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Sekar, P.
- Diagnostic Checking of Time Series Models
Abstract Views :361 |
PDF Views:106
Authors
P. Sekar
1
Affiliations
1 Department of Mathematics, Pachaiyappa’s College for Men, Kancheepuram–631 501, TN, IN
1 Department of Mathematics, Pachaiyappa’s College for Men, Kancheepuram–631 501, TN, IN
Source
Indian Journal of Science and Technology, Vol 3, No 9 (2010), Pagination: 1026-1031Abstract
Diagnostic checks have become a standard tool for identification of models before forecasting the data. The overall test for lack of fit for autoregressive moving average models proposed by Box and Pierce (1970) and a measure of lack of fit in time series models proposed by Ljung and Box (1978) are considered. In this paper, a modification is made and it is shown that a substantially improved approximation results from a simple improvement of this test. Cumulative periodogram check is also given.Keywords
Time Series, ARMA, ARIMA, ForecastingFull Text
References
- Abraham B and Vijayan K (1988) A statistic to check model adequacy in time series. Comm. Statist. Theory Methods. 17(12), 4271-4278.
- Anders Milhqj (1981) A test of fit in time series models. Biometrika, 68, 1, 177-87.
- Anderson RL (1941) Distribution of the time serial correlation coefficients. Ann. Math. Stat. 8(1), 1-13.
- Bartlett MS (1946) On the theoretical specification of sampling properties of autocorrelated time series. J. Royal Stat. Soc. Ser B. 8, 27-41.
- Box GEP and Jenkins GM (1976) Time series analysis: forecasting and control. Revised edition, San Francisco: Holden-day.
- Box GEP and Pierce DA (1970) Distribution of residual autocorrelation in autoregressive integrated moving average models. J. Amer. Stat. Assoc. 64, 1509-1526.
- Box GEP, Jenkins GM and Reinsel GC (1994) Time series analysis: forecasting and control, 3rd edition, Prentice Hall, New Jersey.
- Brockwell BJ and Davis RA (1991) Time series: theory and methods, 2nd edition, Springer-Verlag, New York.
- Davies N, Triggs CM and Newbold P (1977) Significance levels of the Box-Pierce Portmanteau statistic in finite samples. Biometrika. 64(3), 517-522.
- Godfrey LG (1979) Testing the adequacy of a time series model. Biometrika. 66, 67-72.
- Hokstad P (1983) A method for diagnostic checking of time series models. J. Time Ser. Anal. 4(3), 177-183.
- Jenkins GM and Watts DG (1968) On the theoretical specification of sampling properties of autocorrelated time series. J. Royal Stat. Soc. B8, 27.
- Jenkins GM and Watts DG (1969) Spectral analysis and its applications, Holden-day series in time series analysis, San Francisco, California.
- Ljung GM (1986) Diagnostic testing of univariate time series models. Biometrika. 73(3), 725-730.
- Ljung GM and Box GEP (1978) On a measure of lack of fit in time series models. Biometrika. 65, 297-303.
- McLeod AI (1994) Diagnostic checking of periodic auto regression models with application. J. Time Ser. Anal. 15(2), 221-233.
- McLeod AI and Li WK (1983) Diagnostic checking ARMA time series models using squared-residual autocorrelation. J. Time Ser. Anal. 4(4), 269-273.
- Smith JQ (1985) Diagnostic checks of non-standard time series models. J. Forecasting. 4, 283-291.
- Application of Time Series Models
Abstract Views :359 |
PDF Views:95
Authors
P. Sekar
1
Affiliations
1 Department of Mathematics, Pachaiyappa’s College for Men, Kancheepuram–631 501, TN, IN
1 Department of Mathematics, Pachaiyappa’s College for Men, Kancheepuram–631 501, TN, IN
Source
Indian Journal of Science and Technology, Vol 3, No 9 (2010), Pagination: 1032-1037Abstract
Forecasting is an ultimate aim in the study of time series analysis. Anyone who is engaged in planning, controlling and managing projects, personnel, finance and operations will be interested in knowing what will happen in future with the analysis of the available dataKeywords
Time Series, ARMA, ARIMA, ARARMA, Fractional DifferencingFull Text
References
- Box GEP and Jenkins GM (1976) Time series analysis: forecasting and control, revised edn, San Francisco: Holden-day.
