THIS ARTICLE PRESENTS A NON-STOCHASTIC VERSION OF THE GENERALIZED RIDGE REGRESSION ESTIMATOR THAT ARISES FROM A DISCUSSION OF THE PROPERTIES OF A GENERALIZED RIDGE REGRESSION ESTIMATOR WHOSE SHRINKAGE PARAMETERS ARE FOUND TO BE CLOSE TO THEIR UPPER BOUNDS. THE RESULTING ESTIMATOR TAKES THE FORM OF A SHRINKAGE ESTIMATOR THAT IS SUPERIOR TO BOTH THE ORDINARY LEAST SQUARES ESTIMATOR AND THE JAMES-STEIN ESTIMATOR UNDER CERTAIN CONDITIONS. A NUMERICAL STUDY IS PROVIDED TO INVESTIGATE THE RANGE OF SIGNAL TO NOISE RATIO UNDER WHICH THE NEW ESTIMATOR DOMINATES THE JAMES-STEIN ESTIMATOR WITH RESPECT TO THE PREDICTION MEAN SQUARE ERROR.

IN THIS ARTICLE WE INTRODUCE AN EXTENSION OF THE AKASH DISTRIBUTION. WE USE THE SLASH METHODOLOGY TO MAKE THE KURTOSIS OF THE AKASH DISTRIBUTION MORE FLEXIBLE. WE STUDY THE GENERAL PROBABILITY DENSITY FUNCTION OF THIS NEW MODEL, SOME PROPERTIES, MOMENTS, SKEWNESS AND KURTOSIS COEFFICIENTS. STATISTICAL INFERENCE IS PERFORMED USING THE METHODS OF MOMENTS AND MAXIMUM LIKELIHOOD VIA THE EM ALGORITHM. A SIMULATION STUDY IS CARRIED OUT TO OBSERVE THE BEHAVIOR OF THE MAXIMUM LIKELIHOOD ESTIMATOR. AN APPLICATION TO A REAL DATA SET WITH HIGH KURTOSIS IS CONSIDERED, WHERE IT IS SHOWN THAT THE NEW DISTRIBUTION FITS BETTER THAN OTHER EXTENSIONS OF THE AKASH DISTRIBUTION.

RIDGE REGRESSION TECHNIQUES HAVE BEEN FOUND USEFUL TO REDUCE MEAN SQUARE ERRORS OF PARAMETER ESTIMATES WHEN MULTICOLLINEARITY IS PRESENT. BUT THE USEFULNESS OF THE METHOD REST NOT ONLY UPON ITS ABILITY TO PRODUCE GOOD PARAMETER ESTIMATES, WITH SMALLER MEAN SQUARED ERROR THAN ORDINARY LEAST SQUARES, BUT ALSO ON HAVING REASONABLE INFERENTIAL PROCEDURES. THE AIM OF THIS PAPER IS TO DEVELOP ASYMPTOTIC CONFIDENCE INTERVALS FOR THE MODEL PARAMETERS BASED ON RIDGE REGRESSION ESTIMATES AND THE EDGEWORTH EXPANSION. SOME SIMULATION EXPERIMENTS ARE CARRIED OUT TO COMPARE THESE CONFIDENCE INTERVALS WITH THOSE OBTAINED FROM THE APPLICATION OF ORDINARY LEAST SQUARES. ALSO, AN EXAMPLE WILL BE PROVIDED BASED ON THE WELL KNOWN DATA SET OF HALD.

A COMMON PROBLEM IN THE PRACTICE OF REGRESSION ANALYSIS IS MULTICOLLINEARITY. ITS NEGATIVE EFFECTS ON THE LEAST SQUARES ESTIMATOR ARE WELL KNOWN. A NUMBER OF SHRINKAGE ESTIMATORS HAVE BEEN DEVELOPED TO CORRECT THIS PROBLEM, PROMINENT AMONG THEM IS RIDGE REGRESSION. WE DISCUSS THE BAYESIAN INTERPRETATION OF THE ESTIMATOR AND IN PARTICULAR WE PROPOSE A BAYESIAN STRATEGY TO SELECT THE SHRINKAGE PARAMETER. WE PROVIDE AN EXAMPLE OF THE RESULTING RIDGE REGRESSION ESTIMATOR. ALSO, TO COMPARE THE ESTIMATION AND PREDICTION PROPERTIES OF THE BAYESIAN SHRINKAGE RIDGE REGRESSION WITH OTHER WELL KNOWN RIDGE REGRESSION AND ORDINARY LEAST SQUARES ESTIMATORS, WE PERFORM A SIMULATION STUDY.

