diff --git a/src/Numerics/Fit.cs b/src/Numerics/Fit.cs
index b16026cd..14ddce5c 100644
--- a/src/Numerics/Fit.cs
+++ b/src/Numerics/Fit.cs
@@ -106,7 +106,7 @@ namespace MathNet.Numerics
         public static double[] PolynomialWeighted(double[] x, double[] y, double[] w, int order)
         {
             var design = Matrix<double>.Build.Dense(x.Length, order + 1, (i, j) => Math.Pow(x[i], j));
-            return WeightedRegression.Weighted(design, Vector<double>.Build.Dense(y), Matrix<double>.Build.DenseOfDiagonalArray(w)).ToArray();
+            return WeightedRegression.Weighted(design, Vector<double>.Build.Dense(y), Matrix<double>.Build.Diagonal(w.Length, w.Length, w)).ToArray();
         }
 
         /// <summary>
diff --git a/src/Numerics/LinearRegression/WeightedRegression.cs b/src/Numerics/LinearRegression/WeightedRegression.cs
index 3a6b80a9..693a9f99 100644
--- a/src/Numerics/LinearRegression/WeightedRegression.cs
+++ b/src/Numerics/LinearRegression/WeightedRegression.cs
@@ -65,7 +65,7 @@ namespace MathNet.Numerics.LinearRegression
                 predictor = predictor.InsertColumn(0, Vector<T>.Build.Dense(predictor.RowCount, Vector<T>.One));
             }
             var response = Vector<T>.Build.Dense(y);
-            var weights = Matrix<T>.Build.DenseOfDiagonalArray(w);
+            var weights = Matrix<T>.Build.Diagonal(w.Length, w.Length, w);
             return predictor.TransposeThisAndMultiply(weights*predictor).Cholesky().Solve(predictor.TransposeThisAndMultiply(weights*response)).ToArray();
         }