Information Resources on Statistics
http://dr.lib.sjp.ac.lk/handle/123456789/1900
2024-03-28T23:44:26ZBayesiantreatmentofnon-standardproblemsintestanalysis
http://dr.lib.sjp.ac.lk/handle/123456789/8505
Bayesiantreatmentofnon-standardproblemsintestanalysis
Silva, Rajitha M.; Guan, Yuping; Swartz, Tlm B.
This paper extends the methods of [10] in an attempt to handle non-standard problems in test analysis. The approach is based on a Bayesian framework where test characteristics are treated as random parameters for which posterior probability assessments are available. The generality of the approach permits straightforward analyses of problems that may be difficult using standard classical test theory and standard item response theory. We first illustrate the methods on aviation test scores where the test outcomes are not dichotomous (i.e. correct and incorrect responses). Instead, the approach is modified to handle questions with answers on a five-point ordinal scale. The second problem addresses the complication of the assessment of instructors in addition to the assessment of test questions and students.
2019-01-01T00:00:00ZDevelopment of a rapid, sensitive and specific DNA-based method to detect Ralstonia solanacearum in potato for quarantine purposes
http://dr.lib.sjp.ac.lk/handle/123456789/8404
Development of a rapid, sensitive and specific DNA-based method to detect Ralstonia solanacearum in potato for quarantine purposes
Perera, A.A.U.; Weerasena, O.V.D.S.J.; Dasanayaka, P.N.; Wickramarachchi, D.C.
2018-06-30T00:00:00ZPerformance Optimized Expectation Conditional Maximization Algorithms for Nonhomogeneous Poisson Process Software Reliability Models
http://dr.lib.sjp.ac.lk/handle/123456789/6980
Performance Optimized Expectation Conditional Maximization Algorithms for Nonhomogeneous Poisson Process Software Reliability Models
Jayasinghe, C.L.
Attached; nhomogeneous Poisson process (NHPP) and software reliability growth models (SRGM) are a popular approach
to estimate useful metrics such as the number of faults remaining,
failure rate, and reliability, which is defined as the probability of
failure free operation in a specified environment for a specified
period of time. We propose performance-optimized expectation
conditional maximization (ECM) algorithms for NHl)P SRGM.
In contrast to the expectation maximization (EM) algorithm, the
ECM algorithm reduces the maximum-likelihood estimation process to multiple simpler conditional maximization (CM)-steps. The
advantage of these CM-steps is that they only need to consider one
variable at a time, enabling implicit solutions to update rules when
a closed form equation is not available for a model parameter. We
compare the performance of our ECM algorithms to previous EM
and ECM algorithms on many datasets from the research literature. Our results indicate that our ECM algorithms achieve two
orders of magnitude speed up over the EM and ECM algorithms
of [11 when their experimental methodology is considered and three
orders of magnitude when knowledge of the maximum-likelihood
estimation is removed, whereas our approach is as much as 60 times
faster than the EM algorithms of [2]. We subsequently propose a
two-stage algorithm to further accelerate performance.
2017-09-01T00:00:00ZMaximum-Likelihood Estimation of Parameters of NHPP Software Reliability Models Using Expectation Conditional Maximization Algorithm
http://dr.lib.sjp.ac.lk/handle/123456789/6979
Maximum-Likelihood Estimation of Parameters of NHPP Software Reliability Models Using Expectation Conditional Maximization Algorithm
Jayasinghe, C.L.
Attached; ince its introduction in 1977, the expectation maximization (EM) algorithm has been one of the most important and
widely used estimation method in estimating parameters of distributions in the presence of incomplete information. In this paper,
a variant of the EM algorithm, the expectation conditional maximization (ECM) algorithm, is introduced for the first time and
it provides a promising alternative in estimating the parameters
of nonhomogeneous poisson (NHPP) software reliability growth
models (SRGM). This algorithm circumvents the difficult M-step
of the EM algorithm by replacing it by a series of conditional maximization steps. The utility of the ECM approach is demonstrated in
the estimation of parameters of several well-known models for both
time domain and time interval software failure data. Numerical examples with real-data indicate that the ECM algorithm performs
well in estimating parameters ofNHPP SRGM with complex mean
value functions and can produce a faster rate of convergence.
2016-09-01T00:00:00Z