 151 different NARS Organizations. It can be installed on multiple
official machines. The software is installed on more than 2750 computers of 151 NARS
organizations (on an average more than 18 computers per NARS organization). More than 200
researchers/nodal officers from 151 NARS organizations have been trained through 35
Orientation-cum-Installation Training Programmes. This activity has ensured that trained
manpower to install the software is available at all the NARS organizations.
Capacity building efforts have been made through organization of training programmes
including subject specific ones, preparing e-manuals and case studies made available at subproject
website www.iasri.res.in/sscnars for rejuvenating statistical thinking and use of
appropriate advanced statistical techniques in agricultural research and teaching. For creating a
base for resource persons, 209 trainers have been trained through 30 working days training
programmes on SAS: A Comprehensive Overview and SAS Genetics/JMP Genomics and 6
days training programme on Data Analysis Using SAS across 85 NARS organizations.
In order to provide customized training to researchers in NARS, 2166 research personnel have
been trained on Data Analysis using SAS through 104 training programmes of one week
duration each including 37 trainings at different organizations and 15 subject specific
customized training programmes for specific needs of different institutions. 164 researchers
have been trained by nodal officers at Tamil Nadu Agricultural University, Coimbatore and
NRCAF, Jhansi; IGFRI, Jhansi; Junagarh Agricultural University, Junagarh. (156 research scholars and 90 RA/SRFs) have also been trained.
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To sensitize the scientists in NARS with the statistical computing capabilities available for
enhancing their computing and research analytics skills, organized 21 sensitization workshops
at different NARS organizations. For efficient and effective use of Internet Technologies for
delivering lectures to save funds and resources, several Webinar sessions were organized in
collaboration with CRIDA, Hyderabad where the licenses for Webinar sessions have been
procured. Webinar sessions were also organized through the use of Social Networking Tools
(Google Hangouts).
For providing service oriented computing, developed and established Indian NARS Statistical
Computing portal, which is available to NARS users through IP authentication at
http://stat.iasri.res.in/sscnarsportal. It is a paradigm of computing techniques that operate on
software-as-a-service. There is no need of installation of statistical package at client side.
Twenty-four different modules of analysis of data are available on this portal. These modules can
be used by uploading *.xls, *.xlsx, *.csv and *.txt files and results can be saved as *.RTF or
*.pdf files. This has helped the researchers in NARS in analyzing their data in an efficient
manner without losing any time. The portal is extensively being used throughout NARS and has
helped the researchers in analyzing their data without losing any time. INDIAN NARS
STATISTICAL COMPUTING PORTAL (both content and software) are copyrighted material
of ICAR with copyright numbers: L-55719/2013 and SW-7397/2013 issued on October 25, 2013
by Registrar, Copyrights. Some other IP authenticated services like Web Report Studio, BI
DashBoard, Web OLAP Viewer, E-Miner 6.1, E-Miner 7.1 have also been provided to the
researchers of NARS.
For creation of research data repository and standardization of analysis of data for All-India
Coordinated Sorghum Crop Improvement Project, a prototype has been developed by NAARM,
Hyderabad and IASRI, New Delhi in collaboration with Directorate of Sorghum Research
Hyderabad. This system was made operational and now made available at
www.aicsip.naarm.org.in. This would be shifted to IASRI, New Delhi after validation and
testing. The prototype for automation of All India Coordinated Sorghum Improvement Project
needs to be scaled up to cover all AICRPs through a separate mega network project for paving
the way for developing research data repositories, standardizing the analytical modules and
saving on time and resources. Already work in collaboration with AICRP on Vegetable Crops
has been initiated.
For customized analysis, macros for analysis of augmented designs, split-split plot designs, split
factorial designs, strip plot designs, econometric analysis (diversity indices, instability index,
compound growth rate, Garret scoring technique and demand analysis using LA-AIDS model)
have been developed and made available on the project website which are being extensively by
researchers.
Reference manuals have been prepared for the benefit of the researchers which include live
examples from different areas of agriculture, animal and fisheries research illustrating the
power of newer statistical computing techniques and are made available at the website of subproject.
These include Data Analysis Using SAS, Genetics/ Genomics Data Analysis Using
SAS, Data Analysis in Social Sciences Research Using SAS, Data Mining Using SAS, Data
Analysis of Agroforestry Experiments Using SAS, Data Analysis of Natural Resources
Management Research Using SAS; Data Analysis of Farm Implements and Machinery
Research; Data Analysis in Dairy Sciences Research; Data Analysis Using R.
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The capacity building efforts have paved the way for publishing research papers in the high
impact factor journals. Based on feedback received from NARS organizations, 173 research
reports, 260 research papers have been published / accepted for publication (in journals like
Animal Food Science and Technology, Field Crops Research, Journal of Food Engineering,
Euphytica, Journal of Applied Polymer Science, etc.) by analyzing the data using high end
statistical computing facility (out of these 260 research papers, 135 are in Journals with NAAS
rating 6.0); more than 300 students have used this in their dissertations; 1700 students have
used in their course work. The number of data sets analyzed is more than 6000 across NARS.
Nodal officer from CMFRI, Kochi has reported a saving of 20 man months in compilation of data
related to Marine Fish Household Census 2010 consisting of 10 lakh households with 16
attributes. Website of the project is being maintained and updated regularly. Website has been
registered under google analytics on November 15, 2010. Till March 31, 2014 there were 43,239
page views across 529 cities of 82 countries. Average time on page is 3.03 minutes. The total hits
to the portal are: 1,97,816 since April 01, 2011, which amounts to more than 100 hits per day.