- Hosking JRM (1981) Fractional differencing. Biometrika, 68(1), 165-176.
- Montgomary DC and Johnson LA (1976) Forecasting and time series analysis. McGraw Hill Inc., San Francisco. pp: 231-232.
- Parzen E (1982) ARARMA models for time series analysis and forecasting. J. Forecasting. 1, 67-82.
- Sekar P and Sreenivasan M (1996) Simulation and modeling of time series using fractional differencing. Proc. of Int. Conf. on Stochastic Process. Dec. 26-29, Cochin, India, pp:225-233.
- Impact of Gestational and Lactational Exposure to Hexavalent Chromium on Steroidogenic Compartment of Post-Natal Rat Testis
Abstract Views :217 |
PDF Views:0
Authors
Affiliations
1 Department of Endocrinology, Dr. ALM Post-Graduate Institute of Basic Medical Sciences, University of Madras, Taramani Campus, Chennai - 600 113, IN
2 Department of Animal Science, Bharathidasan University, Tiruchirappalli - 620 024, IN
3 Department of Endocrinology, Dr. ALM Post-Graduate Institute of Basic Medical Sciences, University of Madras Taramani Campus, Chennai - 600 113, IN
1 Department of Endocrinology, Dr. ALM Post-Graduate Institute of Basic Medical Sciences, University of Madras, Taramani Campus, Chennai - 600 113, IN
2 Department of Animal Science, Bharathidasan University, Tiruchirappalli - 620 024, IN
3 Department of Endocrinology, Dr. ALM Post-Graduate Institute of Basic Medical Sciences, University of Madras Taramani Campus, Chennai - 600 113, IN
Source
Journal of Endocrinology and Reproduction, Vol 15, No 1&2 (2011), Pagination: 15-26Abstract
Reproductive and embryonic toxicity of hexavalent chromium (CrVI) is known, and adult testis is one of its vulnerable targets. However, it is not known if gestational and lactational exposure to excess Cr affects development and functions of Leydig cells during postnatal life. It is hypothesized that gestational/lactational exposure to CrVI may affect Leydig cell development and differentiation and its functions during postnatal life extending into adulthood. Pregnant [gestational days 9 to 21] and lactating [postnatal days (PND) 1 to 21] rats were exposed to 50ppm and 100ppm CrVI (K2Cr2O7) through drinking water, and testis was collected on PND 30, 60, 90 and 120, and subjected to light and transmission electron microscopic analysis. Serum testosterone and estradiol were determined adopting RIA. Histological evaluation of testes revealed hypertrophy and vacuolation of Leydig cells of CrVI-exposed rats; transmission electron micrographs (TEM) showed lipid accumulation, swollen mitochondria and disorganized smooth endoplasmic reticulum. Lactational exposure to CrVI led to decrease in the number of mitochondria and collapse of mitochondrial cristae. In general, the changes were obvious in PND 30 rats, and became less pronounced by PND 60 to become normal by PND 90. Serum testosterone and estradiol levels showed a general trend of opposite response to CrVI exposure. Gestational exposure to CrVI caused increase in testosterone level in prepuberal rats, but the trend was reversed by PND 60, and by PND 120 its level was more than in coeval controls. A similar trend was noticed in rats which had lactational exposure to CrVI but for a consistent increase in both steroids in PND 30 and PND 60 old rats which were exposed to 50ppm CrVI. By PND 90, testosterone remained elevated or normal, but by PND 120 its level was increased due to lactational exposure to CrVI. On the contrary, serum estradiol in these rats was low by PND 90 and became normal by PND 120. The findings partially support the hypothesis proposed and it is concluded that the fetal type Leydig cells are the major targets for the toxic effects of CrVI exposure during gestational and lactational periods where in lactational exposure may have a persistent effect leading to increased testosterone: estradiol ratio. Nevertheless, the effects of CrVI on testosterone and estradiol are reversible, as the adult type Leydig cells are unaffected.Keywords
CrVI, Estradiol, Leydig Cells, Sertoli Cells, Testicular Toxicity, Testosterone.
