A Stochastic Model for Insect Life History Data

Munholland, Patricia L.; Kalbfleisch, John D.; Dennis, B. R.

doi:10.1007/978-1-4612-3664-1_8

Cited by 5 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For our third model, the distribution of the time of entry to stage 1 and the durations of stages are assumed to have inverse normal distributions as suggested by KEMP et al (1989) andMUNHOLLAND et al (1989) on the basis of a diffusion model for development. In this case, the time of entry to stages j has an inverse normally distributed with density function gj(x) = 4{q//(2~rx3)} exp(--(1/2)uj), and cumulative distribution function…”

Section: The Three Modelsmentioning

confidence: 99%

“…There is therfore something of a problem for the data analyst to decide what is best for .a particular set of data, This problem is compounded because very few comparisons of the different methods have been made. In fact the only attempts to analyse data by several methods seem to be the simulation study of MAt~LY (1974), the analysis of one example by four methods by MUNHOLLAND et al (1989), and a few cases discussed by MANLY (1989a, b).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A comparison of three maximum likelihood models for stage‐frequency data

Manly

Seyb

1989

Population Ecology

View full text Add to dashboard Cite

Summary A comparison has been made between the estimates obtained from maximum likelihood estimation of gamma, inverse normal, and normal distribution models for stage‐frequency data. Results have been compared for six of sets of test data, and from many sets of simulated data. It is concluded that (1) some estimates may differ substantially between the models, (2) estimates from the correct model have little bias, and estimated standard errors are generally close to theoretical values, (3) there are problems in determining degrees of freedom for chi‐squared goodness of fit tests, so that it is best to compare test statistics with simulated distributions, and (4) goodness of fit tests may not discriminate well between the three models.

show abstract

Section: The Three Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comparison of three maximum likelihood models for stage‐frequency data

Manly

Seyb

1989

Population Ecology

View full text Add to dashboard Cite

show abstract

“…Estimation of stage duration parameters has been described by Munholland and Kalbfleisch (1991) and Wood (1993). Their methods differ from ours by being based on unmarked animals and estimating mortality together with stage duration parameters, whereas we estimate mortality together with the production estimation, in which stage duration parame-321 ters are already assumed to have been estimated.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Offspring Production from a Limited Number of Stage‐Structured Censuses

Bakke

Lorentsen

1999

Biometrics

View full text Add to dashboard Cite

We propose a procedure for maximum likelihood estimation of the number of animals or offspring in a closed population where the individuals counted go through stages or age-groups. Application of the procedure requires knowledge of the distributions of the stage durations. A procedure for maximum likelihood estimation of those based on marked animals is also given. The procedures are illustrated by applying them to gray seal (Halichoerus grypus) data from Froan Nature Reserve, Central Norway, from the breeding seasons 1990-1999.

show abstract

“…The problem of estimating parameters for stage-structured populations from sample counts of the numbers in different development stages at several points in time has attracted interest for about the last 40 years, and many solutions have been proposed (Manly, 1989(Manly, , 1990Wood and Nisbet, 1991) ranging from those based on equating totals calculated from the available data with their expected values (e.g., Kiritani and Nakasuji, 1967) to those based on the maximum likelihood estimation of the parameters of very specific statistical models (e.g., Kempton, 1979 andMunholland et al, 1989).…”

Section: Introductionmentioning

confidence: 99%

Note on a method for analyzing stage‐frequency data

Manly

1993

Population Ecology

View full text Add to dashboard Cite

Summary A new method for analyzing stage‐frequency data is proposed which is based on the estimation of rates of transition between one stage and the next highest stage in one unit of time, and a unit time survival rate that is assumed to be constant. Once these estimates are calculated it becomes possible to also estimate the mean durations of stages, stage‐specific survival rates, and numbers entering stages. An advantage of the method is that it can be applied with any distribution of entry times to stage 1, and any distribution of numbers in stages when sampling begins. Use of the method is illustrated on data from a copepod population in a Canadian lake.

show abstract

A Stochastic Model for Insect Life History Data

Cited by 5 publications

References 8 publications

A comparison of three maximum likelihood models for stage‐frequency data

A comparison of three maximum likelihood models for stage‐frequency data

Estimation of Offspring Production from a Limited Number of Stage‐Structured Censuses

Note on a method for analyzing stage‐frequency data

Contact Info

Product

Resources

About