Cmax -Analysis Covariance Parameter Estimates Cov Parm Estimate SUBJECT(SEQUENCE) 0.7294 Residual 0.1584 Type 3 Tests of Fixed Effects Num Den Effect DF DF F Value Pr > F SEQUENCE 1 45 0.18 0.6730 PERIOD 1 45 0.11 0.7456 FORM 1 45 0.38 0.5390 Estimates Standard Label Estimate Error DF ABE for logCmax 0.05083 0.08211 45 t Value Pr > |t| 0.62 0.5390 Least Squares Means Standard Effect FORM Estimate Error FORM R 1.2619 0.1375 FORM T 1.3128 0.1375 Differences of Least Squares Means Effect FORM _FORM Estimate FORM R T -0.05083 DF t Value Pr > |t| Alpha 45 -0.62 0.5390 0.1 Standard Error 0.08211 Differences of Least Squares Means Effect FORM _FORM Lower Upper FORM R T -0.1887 0.08707 confidence intervals as vertical bars and the null hypothesis value of 1 and the acceptance limits as dotted horizontal lines. These plots have been drawn using a modified version of the Splus-code given in Millard and Krause (2001), Chapter 7. The interval for AUC is not contained within the limits of 0.8 to 1.25, but the one for Cmax is. Therefore, T cannot be considered bioequivalent to R as it fails to satisfy the criterion for AUC.

2003 ◽  
pp. 358-358
Keyword(s):  
Least Squares ◽  
Fixed Effects ◽  
Parameter Estimates ◽  
Estimate Error ◽  
Acceptance Limits ◽  
Vertical Bars ◽  
The One ◽  
Type 3 ◽  
Estimate Form ◽  
Subject Sequence

(Continued) Type 3 Tests of Fixed Effects Num Den Effect DF DF F Value Pr > F SEQUENCE 1 43 0.33 0.5672 PERIOD 1 43 0.29 0.5947 FORM 1 43 1.06 0.3080 Estimates Standard Label Estimate Error ABE for logAUC 0.09699 0.09401 DF t Value Pr > |t| 43 1.03 0.3080 Least Squares Means Standard Effect FORM Estimate Error FORM R 4.4143 0.1970 FORM T 4.5113 0.1970 Differences of Least Squares Means Effect FORM _FORM Estimate FORM R T -0.09699 DF t Value Pr > |t| Alpha 43 -1.03 0.3080 0.1 Standard Error 0.09401 Differences of Least Squares Means Effect FORM _FORM Lower Upper FORM R T -0.2550 0.06104 The 90% confidence interval for µ is (−0.0610,0.2550) for log(AUC) and (−0.0871,0.1887) for log(Cmax), where it will be noted that the signs have been reversed compared to those in the SAS output. Exponentiating the limits to obtain confidence limits for exp(µ ), gives (0.9408,1.2905) for AUC and (0.9166,1.2077) for Cmax. A graph-ical summary of these results is given in Figure 7.3. Here, to show the overall pattern and the essential details, we have plotted all the T/R ratios in the left plot and only those ratios which are less than 2.5 in the right plot. Also given in the plots are the

2003 ◽  
pp. 357-357
Keyword(s):  
Confidence Interval ◽  
Least Squares ◽  
Standard Error ◽  
Fixed Effects ◽  
Confidence Limits ◽  
Left Plot ◽  
Estimate Error ◽  
The Right ◽  
Type 3 ◽  
Estimate Form

The antecedents of corporate social and environmental responsibility discourse in Pakistan: Multiple theoretical perspectives

2019 ◽  
Author(s):  
Muhammad Farhan Basheer ◽  
Saqib Muneer ◽  
Muhammad Atif ◽  
Zubair Ahmad
Keyword(s):  
Institutional Theory ◽  
Least Squares ◽  
Agency Theory ◽  
Stakeholder Theory ◽  
Fixed Effects ◽  
Ordinary Least Squares ◽  
Environmental Responsibility ◽  
Legitimacy Theory ◽  
Discourse Practices ◽  
Corporate Social

