Linear Model In Order Research Articles

Why Does the Spatial Agglomeration of Firms Benefit Workers? Examining the Role of Organizational Diversity in U.S. Industries and Labor Markets

Several recent studies across the social sciences show that the spatial agglomeration of employment in a local labor market benefits both firms and workers in terms of better firm performance and higher wages. Drawing from the organizational ecology perspective, we argue that workers receive higher wages in large industrial clusters and urban labor markets because of the greater degree of organizational diversity relative to smaller local industries and labor markets. Using data from the 1990 PUMS-L, we employ a three-level hierarchical linear model in order to test hypotheses regarding organizational diversity and the effects of industrial and urban agglomeration on wages. We find that workers benefit from both forms of agglomeration due to greater establishment size and industrial diversity in large industrial clusters and urban labor markets. We also find that urban agglomeration has a larger substantive effect on wages than industrial agglomeration in reduced models, but the industrial agglomeration effect is more robust across models. These findings support the organizational ecology perspective, which suggests that workers earn higher wages in local industry clusters and urban labor markets because greater organizational diversity leads to more optimal matching between workers and employers. However, the wage premium associated with urban labor markets is also due to other factors, such as greater levels of human capital and higher rates of union density.

A Study on Kohenen Network based on Path Determination for Efficient Moving Trajectory on Mobile Robot

We propose an approach to estimate the real-time moving trajectory of an object in this paper. The object's position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Extended Kalman Filter(EKF) and neural networks are utilized cooperatively. Since the EKF needs to approximate a nonlinear system into a linear model in order to estimate the states, there still exist errors as well as uncertainties. To resolve this problem, in this approach the Kohonen networks, which have a high adaptability to the memory of the inputoutput relationship, are utilized for the nonlinear region. In addition to this, the Kohonen network, as a sort of neural network, can effectively adapt to the dynamic variations and become robust against noises. This approach is derived from the observation that the Kohonen network is a type of self-organized map and is spatially oriented, which makes it suitable for determining the trajectories of moving objects. The superiority of the proposed algorithm compared with the EKF is demonstrated through real experiments.

Factor Structures of the Neurocognitive Assessments and Familial Analysis in First-Episode Schizophrenia Patients, Their Relatives and Controls

Schizophrenia patients have a wide range of cognitive deficits. To explore the structure of these deficits and index their psychometric properties in order to define the major separable factors, exploratory factor analysis (EFA) was performed on a series of neuropsychological test results in a sample of Han Chinese first-episode schizophrenia patients, their relatives and controls without mental illness. The factors derived from the composite sample were tested with confirmatory factor analysis (CFA) in each of the subsamples. The heritability of each factor was estimated by using the mixed linear model in order to determine whether the common factor scores describe the familiarity of the data. A six-factor model of general mental activity, sort and shift, attention and anti-interference, logical memory, reasoning and problem-solving/executive function, and visual reproduction were extracted and confirmed on EFA and CFA. There was a hierarchy in cognitive performance deficits in relatives depending on their relatedness to probands. Patients performed more poorly than siblings/offspring in general mental activity, sort and shift, attention and anti-interference, logical memory, planning and problem-solving, but not in visual reproduction. The logical memory domain was found to be significantly heritable (h(2)=0.34), and reasoning and problem-solving had a marginal heritability of around 0.19. Generalized cognitive deficit accounts for the majority of the overall variance of cognitive deficits in schizophrenia. Estimates of heritability were modest. The present results also support the hypothesis that neurocognitive deficits of schizophrenia are familial and could serve as endophenotypes, especially logical memory, which is the most heritable.

Trajectory estimation of a moving object using Kalman filter and Kohonen networks

SUMMARYA novel approach to estimate the real-time moving trajectory of an object is proposed in this paper. The object's position is obtained from the image data of a charge coupled device (CCD) camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Kalman filter and neural networks are utilized cooperatively. Since the Kalman filter needs to approximate a nonlinear system into a linear model in order to estimate the states, there still exist errors as well as uncertainties. To resolve this problem, in this approach, the Kohonen networks, which have a high adaptability to the memory of the input–output relationship, are utilized for the nonlinear region. In addition to this, the Kohonen network, as a sort of neural network, can effectively adapt to the dynamic variations and become robust against noises. This approach is derived from the observation that the Kohonen network is a type of a self-organized map and is spatially oriented, which makes it suitable for determining the trajectories of moving objects. The superiority of the proposed algorithm compared with the extended Kalman filter is demonstrated through real experiments.

Factors affecting milk yield and ewe's lactation curves estimated with test-day models

Test-day records of purebred Tsigai, Improved Valachian and Lacaune ewes were analysed with a general linear model in order to investigate the effects of flock-test day, lactation number, days in milk, litter size and month of lambing. In total, 121 576 (Tsigai), 247 902 (Improved Valachian) and 2 196 (Lacaune) test-day records gathered over the period 1995–2005 were included in the analyses. Average daily milk yields were 0.604 ± 0.279 kg (Tsigai), 0.595 ± 0.243 kg (Improved Valachian) and 1.053 ± 0.475 kg (Lacaune). The significant (P < 0.05) or highly significant (P < 0.01) effects of flock-test day, lactation number (except for Lacaune), days in milk, litter size (except for Lacaune) and month of lambing (either fixed effects or covariates) tested by Fisher’s tests were shown. The model explained about 50% of daily milk yield variability, with coefficients of determination as follows: 0.479 for Improved Valachian; 0.487 for Tsigai; 0.537 for Lacaune. Differences in estimated least-squares means were tested using multiple-range Scheffe’s tests. A lower daily milk yield was found for the first lactation, single litter and lactations starting in March in comparison with daily milk yield for the second and third lactations (except for Lacaune), multiple litter and lactations starting in January and February (except for Improved Valachian). Ali-Schaeffer regression adopted for sheep was used for the fitting of lactation curve according to breed.  

