Weighted Mixed Regression Estimation Under Biased Stochastic Restrictions

The paper considers the construction of estimators of regression coefficients in a linear regression model when some stochastic and biased apriori information is available. Such apriori information is framed as stochastic restrictions. The dominance conditions of the estimators are derived under the criterion of mean squared error matrix

Simultaneous Prediction of Actual and Average Values of Study Variable Using Stein-rule Estimators

The simultaneous prediction of average and actual values of study variable in a linear regression model is considered in this paper. Generally, either of the ordinary least squares estimator or Stein-rule estimators are employed for the construction of predictors for the simultaneous prediction. A linear combination of ordinary least squares and Stein-rule predictors are utilized in this paper to construct an improved predictors. Their efficiency properties are derived using the small disturbance asymptotic theory and dominance conditions for the superiority of predictors over each other are analyzed

Regularized Proportional Odds Models

The proportional odds model is commonly used in regression analysis to predict the outcome for an ordinal response variable. The maximum likelihood approach becomes unstable or even fails in small samples with relatively large number of predictors. The ML estimates also do not exist with complete separation in the data. An estimation method is developed to address these problems with MLE. The proposed method uses pseudo observations to regularize the observed responses by sharpening them so that they become close to the underlying probabilities. The estimates can be computed easily with all commonly used statistical packages supporting the fitting of proportional odds models with weights. Estimates are compared with MLE in a simulation study and two real life data sets

Stein-Rule Estimation under an Extended Balanced Loss Function

This paper extends the balanced loss function to a more general set up. The ordinary least squares and Stein-rule estimators are exposed to this general loss function with quadratic loss structure in a linear regression model. Their risks are derived when the disturbances in the linear regression model are not necessarily normally distributed. The dominance of ordinary least squares and Stein-rule estimators over each other and the effect of departure from normality assumption of disturbances on the risk property is studied

Mean Squared Error Matrix comparison of Least Squares and Stein-Rule Estimators for Regression Coefficients under Non-normal Disturbances

Choosing the performance criterion to be mean squared error matrix, we have compared the least squares and Stein-rule estimators for coefficients in a linear regression model when the disturbances are not necessarily normally distributed. It is shown that none of the two estimators dominates the other, except in the trivial case of merely one regression coefficient where least squares is found to be superior in comparisons to Stein-rule estimators

Journal Staff

Additive manufacturing is a growing manufacturing technology that has lately transcended from mostly being used in prototype manufacturing to be used in conventional production. The transition has occurred with, among other technologies, Fused Deposit Modelling (FDM), developed by Stratasys for plastic manufacturing. The interest in replacing conventional methods (which for plastic is injection moulding) is increasing in Sweden and at the same time the demand for a more sustainable production increases. We have in this thesis managed to adapt a general framework for sustainability analysis with 31 indicators to a framework, for the analysis of the transition from injection moulding to FDM, with only 8 relevant indicators. This process was conduced through first establishing what sustainability is and mapped both production processes. Afterwards was the differences in the production processes analysed in regards to the general framework for sustainability analysis and a new framework, was created. Finally we analysed how firm management views frameworks for sustainability analysis, how they use them and how our framework complements the wrongful use that sometimes occur today.Adderande tillverkning är en stadigt växande tillverkningsteknik som på senare tid övergått från att huvudsakligen användas till prototyptillverkning till att användas för traditionell produktion. Övergången har bland annat skett med metoden Fused Deposit Modelling (FDM) som utvecklades av Stratasys och producerar i plast. Intresset för att ersätta traditionella metoder (vilket för plast är, bland annat, formsprutning) ökar i Sverige samtidigt som efterfrågan för en hållbar produktion ökar. Vi har i rapporten lyckats anpassa ett generellt ramverk för hållbarhetsanalys med 31 indikatorer till ett ramverk, för att analysera hållbarheten vid en övergång från formsprutning till FDM, med enbart 8 relevanta indikatorer. Denna process genomfördes genom att först etablera vad hållbarhet är och kartlagt de båda tillverkningsprocesserna. Därefter analyserades skillnaderna hos tillverkningssprocesserna utifrån de generella indikatorer för hållbarhetanalys och ett nytt ramverk skapades. Slutligen analyserades hur företagsledningar ser på ramverk för hållbarhetsanalys, hur de använder ramverken och hur vårt ramverk kompletterar det felaktiga användandet som ofta existerar idag

Stimulated emission and excited-state absorption at room temperature on the 550 nm-laser transition in Er3+ doped YAlO3

A pump- and probe-beam technique is used for measuring time-resolved and cw-pumped excited-state absorption (ESA) and stimulated-emission (SE) spectra of Er3+:YAlO3 with high resolution. In combination with absorption and fluorescence spectra, detailed information on the wavelengths and cross-sections of ESA and SE at the 550 nm laser transition is provided

A review of agreement measure as a subset of association measure between raters

Agreement can be regarded as a special case of association and not the other way round. Virtually in all life or social science researches, subjects are being classified into categories by raters, interviewers or observers and both association and agreement measures can be obtained from the results of this researchers. The distinction between association and agreement for a given data is that, for two responses to be perfectly associated we require that we can predict the category of one response from the category of the other response, while for two response to agree, they must fall into the identical category. Which hence mean, once there is agreement between the two responses, association has already exist, however, strong association may exist between the two responses without any strong agreement. Many approaches have been proposed by various authors for measuring each of these measures. In this work, we present some up till date development on these measures statistics

Microdata Imputations and Macrodata Implications: Evidence from the Ifo Business Survey

A widespread method for now- and forecasting economic macro level parameters such as GDP growth rates are survey-based indicators which contain early information in contrast to official data. But surveys are commonly affected by nonresponding units which can produce biases if these missing values can not be regarded as missing at random. As many papers examined the effect of nonresponse in individual or household surveys, only less is known in the case of business surveys. So, literature leaves a gap on this issue. For this reason, we analyse and impute the missing observations in the Ifo Business Survey, a large business survey in Germany. The most prominent result of this survey is the Ifo Business Climate Index, a leading indicator for the German business cycle. To reflect the underlying latent data generating process, we compare different imputation approaches for longitudinal data. After this, the microdata are aggregated and the results are compared with the original indicators to evaluate their implications on the macro level. Finally, we show that the bias is minimal and ignorable