The primary purpose of the study is to explore the antecedents of corporate social and environmental responsibilities discourse practices in Pakistan. The industry sensitivity, government shareholding, block holder ownership, print media coverage, environmental monitoring programs, and strategic posture are examined as antecedents of corporate social and environmental responsibility practices. A multidimensional theoretical perspective namely stakeholder theory (ST), institutional theory (IT), agency theory (PAT), and legitimacy theory (LT) is used to conceptualize the phenomena. All the four of perspective theories (positive accounting theory, legitimacy theory, stakeholder theory, and institutional theory) claim that there are ‘pressures’ that impact the organization. How much ‘pressures’ are recognized, managed or satisfied differs from one perspective of theory to the other. To estimate the data, this study uses three sets of panel data models, i.e., the pooled ordinary least squares model (POLS) or constant coefficients model, fixed effects (FEM or least squares dummy variable/LSDV model) and random-effects models. The final sample is comprising of 173 firms over eight years from 2011 to 2017. The firms listed in PSX are included in the sample. Overall the findings of the study have shown agreement with the proposed results. However, the study has provided more support to the institutional theory and stakeholder theory. Keywords: Corporate Social Responsibility, Stakeholders Theory, Agency Theory, Pakistan

Theoretical accuracy of the last squares method in the isotope dilution analysis

1984 ◽  
Vol 49 (4) ◽  
pp. 805-820
Author(s):  
Ján Klas
Keyword(s):  
Least Squares ◽  
Isotope Dilution ◽  
Least Squares Method ◽  
Isotope Dilution Analysis ◽  
Straight Line ◽  
The One ◽  
Two Parameter ◽  
Theoretical Accuracy

The accuracy of the least squares method in the isotope dilution analysis is studied using two models, viz a model of a two-parameter straight line and a model of a one-parameter straight line.The equations for the direct and the inverse isotope dilution methods are transformed into linear coordinates, and the intercept and slope of the two-parameter straight line and the slope of the one-parameter straight line are evaluated and treated.

scholarly journals Estimating Simultaneous Equation Models through an Entropy-Based Incremental Variational Bayes Learning Algorithm

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 384
Author(s):  
Rocío Hernández-Sanjaime ◽  
Martín González ◽  
Antonio Peñalver ◽  
Jose J. López-Espín
Keyword(s):  
Statistical Theory ◽  
Learning Algorithm ◽  
Real Life ◽  
Simulated Data ◽  
Simultaneous Equation ◽  
Variational Bayes ◽  
Parameter Estimates ◽  
Step Method ◽  
The One ◽  
Simultaneous Equation Models

The presence of unaccounted heterogeneity in simultaneous equation models (SEMs) is frequently problematic in many real-life applications. Under the usual assumption of homogeneity, the model can be seriously misspecified, and it can potentially induce an important bias in the parameter estimates. This paper focuses on SEMs in which data are heterogeneous and tend to form clustering structures in the endogenous-variable dataset. Because the identification of different clusters is not straightforward, a two-step strategy that first forms groups among the endogenous observations and then uses the standard simultaneous equation scheme is provided. Methodologically, the proposed approach is based on a variational Bayes learning algorithm and does not need to be executed for varying numbers of groups in order to identify the one that adequately fits the data. We describe the statistical theory, evaluate the performance of the suggested algorithm by using simulated data, and apply the two-step method to a macroeconomic problem.

scholarly journals Analysis of the New Kuznets Relationship: Considering Emissions of Carbon, Methanol, and Nitrous Oxide Greenhouse Gases—Evidence from EU Countries

2021 ◽  
Vol 18 (6) ◽  
pp. 2907
Author(s):  
Mara Madaleno ◽  
Victor Moutinho
Keyword(s):  
Nitrous Oxide ◽  
Least Squares ◽  
Fixed Effects ◽  
Ghg Emissions ◽  
Ordinary Least Squares ◽  
Kuznets Curve ◽  
Ekc Hypothesis ◽  
Long Run ◽  
Short Run ◽  
The Eu

