Using Observed Residual Error Structure Yields the Best Estimates of Individual Growth Parameters

Obtaining the best possible estimates of individual growth parameters is essential in studies of physiology, fisheries management, and conservation of natural resources since growth is a key component of population dynamics. In the present work, we use data of an endangered fish species to demonstrate the importance of selecting the right data error structure when fitting growth models in multimodel inference. The totoaba (Totoaba macdonaldi) is a fish species endemic to the Gulf of California increasingly studied in recent times due to a perceived threat of extinction. Previous works estimated individual growth using the von Bertalanffy model assuming a constant variance of length-at-age. Here, we reanalyze the same data under five different variance assumptions to fit the von Bertalanffy and Gompertz models. We found consistent significant differences between the constant and nonconstant error structure scenarios and provide an example of the consequences using the growth performance index ϕ′ to show how using the wrong error structure can produce growth parameter values that can lead to biased conclusions. Based on these results, for totoaba and other related species, we recommend using the observed error structure to obtain the individual growth parameters.

Spatial regression and geostatistics discourse with empirical application to precipitation data in Nigeria

In this study, we propose a robust approach to handling geo-referenced data and discuss its statistical analysis. The linear regression model has been found inappropriate in this type of study. This motivates us to redefine its error structure to incorporate the spatial components inherent in the data into the model. Therefore, four spatial models emanated from the re-definition of the error structure. We fitted the spatial and the non-spatial linear model to the precipitation data and compared their results. All the spatial models outperformed the non-spatial model. The Spatial Autoregressive with additional autoregressive error structure (SARAR) model is the most adequate among the spatial models. Furthermore, we identified the hot and cold spot locations of precipitation and their spatial distribution in the study area.

Measure of Slope Rotatability for Second Order Response Surface Designs Under Tri-Diagonal Correlation Error Structure Using Symmetrical Unequal Block Arrangements with Two Unequal Block Sizes

In this paper, measure of slope rotatability for second order response surface designs using symmetrical unequal block arrangements with two unequal block sizes under tri-diagonal correlation error structure is suggested and illustrated with examples.

On Second Order Slope Rotatable Designs under Intra-class Correlated Structure of Errors Using a Pair of Dissimilar Incomplete Block Designs

In this paper, a study of second order slope rotatable designs under intra-class correlation error structure using two suitably chosen dissimilar incomplete block designs like balanced incomplete block designs and symmetrical unequal block arrangements with two unequal block sizes are suggested. Further, we study the variance of the estimated slopes for different values of the intra-class correlation coefficient (ρ) and the distance from the centre (d) for v factors are suggested. Some illustrative examples are also suggested.