New thilinear hybrid of hard yellow corn for the Peruvian tropic

El objetivo de este estudio es evaluar y comparar el comportamiento agronómico de cinco híbridos trilineales experimentales de maíz amarillo duro y la variedad Marginal 28T, en ocho localidades del trópico peruano. El experimento se realizó en dos fases: de marzo del 2018 a marzo del 2019 en cuatro parcelas de validación en San Martín y de marzo a diciembre del 2019 en cuatro parcelas de adaptabilidad en San Martín, Pucallpa, Loreto y Amazonas. Las variables evaluadas fueron: altura de planta y mazorca, dimensiones y peso de mazorca, acame de raíz, resistencia a roya y rendimiento (t ha-1). Se aplicó el diseño de bloques completos al azar con análisis combinado y la interacción genotipo x ambiente del rendimiento con el modelo efectos aditivos principales y modelos de interacción multiplicativa. Resultados. El híbrido HTE6 fue superior en diámetro de mazorca (4,66 cm), peso de mazorca (190,76 g), número de hileras por mazorca (14,26), granos por hilera (37,45), peso total de grano (156,21 g) y rendimiento de grano (7,21 t ha-1). HTE6 mostró adaptabilidad superior en Iquitos (9,20 t ha-1) y San Martín (8,10 t ha-1). En la interacción genotipo-ambiente alcanzó 7,18 t ha-1 y fue el más estable en las ocho localidades. Conclusión. De los cinco híbridos evaluados y la variedad Marginal 28T, el HTE6 tuvo el mejor desempeño agronómico y el mayor rendimiento en las ocho localidades evaluadas. Por lo que se consideró el híbrido trilineal para su liberación comercial en regiones del trópico peruan

Analysis of panel data models with grouped observations

We present an iterative estimation procedure to estimate panel data models when some observations are missed or grouped with arbitrary classification intervals. The analysis is carried out from the perspective of panel data models, in which the error terms may follow an arbitrary distribution. We propose an easy-to-implement algorithm to estimate all of the model parameters and the asymptotic stochastic properties of the resulting estimate are investigated as the number of individuals and the number of time periods increase

Analysis of panel data models with grouped observations

We present an iterative estimation procedure to estimate panel data models when some observations are missed or grouped with arbitrary classification intervals. The analysis is carried out from the perspective of panel data models, in which the error terms may follow an arbitrary distribution. We propose an easy-to-implement algorithm to estimate all of the model parameters and the asymptotic stochastic properties of the resulting estimate are investigated as the number of individuals and the number of time periods increase

Dynamic mixed models for familial longitudinal data [book review]

SUTRADHAR, Brajendra C., "Dynamic mixed models for familial longitudinal data". Springer, 2011. ISBN 9781441983411Depto. de Estadística e Investigación OperativaFac. de Ciencias Matemáticaspu

Robust analysis of variance with imprecise data: an ad hoc algorithm

We present an easy to implement algorithm, which is valid to analyse the variance of data under several robust conditions. Firstly, the observations may be precise or imprecise. Secondly, the error distributions may vary within the wide class of the strongly unimodal distributions, symmetrical or not. Thirdly, the variance of the errors is unknown. The algorithm starts by estimating the parameters of the ANOVA linear model. Then, the asymptotic covariance matrix of the effects is estimated. Finally, the algorithm uses this matrix estimate to test ANOVA hypotheses posed in terms of linear combinations of the effects

Regularization in statistics - Discussion

Depto. de Estadística e Investigación OperativaFac. de Ciencias MatemáticasTRUEpu

A robust algorithm for the sequential linear analysis of environmental radiological data with imprecise observations

In this paper we present an algorithm suitable to analyse linear models under the following robust conditions: the data is not received in batch but sequentially; the dependent variables may be either non-grouped or grouped, that is, imprecisely observed; the distribution of the errors may be general, thus, not necessarily normal; and the variance of the errors is unknown. As a consequence of the sequential data reception, the algorithm focuses on updating the current estimation and inference of the model parameters (slopes and error variance) as soon as a new data is received. The update of the current estimate is simple and needs scanty computational requirements. The same occurs with the inference processes which are based on asymptotics. The algorithm, unlike its natural competitors, has some memory; therefore, the storage of the complete up-to-date data set is not needed. This fact is essential in terms of computer complexity, so reducing both the computing time and storage requirements of our algorithm compared with other alternatives

Mean-based iterative procedures in linear models with general errors and grouped data

We present in this paper iterative estimation procedures, using conditional expectations, to fit linear models when the distributions of the errors are general and the dependent data stem from a finite number of sources, either grouped or non-grouped with different classification criteria. We propose an initial procedure that is inspired by the expectation-maximization (EM) algorithm, although it does not agree with it. The proposed procedure avoids the nested iteration, which implicitly appears in the initial procedure and also in the EM algorithm. The stochastic asymptotic properties of the corresponding estimators are analysed