Decreased greenhouse gas emissions (GHG) are urgently needed in view of global health threat represented by climate change. The goal of this paper is to test the validity of the Environmental Kuznets Curve (EKC) hypothesis, considering less common measures of environmental burden. For that, four different estimations are done, one considering total GHG emissions, and three more taking into account, individually, the three main GHG gases—carbon dioxide (CO2), nitrous oxide (N2O), and methane gas (CH4)—considering the oldest and most recent economies adhering to the EU27 (the EU 15 (Old Europe) and the EU 12 (New Europe)) separately. Using panel dynamic fixed effects (DFE), dynamic ordinary least squares (DOLS), and fully modified ordinary least squares (FMOLS) techniques, we validate the existence of a U-shaped relationship for all emission proxies considered, and groups of countries in the short-run. Some evidence of this effect also exists in the long-run. However, we were only able to validate the EKC hypothesis for the short-run in EU 12 under DOLS and the short and long-run using FMOLS. Confirmed is the fact that results are sensitive to models and measures adopted. Externalization of problems globally takes a longer period for national policies to correct, turning global measures harder and local environmental proxies more suitable to deeply explore the EKC hypothesis.

Generalised DOPs with Consideration of the Influence Function of Signal-in-Space Errors

2011 ◽  
Vol 64 (S1) ◽  
pp. S3-S18 ◽  
Author(s):  
Yuanxi Yang ◽  
Jinlong Li ◽  
Junyi Xu ◽  
Jing Tang
Keyword(s):  
Least Squares ◽  
Stochastic Models ◽  
Influence Function ◽  
A Priori ◽  
Functional Model ◽  
Global Navigation Satellite Systems ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Integrated Navigation ◽  
Satellite Systems

Integrated navigation using multiple Global Navigation Satellite Systems (GNSS) is beneficial to increase the number of observable satellites, alleviate the effects of systematic errors and improve the accuracy of positioning, navigation and timing (PNT). When multiple constellations and multiple frequency measurements are employed, the functional and stochastic models as well as the estimation principle for PNT may be different. Therefore, the commonly used definition of “dilution of precision (DOP)” based on the least squares (LS) estimation and unified functional and stochastic models will be not applicable anymore. In this paper, three types of generalised DOPs are defined. The first type of generalised DOP is based on the error influence function (IF) of pseudo-ranges that reflects the geometry strength of the measurements, error magnitude and the estimation risk criteria. When the least squares estimation is used, the first type of generalised DOP is identical to the one commonly used. In order to define the first type of generalised DOP, an IF of signal–in-space (SIS) errors on the parameter estimates of PNT is derived. The second type of generalised DOP is defined based on the functional model with additional systematic parameters induced by the compatibility and interoperability problems among different GNSS systems. The third type of generalised DOP is defined based on Bayesian estimation in which the a priori information of the model parameters is taken into account. This is suitable for evaluating the precision of kinematic positioning or navigation. Different types of generalised DOPs are suitable for different PNT scenarios and an example for the calculation of these DOPs for multi-GNSS systems including GPS, GLONASS, Compass and Galileo is given. New observation equations of Compass and GLONASS that may contain additional parameters for interoperability are specifically investigated. It shows that if the interoperability of multi-GNSS is not fulfilled, the increased number of satellites will not significantly reduce the generalised DOP value. Furthermore, the outlying measurements will not change the original DOP, but will change the first type of generalised DOP which includes a robust error IF. A priori information of the model parameters will also reduce the DOP.