- A New Approach for Solution to a Fuzzy Assignment Problem
Abstract Views :335 |
PDF Views:2
Authors
S. Dhanasekar
1,
P. Sekar
2
Affiliations
1 Department of Mathematics, SRM University, Kattangulathur, IN
2 C. Kandaswamy Naidu College for Men, Anna Nagar, IN
1 Department of Mathematics, SRM University, Kattangulathur, IN
2 C. Kandaswamy Naidu College for Men, Anna Nagar, IN
Source
Fuzzy Systems, Vol 4, No 7 (2012), Pagination: 244-247Abstract
Assignment problem is a well known topic and is used very often in solving problems of engineering and management sciences. If the cost is not deterministic, then the problem is said to be assignment problem with fuzzy costs. We propose new method based on branch and bound technique to solve the fuzzy assignment problem branch and bound technique is used to solve the travelling salesman problem. Compared with the efficient of existing methods we find that ours is more efficient. Finally to shoe the efficiency of the proposed method we solve two numerical examples.Keywords
Fuzzy Numbers, Fuzzy Ranking, Branch and Bound Technique.- Assessment of Optimal Combination of Operating Parameters using Graph Theory Matrix Approach
Abstract Views :186 |
PDF Views:0
Authors
Affiliations
1 Department of Mathematics, Saveetha School of Engineering, Saveetha University, Chennai - 602105, Tamil Nadu, IN
2 Department of Physics, Saveetha School of Engineering, Saveetha University, Chennai - 602105, Tamil Nadu, IN
3 Department of Mathematics, C. Kandaswami Naidu College for Men, Anna Nagar, Chennai – 600102, Tamil Nadu, IN
1 Department of Mathematics, Saveetha School of Engineering, Saveetha University, Chennai - 602105, Tamil Nadu, IN
2 Department of Physics, Saveetha School of Engineering, Saveetha University, Chennai - 602105, Tamil Nadu, IN
3 Department of Mathematics, C. Kandaswami Naidu College for Men, Anna Nagar, Chennai – 600102, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 36 (2016), Pagination:Abstract
Background/Objectives: Graph theory matrix approach is a logical and systematical approach originated from combinatorial mathematics. Graph theory matrix approach is adopted to find the optimal combination of operating parameters. Methods/Statistical Analysis: Graph theory matrix approach helps to analyze and understand the system as a whole by identifying system and sub-system up to the component level. Attributes digraph is developed to represent the inheritance and the interdependencies of the subsystems. Matrix method is adopted to convert the digraph into mathematical form. Permanent function is deduced to determine the parameter index to find the optimal combination of operating parameters on a diesel engine. Findings: The combination of 18 Ampere load, 270 BTDC Injection timing and 200 bar Injection pressure forms the optimal combination of operating parameters having the highest value of Permanent index. Applications/Improvements: Graph theory matrix approach offers simple, generic, easy and convenient computation. It finds applications in the fields of education, neural networks, automotive industry, manufacturing, electronic devices, total quality management, location of plants, supply chain management, information technology, human resource selection etc.Keywords
Engine, Graph Theory, Matrix Approach, Operating Parameter, Permanent Index.Full Text
- Graph Theory Matrix Approach – A Review
Abstract Views :171 |
PDF Views:0
Authors
N. K. Geetha
1,
P. Sekar
1
Affiliations
1 Department of Mathematics, C. Kandaswami Naidu College for Men, Anna Nagar, Chennai - 600102, IN
1 Department of Mathematics, C. Kandaswami Naidu College for Men, Anna Nagar, Chennai - 600102, IN
Source
Indian Journal of Science and Technology, Vol 9, No 16 (2016), Pagination:Abstract
Background: Graph theory matrix approach as a decision making tool. Methods: Graph theory matrix approach for determining the permanent index in comparing and ranking the alternatives in decision making. Findings: The best combination of attributes that influence the system can be found out using this combinatorial approach. Application: Decision making can be done appropriately using this approach.Keywords
Digraph, GTMA, Matrix, Permanent Function, Permanent IndexFull Text