Transformation model selection by multiple hypotheses testing

Transformations between different geodetic reference frames are often performed such that first the transformation parameters are determined from control points. If in the first place we do not know which of the numerous transformation models is appropriate then we can set up a multiple hypotheses test. The paper extends the common method of testing transformation parameters for significance, to the case that also constraints for such parameters are tested. This provides more flexibility when setting up such a test. One can formulate a general model with a maximum number of transformation parameters and specialize it by adding constraints to those parameters, which need to be tested. The proper test statistic in a multiple test is shown to be either the extreme normalized or the extreme studentized Lagrange multiplier. They are shown to perform superior to the more intuitive test statistics derived from misclosures. It is shown how model selection by multiple hypotheses testing relates to the use of information criteria like AICc and Mallows’ Cp, which are based on an information theoretic approach. Nevertheless, whenever comparable, the results of an exemplary computation almost coincide

PENGARUH REKRUTMEN DAN SELEKSI TERHADAP KINERJA KARYAWAN PT POS INDONESIA JAKARTA PUSAT

Enrico Nadeak, 2017;The Impact of Recruitmen and Selection with Employees Performance of Indonesia Post Office Central Jakarta. Essay, Jakarta : Human Resources Management, Study Program Management, , Faculty ofEconomic, Jakarta State University. The purpose of this research is to gain the proper knowledge and accurate and can be trusted on the effect of partially or simultaneously on the recruitment and selection of employees performance of PT Pos Indonesia. The research model used in this study using regression analysis. This study was conducted on 113 employees from various levels and departments that exist in PT Pos Indonesia. Data collection techniques using the methods of the survey are then processed by using a subset of the statistics analysis program SPSS 22.0. The results of the partial significance test indicates that the recruitment and selection process has a positive and significant impact on employee performance. Results of simultaneous significance ujji indicate that the recruitment and selection process jointly affect the performance of employees of PT Pos Indonesia. Key Word : recruitment, selection, employees performance, PT Pos Indonesi

A simple decision market model

Economic modeling of decision markets has mainly considered the market scoring rule setup. Literature has made reference to the alternative, joint elicitation type decision market, but no in depth analysis of it appears to have been published. This paper develops a simple decision market model of the joint elicitation type, that provides a specific decision market nomenclature on which to base future analysis.A generally accepted prediction market model is modified, by introducing two additional concepts: “proper information market” and “relevant information”. Our work then provides original contributions to the theoretical discourse on information markets, including finding the sufficient and necessary condition for convergence to the best possible prediction. It is shown in our new prediction market model that “all agents express relevant information” is a sufficient and necessary condition for convergence to the direct communication equilibrium in a proper information (prediction) market.Our new prediction market model is used to formulate a simple decision market model of the joint elicitation market type. It is shown that our decision market will select the best decision if a specific selection and payout rule is defined. Importantly, our decision market model does not need to delay payment of any contracts to the observation of the desired outcome. Therefore, when dealing with long-term outcome projects, our decision market does not need to be a long running market. Future work will test for the statistical significance of relevant information (identified as important in our idealized decision market model) in laboratory and real world settings

Model selection in a multi-hypothesis test setting: applications in financial econometrics

In this thesis, we investigate model selection in a general setting and perform several exercises in financial econometrics. We present the multi-hypothesis testing (MHT) framework, with which we design different type of model comparisons. We distinguish between test of model performance significance, of relative and absolute model performance and apply our framework to market risk forecasting model, to latent factor jump-diffusion models employed for the estimation of the statistical measure of an equity index, as well as to equity option pricing models. We develop original tests and, with regard to the proper exercise of model selection from an initial battery of models without any reference to a benchmark model, we combine the MHT approach with the model confidence set (MCS) to deliver a novel test of model comparison that is performed along with the established version of the MCS, as well as with an alternative simplified new MCS test that are detailed in the course of this work. We collect empirical evidence concerning model comparison in several subjects. With respect to market risk forecasting models, we have found that models capturing volatility clustering or targeting directly an auto-correlated conditional distribution percentile, perform better than the target model set and in particular, better than the historical simulation, widely employed by practitioners, and better than the so called RiskMetrics model. With respect to the equity index data dynamics, we have found that the popular affine jump-diffusion model requires a CEV augmentation to perform appropriately and that those models are slightly overperformed by an alternative stochastic volatility model, characterised by stochastic hazard with high frequency small jumps. The test performed over a large model set employed in the option pricing exercise points to a wide similarity of the results obtained by the many model specifications of the superior exponential volatility model, therefore suggesting a more careful adjustment of the model complexity. The model selection framework has proven very flexible in dealing with the varied collection of statistical problems. In particular, our main contribution represented by the generalised MHT based MCS test provides a method for model selection that is robust to finite sample distribution and that has the advantage of an adjustable tolerance for false rejections, allowing conservative to aggressive testing profiles

A novel approach to the bias-variance problem in bump hunting

This study explores various data-driven methods for performing background-model selection, and for assigning uncertainty on the signal-strength estimator that arises due to the choice of background model. The performance of these methods is evaluated in the context of several realistic example problems. Furthermore, a novel strategy is proposed that greatly simplifies the process of performing a bump hunt when little is assumed to be known about the background. This new approach is shown to greatly reduce the potential bias in the signal-strength estimator, without degrading the sensitivity by increasing the variance, and to produce confidence intervals with valid coverage properties.Comment: 26 pages, 17 figure

Multiple Chemodynamic Stellar Populations of the Ursa Minor Dwarf Spheroidal Galaxy

We present a Bayesian method to identify multiple (chemodynamic) stellar populations in dwarf spheroidal galaxies (dSphs) using velocity, metallicity, and positional stellar data without the assumption of spherical symmetry. We apply this method to a new Keck/DEIMOS spectroscopic survey of the Ursa Minor (UMi) dSph. We identify 892 likely members, making this the largest UMi sample with line-of-sight velocity and metallicity measurements. Our Bayesian method detects two distinct chemodynamic populations with high significance ($\ln{B}\sim33$). The metal-rich ($[{\rm Fe/H}]=-2.05\pm0.03$) population is kinematically colder (radial velocity dispersion of $\sigma_v=4.9\pm0.8 \, {\rm km \, s^{-1}}$) and more centrally concentrated than the metal-poor ($[{\rm Fe/H}]=-2.29\pm0.05$) and kinematically hotter population ($\sigma_v =11.5\pm0.9\, {\rm km \, s^{-1}}$). Furthermore, we apply the same analysis to an independent MMT/Hectochelle data set and confirm the existence of two chemodynamic populations in UMi. In both data sets, the metal-rich population is significantly flattened ($\epsilon=0.75\pm0.03$) and the metal-poor population is closer to spherical ($\epsilon=0.33_{-0.09}^{+0.12}$). Despite the presence of two populations, we are unable to robustly estimate the slope of the dynamical mass profile. We found hints for prolate rotation of order $\sim 2 \, {\rm km \, s^{-1}}$ in the MMT data set, but further observations are required to verify this. The flattened metal-rich population invalidates assumptions built into simple dynamical mass estimators, so we computed new astrophysical dark matter annihilation (J) and decay profiles based on the rounder, hotter metal-poor population and inferred $\log_{10}{(J(0.5^{\circ})/{\rm GeV^{2} \, cm^{-5}})}\approx19.1$ for the Keck data set. Our results paint a more complex picture of the evolution of Ursa Minor than previously discussed.Comment: 20 pages, 11 figures, data included. Comments welcome. Accepted to MNRA