Effects of Age and Calving Season on Lactation Curves of Milk Production Traits in Italian Water Buffaloes

Test day (TD) records of milk production traits (milk yield, fat, and protein percentages) of 534 Italian buffalo cows were analyzed with a mixed linear model in order to estimate lactation curves pertaining to different ages at calving and different seasons of calving. Milk yield lactation curves of younger animals were lower than those of older animals until 20 wk from parturition. No effect of age at calving could be observed for fat and protein percentages. Season of calving affected milk yield only in the first phase of lactation, with the lowest production levels for summer calvings; no effect could be observed on fat and protein contents. Average correlations among TD measures within lactation were 0.59, 0.31, and 0.36 for milk yield, fat, and protein percentages, respectively. Five standard linear functions of time were able to reconstruct the average lactation curves. Goodness of fit was satisfactory for all models considered, although only the five-parameter model was flexible enough to fit all the three traits considered with excellent results.

Initial Selection and Cause of Disability for Individual Permanent Health Insurance

AbstractWe investigate the influence of initial selection (the impact of underwriting during the early years of a policy's life) on individual Permanent Health Insurance claim inceptions. In Gutiérrez-Delgado (1999) a decreasing trend was found. In this paper we include the effect of cause of disability and fit a generalized linear model in order to gain a greater understanding of the phenomenon. Both effects, policy duration and cause of disability, are found to have a significant effect on the number of claims. We describe their influence using factors that collect the information available through the fitted model. Results from both factors suggest that the grouping of diseases selected for the research helps to explain partially our earlier results. In addition there is some evidence of moral hazard in mental disorders and musculoskeletal diseases which also contributes to the understanding of the negative trend found.

Simple Forms of the Best Linear Unbiased Predictor in the General Linear Regression Model

This article presents necessary and sufficient conditions to be satisfied by the best linear unbiased predictor of future observations in the general linear model in order to have a simple form. Under these conditions, the predictors have an expression similar to that in the uncorrelated case and some parameters related to the covariances between some observations need not to be known.

A Method for Modelling Varying Run-Off Evolutions in Claims Reserving

AbstractThis paper considers the application of state space modelling to the chain ladder linear model in order to allow the run-off parameters to vary with accident year. In the usual application of the chain ladder technique, the development factors are assumed to be the same for each accident year. This implies that the run-off shape does not alter with accident year. This paper shows how this assumption can be relaxed in order to allow a recursive smooth model to be applied, or for large changes in the shape of the run-off curve. It is possible for these changes to be modelled using external inputs, or for a multiprocess model to be used to detect changes in the run-off shape.

Estimate of the size of a hunted population

We present an iterative method to estimate the size of a hunted population by using the hunting statistics. This method differs from the Seber's catch per unit effort model (based on a sampling Poisson process with regard to effort). It uses the logistic linear model in order to estimate the capture probability for each hunting. For each and every hunting, the probabilities of capture being known, we give the maximum likelihood estimates of the population size, as well as its confidence interval. This method has been applied to the results of wild boar ( Sus scrofa) hunting.

The Multiprocess Dynamic Linear Model with Biased Perturbations: A Real Time Model for Growth Hormone Level

This paper introduces biased perturbation distributions into the multiprocess dynamic linear model in order to represent growth hormone levels in animals, which are characteristically noisy pulsatile time series. The use of biased perturbation distributions allows a pulse to be detected on the first observation immediately after it occurs, thus allowing the process to be modelled in real time. This approach is found to be quite effective at detecting the occurrence of a pulse in growth hormone level, and revising the parameter estimates and predictions accordingly.

The multiprocess dynamic linear model with biased perturbations: A real time model for growth hormone level

This paper introduces biased perturbation distributions into the multiprocess dynamic linear model in order to represent growth hormone levels in animals, which are characteristically noisy pulsatile time series. The use of biased perturbation distributions allows a pulse to be detected on the first observation immediately after it occurs, thus allowing the process to be modelled in real time. This approach is found to be quite effective at detecting the occurrence of a pulse in growth hormone level, and revising the parameter estimates and predictions accordingly.

Closed Loop Dynamics of Nuclear Rocket Engines with Bleed Turbine Drive

The dynamics of nuclear rocket engines is studied, with attention to integral temperature error feedback control of reactivity and proportional pressure error feedback control of propellant flow with first order lags placed between the desired controller positions and the actual positions. The resulting series of ordinary, nonlinear differential equations are approximated by a linear model in order to analyze the low-frequency dynamics. It is shown that the low and high frequencies may be decoupled and that the proposed method of control is stable for small variations away from any point of steady-state operation. Algebraic equations, in terms of design parameters, are derived for control settings that yield optimum response characteristics. It is further shown that the asymptotic response is improved by reduction of the mechanical inertia of the turbopump but is independent of the thermal inertia of the core. The analysis is corroborated by analog simulation of the nonlinear model for the case of lowpower- -high-power transition, using only feedback control for flow and reactivity variation. (auth)