The Four-States Model of Memory Retrieval Experiences

2007 ◽  
Vol 215 (1) ◽  
pp. 61-71 ◽  
Author(s):  
Edgar Erdfelder ◽  
Lutz Cüpper ◽  
Tina-Sarah Auer ◽  
Monika Undorf
Keyword(s):  
Signal Detection ◽  
Memory Retrieval ◽  
Measurement Model ◽  
Standard Errors ◽  
Parameter Estimates ◽  
Unequal Variances ◽  
One Dimensional ◽  
Detection Model ◽  
Memory States ◽  
The One

Abstract. A memory measurement model is presented that accounts for judgments of remembering, knowing, and guessing in old-new recognition tasks by assuming four disjoint latent memory states: recollection, familiarity, uncertainty, and rejection. This four-states model can be applied to both Tulving's (1985) remember-know procedure (RK version) and Gardiner and coworker's ( Gardiner, Java, & Richardson-Klavehn, 1996 ; Gardiner, Richardson-Klavehn, & Ramponi, 1997 ) remember-know-guess procedure (RKG version). It is shown that the RK version of the model fits remember-know data approximately as well as the one-dimensional signal detection model does. In contrast, the RKG version of the four-states model outperforms the corresponding detection model even if unequal variances for old and new items are allowed for.We show empirically that the two versions of the four-statesmodelmeasure the same state probabilities. However, the RKG version, requiring remember-know-guess judgments, provides parameter estimates with smaller standard errors and is therefore recommended for routine use.

scholarly journals Local labor market size and qualification mismatch

2018 ◽  
Vol 19 (6) ◽  
pp. 1261-1286
Author(s):  
Francesco Berlingieri
Keyword(s):  
Labor Market ◽  
Fixed Effects ◽  
Agglomeration Economies ◽  
Market Size ◽  
Local Labor Market ◽  
Skill Mismatch ◽  
Large Cities ◽  
Urban Wage Premium ◽  
Urbanized Areas ◽  
The One

Abstract This paper investigates the effect of the size of the local labor market on skill mismatch. Using survey data for Germany, I find that workers in large cities are both less likely to be overqualified for their job and to work in a different field than the one for which they trained. Different empirical strategies are employed to account for the potential sorting of talented workers into more urbanized areas. Results on individuals who have never moved away from the place in which they grew up and fixed effects estimates obtaining identification through regional migrants suggest that sorting does not fully explain the existing differences in qualification mismatch across areas. This provides evidence of the existence of agglomeration economies through better matches. However, lower qualification mismatch in larger cities is found to explain at best a small part of the urban wage premium.

scholarly journals Pengaruh Sektor Tersier terhadap Pertumbuhan Ekonomi Kawasan Barat dan Timur Indonesia

2021 ◽  
Vol 2021 (1) ◽  
pp. 782-791
Author(s):  
Pramudya Kusuma ◽  
Aisyah Fitri Yuniasih
Keyword(s):  
Least Squares ◽  
Fixed Effects ◽  
Generalized Least Squares ◽  
Fixed Effects Model ◽  
Panel Fixed Effects

Sektor tersier merupakan sektor lapangan usaha yang menghasilkan produk berupa jasa. Sektor tersier sendiri telah mendominasi perekonomian di Indonesia. Perubahan struktural ekonomi menuju sektor tersier diperkirakan mampu memengaruhi pertumbuhan ekonomi sehingga dilakukan penelitian untuk menganalisis pengaruh sektor tersier terhadap pertumbuhan ekonomi provinsi-provinsi di Indonesia. Analisis yang dilakukan membagi provinsi-provinsi di Indonesia menjadi Kawasan Barat Indonesia (KBI) dan Kawasan Timur Indonesia (KTI). Metode yang digunakan adalah analisis data panel Fixed Effects Model dengan estimasi Feasible Generalized Least Squares. Hasil penelitian menunjukkan bahwa pada KBI maupun KTI, sektor tersier yang dijelaskan oleh produktivitas tenaga kerja dan share tenaga kerja memiliki pengaruh positif terhadap pertumbuhan ekonomi. Variabel lain yang digunakan yaitu belanja langsung pemerintah memiliki pengaruh positif terhadap pertumbuhan ekonomi serta laju pertumbuhan penduduk memiliki pengaruh negatif terhadap pertumbuhan ekonomi. Peningkatan produktivitas sektor tersier dan tenaga kerja sektor tersier dapat dilakukan sebagai upaya peningkatan pertumbuhan ekonomi.

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