Based on the user logged information, the total number of logged in users from Indian NARS
since March 2012 are 24,926. It has been included in the FOCARS training programmes
conducted by NAARM, Hyderabad and 799 scientists have been sensitized about the capabilities
of the software and data analysis using SAS. Also 211 scientists were sensitized through
Refresher Courses conducted at NAARM, Hyderabad about data analysis using SAS.
The high end Statistical Computing Package procured with perpetual license can be used till
hardware and operating systems would support the versions available as on June 29, 2013.
Hence, the statistical computing environment created would be useful in all future research
endeavours. The capacity building efforts made are expected to have a multiplier effect. The
activities of Strengthening Statistical Computing for NARS are proposed in EFC for XII Plan of
IASRI and would be continued in project mode from April 01, 2014. This would facilitate
implementation of updating license files, resolving maintenance issues and maintenance of
service oriented computing modules. To further harness the benefits of the healthy statistical
computing environment created and meeting the future requirements, the activities of enhancing
Statistical Computing for NARES may be continued in the form of a network project with
provision of appropriate funds. It would also help in strengthening of Indian NARS Statistical
computing portal by adding more modules of statistical analysis of data to culminate into a
service oriented computing resource for standardization of statistical analysis of data. Capacity
building efforts in statistical concepts and statistical computing need to be pursued on continuing
basis including international exposure for synthesizing various statistical packages into the
basket. More statistical packages including freeware may be brought under this umbrella. In
future, for procurement of statistical packages as per requirement and updates and upgrades of
the existing ones, enough provision of funding need to be made.Not AvailableNAIP Component
Chapter 38 In: Management of Plant Genetic Resources (Eds. Jacob Sherry R, N Singh, K Srinivasan, V Gupta, J Radhamani, A Kak, C Pandey, S Pandey, J Aravind, IS Bisht and RK Tyagi)
No full textNot AvailableIn genetic resources environment, which is a field in the forefront of biological research, an essential activity is to test or evaluate the new germplasm/provenances/superior selections (new treatments, henceforth called test treatments) with the existing provenances or released varieties (checks, henceforth called control treatments). A problem in these evaluation studies is that the quantity of the genetic material collected from the exploration trips is very limited or cannot be made available since a part of this is to be deposited in genebank. The available quantity of seed is often not sufficient for replicated trials. Moreover, the number of new germplasm or provenances to be tested is very high (usually about 1000-2000 and sometimes
upto 3000 accessions). A problem of interest is to design such experiments for making comparisons among the test treatments, among the control treatments and test treatments vs control treatments. The basic interest in these trials is to identify the promising germplasms. The promising germplasms identified are then subjected to more rigorous experimentation by allowing replications (generally two to three) of the test treatments (the promising germplasms or entries) along with the controls. In order to test the adaptability of these germplasms or entries in different environmental conditions, the trials are generally conducted over different environments (locations or years) to identify the promising lines. The purpose of the present
note is to make an attempt to highlight some considerations in designing of experiments and analysis of experimental data. No claim is made about it being exhaustive.Not Availabl
Annual Report 2007 - 2008
No full textNot AvailableExperiments in which data on several responses are measured from an experimental unit corresponding to the application of a treatment are known as multi-response experiments. Multi-response experiments are of two types viz. complete multi-response experiments (all the response variables are recorded from each experimental unit) and incomplete multi-response experiments (recording of all the responses variables from each experimental unit is not feasible). For complete multi-response experiments, it has been shown that the designs that are efficient for single response experiments are also efficient for complete multi-response experiments provided that the response variables are less than the error degrees of freedom. Obtained a method of construction of designs for incomplete multi-response experiments using combination of randomized complete block (RCB) designs as treatment-wise design and balanced incomplete block (BIB) designs as response-wise design. The designs obtainable from this method are economical from resource point of view. Developed a step wise procedure of analysis of incomplete multi-response designs obtained as a combination of RCB design and BIB design. Developed beta version of software for generation of nested block designs both for independent errors and correlated errors. Developed an algorithm for generation of efficient supersaturated designs for two-level factorial experiments and obtained several efficient super saturated designs. Developed a software for detection and handling of outlier(s) in the experimental data. Efficient designs for asymmetric parallel line assays and slope ratio assays have been obtained by deleting all observations corresponding to a dose (of standard or test preparation) from efficient block designs for symmetric parallel line assays and slope ratio assays. To disseminate the knowledge available on combinatorial aspects of designs and analytical procedures acquired to scientists engaged in research in National Agricultural Research System, advisory services were pursued rigorously. Design Resources Server has been strengthened in collaboration with National Professor by adding links on -designs, designs for bioassays, supersaturated designs, modules for generation of randomized layout of square lattice designs, completely randomized designs, RCB designs, Latin square designs and augmented designs. To provide steps of analysis of data generated through designed experiments using SAS and SPSS, a new page “Analysis of Data” has been launched on Design Resources Server. Discussion Board has been initiated for sharing research with fellow scientists over the globe or for flagging issues for attention of scientific community. A list of experts in design of experiments over the globe is uploaded which will be useful for establishing linkages.Not Availabl
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No full textNot AvailableSome new methods of construction of 2- and 3-associate class nested partially balanced incomplete block (NPBIB) designs have been given. Catalogues of NPBIB designs with number of treatments (v) <= 30 and number of replications (r) <= 15 have also been given. Some results on non-existence of NPBIB designs are also given.Not Availabl
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