SE CONSTRUY UN INDICADOR LDER MENSUAL PARA EL NDICE DE ACTIVIDAD ECONMICA REGIONAL DE LA REGIN DE LA ARAUCANA CON SERIES DE DATOS PROPORCIONADOS POR EL INSTITUTO NACIONAL DE ESTADSTICAS REGIONAL QUE ABARCAN UN PERIODO DE 15 AOS. EL INDICADOR SE CORRELACION EN UN 0.96 Y PRESENT UN ERROR DE PRONSTICO PROMEDIO DE 0.28.ABSTRACTAN ESTIMATE OF INACER LEAD INDICATORS AND THEIR APPLICATION IN THE ARAUCANIA REGION THE RESEARCH ON WHICH THIS ARTICLE IS BASED AIMED AT CREATING AN ALTERNATIVE METHOD FOR THE CONSTRUCTION OF REGIONAL LEAD INDICATORS WHICH ANTICIPATE THE EVOLUTION OF THE REGIONAL ECONOMIC ACTIVITY INDEX IN A TRIMESTER. A MONTHY LEAD INDICATOR WAS CREATED FOR ARAUCANAS REGIONAL ECONOMIC ACTIVITY INDEX WITH THE DATA FOR A PERIOD OF FIFTEEN YERS PROVIDED BY BY THE REGIONAL OFFICE OF THE NATIONAL STATISTICS INSTITUTE. THE CORRELATION OF THE INDICATOR IS 0.96 AND PRESENTS AN AVERAGE PREVISION ERROR OF 0.28.

A SYLLABUS IS A SET OF COURSES, THEIR PREREQUISITES AND A SET OF RULES DEFINING THE CONTINUANCE OF A STUDENT IN AN ACADEMIC PROGRAM. THE MANUAL VERIFICATION OF THE POSSIBLE CONTRADICTIONS BETWEEN PREREQUISITES AND THE SET OF RULES IS A DIFFICULT TASK DUE TO THE LARGE NUMBER OF POSSIBLE CASES. IN THIS ARTICLE WE PRESENT THE DIFFERENT STAGES IN THE VERIFICATION OF A SYLLABUS, ITS MODELLING AND ANALYSIS USING PETRI NETS, AND SUGGESTED WAYS IN WHICH THIS MAY BE USED BY THE UNIVERSITY ADMINISTRATION IN THE DECISION MAKING PROCESS

IN THIS WORK A RANKING OF CHILEAN UNIVERSITIES ON THE BASIS OF PUBLICLY AVAILABLE INFORMATION IS DEVELOPED. THIS RANKING TAKES INTO ACCOUNT THE MULTIVARIATE CHARACTER OF THESE INSTITUTIONS. ALSO, IT IS NOTED THAT THE RESULTS ARE CONSISTENT WITH THOSE OF A WELL-KNOWN INTERNATIONAL RANKING THAT USES A DIFFERENT SET OF DATA, AS WELL AS WITH SEVERAL MULTIVARIATE ANALYSES OF THE DATA CONSIDERED IN THIS STUDY.

THIS ARTICLE CONSIDERS BOTH PARTIAL LEAST SQUARES (PLS) AND RIDGE REGRESSION (RR) METHODS TO COMBAT MULTICOLLINEARITY PROBLEM. A SIMULATION STUDY HAS BEEN CONDUCTED TO COMPARE THEIR PERFORMANCES WITH RESPECT TO ORDINARY LEAST SQUARES (OLS). WITH VARYING DEGREES OF MULTICOLLINEARITY, IT IS FOUND THAT BOTH, PLS AND RR, ESTIMATORS PRODUCE SIGNIFICANT REDUCTIONS IN THE MEAN SQUARE ERROR (MSE) AND PREDICTION MEAN SQUARE ERROR (PMSE) OVER OLS. HOWEVER, FROM THE SIMULATION STUDY IT IS EVIDENT THAT THE RR PERFORMS BETTER WHEN THE ERROR VARIANCE IS LARGE AND THE PLS ESTIMATOR ACHIEVES ITS BEST RESULTS WHEN THE MODEL INCLUDES MORE VARIABLES. HOWEVER, THE ADVANTAGE OF THE RIDGE REGRESSION METHOD OVER PLS IS THAT IT CAN PROVIDE THE 95% CONFIDENCE INTERVAL FOR THE REGRESSION COEFFICIENTS WHILE PLS CANNOT.

WE INTRODUCE A NEW APPROACH TO CLUSTERING CATEGORICAL DATA: CONDORCET CLUSTERING WITH A FIXED NUMBER OF GROUPS, DENOTED ?-CONDORCET. AS K-MODES, THIS APPROACH IS ESSENTIALLY BASED ON SIMILARITY AND DISSIMILARITY MEASURES. THE PAPER IS DIVIDED INTO THREE PARTS: FIRST, WE PROPOSE A NEW CONDORCET CRITERION, WITH A FIXED NUMBER OF GROUPS (TO SELECT CASES INTO CLUSTERS). IN THE SECOND PART, WE PROPOSE A HEURISTIC ALGORITHM TO CARRY OUT THE TASK. IN THE THIRD PART, WE COMPARE ?-CONDORCET CLUSTERING WITH K-MODES CLUSTERING. THE COMPARISON IS MADE WITH A QUALITY?S INDEX, ACCURACY OF A MEASUREMENT, AND A WITHIN-CLUSTER SUM-OF-SQUARES INDEX. OUR FINDINGS ARE ILLUSTRATED USING REAL DATASETS: THE FELINE DATASET AND THE US CENSUS 1990 DATASET.

WE INTRODUCE A NEW APPROACH TO CLUSTERING CATEGORICAL DATA: CONDORCET CLUSTERING WITH A FIXED NUMBER OF GROUPS, DENOTED A-CONDORCET. AS K-MODES, THIS APPROACH IS ESSENTIALLY BASED ON SIMILARITY AND DISSIMILARITY MEASURES. THE PAPER IS DIVIDED INTO THREE PARTS: FIRST, WE PROPOSE A NEW CONDORCET CRITERION, WITH A FIXED NUMBER OF GROUPS (TO SELECT CASES INTO CLUSTERS). IN THE SECOND PART, WE PROPOSE A HEURISTIC ALGORITHM TO CARRY OUT THE TASK. IN THE THIRD PART, WE COMPARE ?-CONDORCET CLUSTERING WITH K-MODES CLUSTERING. THE COMPARISON IS MADE WITH A QUALITY?S INDEX, ACCURACY OF A MEASUREMENT, AND A WITHIN-CLUSTER SUM-OF-SQUARES INDEX. OUR FINDINGS ARE ILLUSTRATED USING REAL DATASETS: THE FELINE DATASET AND THE US CENSUS 1990 DATASET.

THIS ARTICLE DEMONSTRATES THE MAIN RESULTS OBTAINED BY A STUDY CONDUCTED IN 2009 WITH 166 PROFESSIONAL SOCIAL WORKERS WHO DIRECTLY EXERCISE THE PROFESSION IN GRAN CONCEPCIÓN, CHILE. THE STUDY WAS STIMULATED BY THE ABSENCE OF EMPIRIC STUDIES ABOUT THE VALUATION OF THE GENERIC COMPETENCIES THESE PROFESSIONALS REQUIRE IN THE DIRECT EXERCISE OF THEIR PROFESSION. THE STUDY FOLLOWED A TRANSVERSAL-DESCRIPTIVE DESIGN OF A QUANTITATIVE NATURE. THE INSTRUMENT USED FOR DATA COLLECTION WAS A QUESTIONNAIRE, AND THE ANALYSES WERE BASED ON SINGLE, DOUBLE AND MULTIPLE VARIABLES. THEIR RELIABILITY WERE CALCULATED ACCORDING TO CRONBACH'S ALPHA AT 0.934. THE MAIN RESULTS SHOW A HIGH VALUATION OF THE GENERIC AND SYSTEMIC COMPETENCIES, FOLLOWED BY INTERPERSONAL AND INSTRUMENTAL ONES FOR THE EXERCISE OF THE PROFESSION.